From 89f42bb55168c736787af8df6588e92caec7b194 Mon Sep 17 00:00:00 2001 From: Marcus Date: Thu, 16 Jan 2025 12:16:47 +0000 Subject: [PATCH] Revisions to paper text --- paper/paper.bib | 10 +- paper/paper.md | 2 +- paper/sections/discussion.md | 6 +- paper/sections/intro.md | 8 +- paper/sections/meta_science.md | 9 +- paper/sections/science.md | 2 +- ...rojectTemplate_SoundLocalization_SNN.ipynb | 1716 ++++++ ...ting_notebook_annotated_for_teaching.ipynb | 4720 ++++++++++++++++- 8 files changed, 6454 insertions(+), 19 deletions(-) create mode 100644 teaching/ProjectTemplate_SoundLocalization_SNN.ipynb diff --git a/paper/paper.bib b/paper/paper.bib index e14471f..e6ea930 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -345,7 +345,7 @@ @misc{beniaguev_dendro_plexing_2024 @inproceedings{ hassani2023dilated, title={Dilated convolution with learnable spacings}, -author={Ismail Khalfaoui Hassani and Thomas Pellegrini and Timoth{\'e}e Masquelier}, +author={Ismail Khalfaoui-Hassani and Thomas Pellegrini and Timoth{\'e}e Masquelier}, booktitle={The Eleventh International Conference on Learning Representations }, year={2023}, url={https://openreview.net/forum?id=Q3-1vRh3HOA} @@ -530,3 +530,11 @@ @misc{kingma2017adammethodstochasticoptimization primaryClass={cs.LG}, url={https://arxiv.org/abs/1412.6980}, } + +@inproceedings{PeiYe2021, + title={Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity}, + author={Felix Pei and Joel Ye and David M. Zoltowski and Anqi Wu and Raeed H. Chowdhury and Hansem Sohn and Joseph E. O’Doherty and Krishna V. Shenoy and Matthew T. Kaufman and Mark Churchland and Mehrdad Jazayeri and Lee E. Miller and Jonathan Pillow and Il Memming Park and Eva L. Dyer and Chethan Pandarinath}, + booktitle={Advances in Neural Information Processing Systems (NeurIPS), Track on Datasets and Benchmarks}, + year={2021}, + url={https://arxiv.org/abs/2109.04463} +} \ No newline at end of file diff --git a/paper/paper.md b/paper/paper.md index d393482..0e755bb 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -113,7 +113,7 @@ downloads: +++ {"part": "abstract"} -Neuroscientists are increasingly initiating large-scale collaborations which bring together tens to hundreds of researchers. However, while these projects represent a step-change in scale, they retain a traditional structure with centralised funding, participating laboratories and data sharing on publication. Inspired by an open-source project in pure mathematics, we set out to test the feasibility of an alternative structure by running a grassroots, massively collaborative project in computational neuroscience. To do so, we launched a public Git repository, with code for training spiking neural networks to solve a sound localisation task via surrogate gradient descent. We then invited anyone, anywhere to use this code as a springboard for exploring questions of interest to them, and encouraged participants to share their work both asynchronously through Git and synchronously at monthly online workshops. At a scientific level, our work investigated how a range of biologically-relevant parameters, from time delays to membrane time constants and levels of inhibition, could impact sound localisation in networks of spiking units. At a more macro-level, our project brought together 31 researchers from multiple countries, provided hands-on research experience to early career participants, and opportunities for supervision and teaching to later career participants. Looking ahead, our project provides a glimpse of what open, collaborative science could look like and provides a necessary, tentative step towards it. +Neuroscientists are increasingly initiating large-scale collaborations which bring together tens to hundreds of researchers. However, while these projects represent a step-change in scale, their workflow resembles many scientific endeavours. That is, there are participating laboratories who collaborate together and then make their data, methods and results available. Inspired by open-source projects in pure mathematics, we set out to test the feasibility of an alternative structure by running a grassroots, massively collaborative project in computational neuroscience. To do so, we launched a public Git repository, with code for training spiking neural networks to solve a sound localisation task via surrogate gradient descent. We then invited anyone, anywhere to use this code as a springboard for exploring questions of interest to them, and encouraged participants to share their work both asynchronously through Git and synchronously at monthly online workshops. At a scientific level, our work investigated how a range of biologically-relevant parameters, from time delays to membrane time constants and levels of inhibition, could impact sound localisation in networks of spiking units. At a more macro-level, our project brought together 31 researchers from multiple countries, provided hands-on research experience to early career participants, and opportunities for supervision and teaching to later career participants. Looking ahead, our project provides a glimpse of what open, collaborative science could look like and provides a necessary, tentative step towards it. +++ # Introduction diff --git a/paper/sections/discussion.md b/paper/sections/discussion.md index 2afea41..c7db135 100644 --- a/paper/sections/discussion.md +++ b/paper/sections/discussion.md @@ -1,6 +1,6 @@ ## What went well -The decision to start from the code base of the [Cosyne tutorial](https://neural-reckoning.github.io/cosyne-tutorial-2022/) {cite:p}`10.5281/zenodo.7044500` was very helpful. It meant that users had a clear entry path for the project without needing prior expertise, and a code base that was designed to be easy to understand. In addition, the popularity of the tutorial (over 30k views on YouTube at the time of writing) meant that many people heard about this project and were interested in participating. In addition, the GitHub-based infrastructure allowed for asynchronous work and a website that was automatically updated each time anyone made a change to their code or to the text of the paper. +The decision to start from the code base of the [Cosyne tutorial](https://neural-reckoning.github.io/cosyne-tutorial-2022/) {cite:p}`10.5281/zenodo.7044500` was very helpful. It meant that users had a clear entry path for the project without needing prior expertise, and a code base that was designed to be easy to understand. In addition, the popularity of the tutorial (over 30k views on YouTube at the time of writing) meant that many people heard about this project and were interested in participating. In addition, the GitHub-based infrastructure allowed for asynchronous work our website, that was automatically updated each time anyone made a change to their code or to the text of the paper, allowed for easy sharing of results. By providing models which used spiking neurons to transform sensory inputs into behavioural outputs, participants were free to explore in virtually any direction they wished, much like an open-world or sandbox video game. Indeed over the course of the project we explored the full sensory-motor transformation from manipulating the nature of the input signals to perturbing unit activity and assessing network behaviour. Consequently, our code forms an excellent basis for teaching, as concepts from across neuroscience can be introduced and then implemented in class. In this direction, we integrated our project into two university courses and provide slides and a highly annotated python notebook, for those interested in teaching with these models. @@ -13,8 +13,8 @@ Our second challenge, was the project's exploratory nature. While this appealed A third challenge, which arose towards the end of the project, was how to fairly assign credit. We had initially - and perhaps somewhat idealistically - stated that anyone who contributed to the project, either by writing code or participating in one of the workshops, would be included on the author list. To the extent that it was possible, we have followed through with this, though we were simply unable to contact several of the participants and so could not include them as authors. Another issue with this system is that participants with unequal contributions, e.g. attending a workshop vs contributing an entire section of the paper, would be assigned similar credit, i.e. authorship. To resolve this, we experimented with using the number or size of GitHub commits to order authors, however we found that these metrics did not accurately reflect contributions. For example, it may be quicker to commit a large-amount of low quality text than a concise well written section, and similarly there is no good reason to distinguish between two authors who submit the same amount of work through a different number of commits. We attempted to address this challenge by providing a contributions table ([](#contributors)) and agreeing an author order. This order was agreed on unanimously, though could easily cause issues in other projects. Consequently, we recommend that a strategy for credit assignment be determined collaboratively at the start of the project, and made explicit so that participants can clearly understand how their contribution will translate to credit. Alternatively, such projects could publish under a pseudonym, e.g. COMOB. -Ultimately, while the project explored many interesting directions, which will form the basis for future work, we did not reach a point where we could draw strong scientific conclusions about sound localization. From group discussions we concluded that this is likely due to the free-form nature of our project, which would have benefited from a more coordinated approach. The question is, how to do this without compromising the ideals of a grass-roots project? Extending the voting idea above, one approach would be to make the proposer of the, democratically selected, project responsible for making sure that results are comparable and generally keeping the project on the right track. A role similar to a traditional supervisor, but with the critical difference that they are elected by their peers and only on a project by project basis. +Ultimately, while the project explored many interesting directions, which will form the basis for future work, we did not reach a point where we could draw strong scientific conclusions about sound localization. From group discussions we concluded that this is likely due to the free-form nature of our project, which would have benefited from a more coordinated approach. The question is, how to do this without compromising the ideals of a grass-roots project? Extending the voting idea above, one approach would be to make the proposer of the democratically selected project responsible for making sure that results are comparable and generally keeping the project on the right track. A role similar to a traditional supervisor, but with the critical difference that they are elected by their peers and only on a project by project basis. ## Conclusions -This paper does not present a scientific breakthrough. However, it does demonstrate the feasibility of open research projects which bring together large number of participants across countries and career stages to work together collaboratively on scientific projects. Moreover, several follow-up research projects are planned based on pilot data from our work and, building on our experience, we plan to launch a second COMOB project soon. \ No newline at end of file +This paper does not present a scientific breakthrough. However, it does demonstrate the feasibility of open research projects which bring together large number of participants across countries and career stages to work together collaboratively on scientific projects. Looking ahead, we hope that the diversity of expertise and perspectives, such projects support, will allow for discoveries beyond what any single group could realise. \ No newline at end of file diff --git a/paper/sections/intro.md b/paper/sections/intro.md index 80fd13b..21a2261 100644 --- a/paper/sections/intro.md +++ b/paper/sections/intro.md @@ -1,7 +1,7 @@ -Inspired by the success of endeavours like the [Human Genome Project](https://www.genome.gov/human-genome-project) and [CERN](https://home.cern/), neuroscientists are increasingly initiating large-scale collaborations. Though, how to best structure these projects remains an open-question {cite:p}`doi.org/10.1038/539159a`. The largest efforts, e.g. the [International Brain Laboratory](https://www.internationalbrainlab.com/) [@doi.org/10.1016/j.neuron.2017.12.013;@doi.org/10.1016/j.conb.2020.10.020], [The Blue Brain Project](https://www.epfl.ch/research/domains/bluebrain/) and [Human Brain Project](https://www.humanbrainproject.eu) bring together tens to hundreds of researchers across multiple laboratories. However, while these projects represent a step-change in scale, they retain a legacy structure which resembles a consortia grant. I.e. there are participating laboratories who collaborate together and then make their data, methods and results available upon publication. As such, interested participants face a high barrier to entry: joining a participating laboratory, initiating a collaboration with the project, or awaiting publications. So how could these projects be structured differently? +Inspired by the success of endeavours like the [Human Genome Project](https://www.genome.gov/human-genome-project) and [CERN](https://home.cern/), neuroscientists are increasingly initiating large-scale collaborations. The largest efforts, such as the [International Brain Laboratory](https://www.internationalbrainlab.com/) [@doi.org/10.1016/j.neuron.2017.12.013;@doi.org/10.1016/j.conb.2020.10.020], [The Blue Brain Project](https://www.epfl.ch/research/domains/bluebrain/) and [Human Brain Project](https://www.humanbrainproject.eu) bring together tens to hundreds of researchers across multiple laboratories. These projects have generated scientific insights, large-scale datasets, tools and educational materials. However, while they represent a step-change in scale, their workflow resembles many scientific endeavours. That is, there are participating laboratories who collaborate together and then make their data, methods and results available. As such, to participate, individuals must join a participating laboratory, initiate a collaboration with the project, or wait for the publication of data and resources. So how could these projects be structured differently{cite:p}`doi.org/10.1038/539159a`? -One alternative is a bench marking contest, in which participants compete to obtain the best score on a specific task. Such contests have driven progress in fields from machine learning {cite:p}`10.1109/CVPR.2009.5206848` to [protein folding](https://predictioncenter.org/), and have begun to enter neuroscience. For example, in [Brain-Score](https://www.brain-score.org/) [@10.1101/407007;@10.1016/j.neuron.2020.07.040] participants submit models, capable of completing a visual processing task, which are then ranked according to a quantitative metric. As participants can compete both remotely and independently, these contests offer a significantly lower barrier to entry. Though, they emphasise competition over collaboration, and critically they require a well defined, quantifiable endpoint. In [Brain-Score](https://www.brain-score.org/), this endpoint is a composite metric which describes the model's similarity to experimental data in terms of both behaviour and unit activity. However, this metric's relevance is debatable {cite:p}`doi:10.1017/S0140525X22002813` and more broadly, defining clear endpoints for neuroscientific questions remains challenging. +One alternative are bench marking contests, in which participants compete to obtain the best score on a specific task. Bench marking contests have driven progress in fields from computer vision {cite:p}`10.1109/CVPR.2009.5206848` to [protein folding](https://predictioncenter.org/), and have begun to enter neuroscience. For example, in [Brain-Score](https://www.brain-score.org/) [@10.1101/407007;@10.1016/j.neuron.2020.07.040] participants submit models, capable of completing a visual processing task, which are then ranked according to a quantitative metric. As participants can compete both remotely and independently, these contests offer a low barrier to entry. However, defining quantifiable endpoints for neuroscientific questions remains challenging {cite:p}`doi:10.1017/S0140525X22002813`. -Another alternative is massively collaborative projects in which participants work together to solve a common goal. For example, in the [Polymath Project](https://polymathprojects.org/) unsolved mathematical problems are posed, and then participants share comments, ideas and equations online as they collectively work towards solutions. Similarly, the [Busy Beaver Challenge](https://bbchallenge.org/) recently [announced](https://discuss.bbchallenge.org/t/july-2nd-2024-we-have-proved-bb-5-47-176-870/237) a formal proof of a conjecture that was open for decades, [based mainly on contributions from amateur mathematicians, organised purely online](https://www.quantamagazine.org/amateur-mathematicians-find-fifth-busy-beaver-turing-machine-20240702/). Inspired by this approach, we founded [COMOB (Collaborative Modelling of the Brain)](https://comob-project.github.io/) - an open-source movement, which aims to tackle neuroscientific questions. Here, we share our experiences and results from our first project, in which we explored spiking neural network models of sound localization. +Another alternative are massively collaborative projects, in which a problem is openly stated and contributions are welcomed from anyone willing to participate. For example, in the [Polymath Project](https://polymathprojects.org/) unsolved mathematical problems are posed, and then participants share comments, ideas and equations online as they collectively work towards solutions. Similarly, the [Busy Beaver Challenge](https://bbchallenge.org/) recently [announced](https://discuss.bbchallenge.org/t/july-2nd-2024-we-have-proved-bb-5-47-176-870/237) a formal proof of a conjecture that was open for decades, [based mainly on online contributions from amateur mathematicians](https://www.quantamagazine.org/amateur-mathematicians-find-fifth-busy-beaver-turing-machine-20240702/). Inspired by this approach, we founded [COMOB (Collaborative Modelling of the Brain)](https://comob-project.github.io/) - to explore if and how this collaborative model could be leveraged in neuroscience. -We start by detailing how we ran the project in terms of organisation and infrastructure in [](#metascience). We then briefly summarise our scientific results in [](#science). We conclude the main text with a [](#discussion) of what went well, what went wrong, and how we think future projects of this sort could learn from our experiences. Finally, in the [](#appendices) we provide longer, detailed write-ups of some of our scientific results. \ No newline at end of file + Here, we share our experiences and results. We start by detailing how we ran the project in terms of organisation and infrastructure in [](#metascience). We then briefly summarise our scientific results in [](#science). We conclude the main text with a [](#discussion) of what went well, what went wrong, and how future projects like this could learn from our experiences. Finally, in the [](#appendices) we provide detailed write-ups of our scientific results. \ No newline at end of file diff --git a/paper/sections/meta_science.md b/paper/sections/meta_science.md index c33e74c..8f2a7b0 100644 --- a/paper/sections/meta_science.md +++ b/paper/sections/meta_science.md @@ -27,11 +27,4 @@ For those interested in pursuing a similar project our repository can easily be (teaching-section)= ## Teaching with this framework -This project emerged from a tutorial, and the code remains well suited for teaching several concepts from across neuroscience. As such, we integrated our project into a Physics MSc course on Biophysics and Neural Circuits. Working individually or in pairs, students actively engaged by adjusting network parameters and modifying the provided code to test their own hypotheses. Later, brief progress report presentations stimulated dynamic discussions in class, as all students, while working on the same project and code, pursued different hypotheses. We found that this setup naturally piqued interest in their peers’ presentations, enhanced their understanding of various project applications, and facilitated collaborative learning. Moreover, it allowed for engagement from students at a range of skill levels, and helped bridge the gap between teaching and research. For those interested in teaching with this framework, introductory slides and a dedicated Python notebook are available on our [GitHub repository](https://github.com/comob-project/snn-sound-localization). - -% The project’s stochastic outcomes necessitated substantial statistical analysis, adding an experimental dimension that made the project outcome less deterministic and, thus, more engaging than standard step-wise exercises. However, the project does not demand complex programming nor deep mathematical understandings of neural networks, and so allows practical exploration of neural network applications appropriate for various student levels. This adaptability allowed students of varying skill levels to progress at their own pace. Moreover, the open-ended nature of the project allowed the use of generative AI tools, enabling students to overcome coding challenges and deepen their understanding of the provided code and underlying machine learning concepts, thereby enhancing their learning curve and engagement. - -% Working on a real research project not only sustained interest and demonstrated real-world impact but also provided additional inspiration through the accessible contributions of all project participants. This educational initiative thus successfully bridged the gap between teaching and research, with student feedback highlighting its effectiveness in enhancing both theoretical and practical knowledge. The desire for more time to delve deeper into the projects indicated its strength in engaging students and sparking their interest. - -% In sum, this framework's multidisciplinary nature makes it versatile in various teaching contexts, and suited to discussing both machine learning concepts and open challenges in neuroscience, such as how to decipher brain circuits with recording tools and experimental manipulations like optogenetics. -% For those interested in teaching with this framework, we have provided slides and a highly annotated introductory Python notebook [here](). \ No newline at end of file +This project emerged from a tutorial, and the code remains well suited for teaching several concepts from across neuroscience. As such, we integrated our project into a Physics MSc course on Biophysics and Neural Circuits. Working individually or in pairs, students actively engaged by adjusting network parameters and modifying the provided code to test their own hypotheses. Later, brief progress report presentations stimulated dynamic discussions in class, as all students, while working on the same project and code, pursued different hypotheses. We found that this setup naturally piqued interest in their peers’ presentations, enhanced their understanding of various project applications, and facilitated collaborative learning. Moreover, it allowed for engagement from students at a range of skill levels, and helped bridge the gap between teaching and research. For those interested in teaching with this framework, introductory slides and a dedicated Python notebook are available on our [GitHub repository](https://github.com/comob-project/snn-sound-localization). \ No newline at end of file diff --git a/paper/sections/science.md b/paper/sections/science.md index a50dd04..5d9f36c 100644 --- a/paper/sections/science.md +++ b/paper/sections/science.md @@ -6,7 +6,7 @@ Animals localise sounds by detecting location- or direction-specific cues in the The classic model of ITD sensitivity is the delay line model of {cite:t}`Jeffress1948` in which an array of binaural coincidence detector neurons receive inputs from the two ears with different delays. When a neurons' delays exactly match the acoustic delays induced by the sound location, it will be maximally active. Therefore, the identity of the most active neuron indicates the direction of the sound. This model is widely accepted, though was shown to be inefficient with respect to neural noise by {cite:t}`McAlpine2003`, who proposed an alternative model based on the two binaural hemispheres average firing rates - which is optimally robust to neural noise. However, {cite:t}`goodman_decoding_2013` showed that these models perform too poorly to account for behavioural data, especially in situations where sounds had complex and unknown spectral properties, or in the presence of background noise, and proposed an alternative based on a perceptron-like neural network - which is both robust to neural noise and performed well across a range of conditions. -Building on this literature, and our Cosyne tutorial, the starting point of this project was to ask: what solutions would you find if you directly optimised a spiking neural network to localise sounds? How would those solutions depend on the available neural mechanisms and statistics of the sound? Could we understand the solutions found in a simple way? What properties would the solution have in terms of robustness to noise, generalisation, and so forth? And could the solutions found by optimisation throw light on features found in the auditory systems of different animals? +Building on this literature, and our Cosyne tutorial, the starting point of our project was to ask: what solutions would you find if you directly optimised a spiking neural network to localise sounds? How would those solutions depend on the available neural mechanisms and statistics of the sound? Could we understand the solutions found? What properties would the solution have in terms of robustness to noise, generalisation, and so forth? And could the solutions found by optimisation throw light on features found in the auditory systems of different animals? ## A simple spiking neural network model diff --git a/teaching/ProjectTemplate_SoundLocalization_SNN.ipynb b/teaching/ProjectTemplate_SoundLocalization_SNN.ipynb new file mode 100644 index 0000000..01971d2 --- /dev/null +++ b/teaching/ProjectTemplate_SoundLocalization_SNN.ipynb @@ -0,0 +1,1716 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dfb23669", + "metadata": { + "id": "dfb23669" + }, + "source": [ + "# Sound localisation with surrogate gradient descent\n", + "\n", + "This is a concise version of the SNN implementation of the sound localization task. Detailed explanations from the tutorial are removed to make it an accessible code to develop you project." + ] + }, + { + "cell_type": "markdown", + "source": [ + "## To setup before you start\n", + "First, download a copy of this notebook to your personal google drive:\n", + "1. mount your google drive" + ], + "metadata": { + "id": "Dg9b4XdMDjPl" + }, + "id": "Dg9b4XdMDjPl" + }, + { + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9hL7mQSV_tCi", + "outputId": "db1f0d6a-ccca-487b-d262-1ca0503c47cb" + }, + "id": "9hL7mQSV_tCi", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "2. Save a copy of the notebook to your drive: \"Files\" => \"Save a copy in Drive\"\n", + "3. Locate where this copy was saved in your dirve: \"Files\" => \"Locate in Drive\"\n", + "4. Now you can rename the located file and move it to a location of your choice in your google drive\n" + ], + "metadata": { + "id": "mdi_e2fvE1FW" + }, + "id": "mdi_e2fvE1FW" + }, + { + "cell_type": "markdown", + "source": [ + "## Data Generation" + ], + "metadata": { + "id": "Zq-l_P7uFEY_" + }, + "id": "Zq-l_P7uFEY_" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee3c91b7", + "metadata": { + "id": "ee3c91b7" + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.gridspec import GridSpec\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "\n", + "dtype = torch.float\n", + "\n", + "# Check whether a GPU is available\n", + "if torch.cuda.is_available():\n", + " device = torch.device(\"cuda\")\n", + "else:\n", + " device = torch.device(\"cpu\")\n", + "\n", + "my_computer_is_slow = True # set this to True if using Colab\n", + "\n", + "import pdb\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Input signal parameter configuration\n", + "\n", + "Here's a picture of the architecture for the stimuli:\n", + "\n", + "\n", + "\"Stimuli" + ], + "metadata": { + "id": "EIBg54r7OySj" + }, + "id": "EIBg54r7OySj" + }, + { + "cell_type": "code", + "source": [ + "# We use the following constants to make equations look nicer below\n", + "second = 1\n", + "ms = 1e-3\n", + "Hz = 1\n", + "\n", + "# Stimulus and simulation parameters\n", + "dt = 1*ms # large time step to make simulations run faster for tutorial\n", + "anf_per_ear = 100 # number of auditory nerve fibers connected to each ear with independent noise\n", + "envelope_power = 2 # higher values make sharper envelopes. Easier by eye => But does the network perform better ?\n", + "rate_max = 600*Hz # maximum Poisson firing rate\n", + "f = 20*Hz # stimulus frequency\n", + "duration = .1*second # stimulus duration\n", + "duration_steps = int(np.round(duration/dt)) # number of simulation steps\n", + "input_size = 2*anf_per_ear" + ], + "metadata": { + "id": "9DL57fuXOxMP" + }, + "id": "9DL57fuXOxMP", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "id": "345a4686", + "metadata": { + "id": "345a4686" + }, + "source": [ + "### Input signal generation function\n" + ] + }, + { + "cell_type": "markdown", + "id": "bd312e52", + "metadata": { + "id": "bd312e52" + }, + "source": [ + "The functions below return two arrays ``ipd`` and ``spikes``.\n", + "\n", + "- ``ipd`` is an array of length ``num_samples`` that gives the true IPD,\n", + "- ``spikes`` is an array of 0 (no spike) and 1 (spike) of shape ``(num_samples, duration_steps, 2*anf_per_ear)``, where - ``duration_steps`` is the number of time steps there are in the stimulus." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5bb26693", + "metadata": { + "id": "5bb26693" + }, + "outputs": [], + "source": [ + "# Generate an input signal (spike array) from array of true IPDs\n", + "def input_signal(ipd, envelope_power=envelope_power):\n", + " \"\"\"\n", + " Generate a Poisson spike train based on an input Interaural Phase Difference (IPD) array\n", + " and the delays imposed by the individual auditory nerve fibers.\n", + "\n", + " Parameters\n", + " ----------\n", + " ipd : array-like\n", + " An array of true Interaural Phase Differences (IPD). Shape: (num_samples, )\n", + " envelope_power : float, optional\n", + " A parameter controlling the strength of the signal envelope, which modulates\n", + " the spike train generation. Default value is the globally defined `envelope_power`.\n", + "\n", + " Returns\n", + " -------\n", + " spikes : ndarray\n", + " A binary array indicating spike occurrences, shaped (num_samples, duration_steps, 2*anf_per_ear).\n", + " `spikes[i, j, k]` is 1 if a spike occurred at the jth time step for the ith IPD in the kth auditory nerve fiber,\n", + " and 0 otherwise.\n", + "\n", + " Notes\n", + " -----\n", + " - The function first calculates an array of phases (`phi`) to define the sinudoidal auditory stimulus and adds a random\n", + " phase offset because we want that the system learns to infer the angular location of the sound source indepent of its distance\n", + " to the source.\n", + " - An array of theta values is initialized that will hold the transformed phi values according to the phase delay imposed by the\n", + " individual auditory nerve fibers and the ipd between the two ears.\n", + " - Different phase delays, ranging from 0 to pi/2, are calculated and added with the ipd value to generate theta.\n", + " - Poisson spikes are generated based on the theta values and a sinusoidal modulation of the firing rate.\n", + " - The spikes are returned as a binary array, indicating the occurrence of spikes across auditory nerve fibers and time.\n", + " \"\"\"\n", + " num_samples = len(ipd) # corresponds to the number of different locations of the source in the data set\n", + "\n", + " T = np.arange(duration_steps)*dt # array of times over which the auditory signal is constructed\n", + " phi = 2*np.pi*(f*T) + 2*np.pi*np.random.rand() # array of phases corresponding to those times with random offset\n", + " # because we want that the system learns to infer the angular location of the sound source indepent of its distance\n", + " # to the source. The phase in this array increases linearly.\n", + "\n", + " phase_delays = np.linspace(0, np.pi/2, anf_per_ear) # array of phase delays introduced by the auditory nerve fibers.\n", + " # For each ear, we have anf_per_ear different phase delays from 0 to pi/2 so\n", + " # that the differences between the two ears can cover the full range from -pi/2 to pi/2\n", + "\n", + " theta = np.zeros((num_samples, duration_steps, 2*anf_per_ear)) # 3D array that holds the spike pattern of all auditory nerve fibers for all the interaural phase difference in the data set.\n", + " # num_samples = number of different IPD values in our data set\n", + " # duration_step = number of time points in our auditory signal\n", + " # 2*anf_per_ear = total number of auditory nerve fibers\n", + "\n", + " # Now we set up these theta values. Some numpy vectorisation logic using broadcasting to implements the idea in the text above.\n", + " theta[:, :, :anf_per_ear] = phi[np.newaxis, :, np.newaxis]+phase_delays[np.newaxis, np.newaxis, :]\n", + " theta[:, :, anf_per_ear:] = phi[np.newaxis, :, np.newaxis]+phase_delays[np.newaxis, np.newaxis, :]+ipd[:, np.newaxis, np.newaxis]\n", + "\n", + " # now generate Poisson spikes at the given firing rate as in the previous notebook\n", + " spikes = rate_max*dt*(0.5*(1+np.sin(theta)))**envelope_power > np.random.rand(num_samples, duration_steps, 2*anf_per_ear)\n", + " return spikes, theta\n", + "\n", + "# Generate some true IPDs from (-pi/2, pi/2) and corresponding spike arrays\n", + "def random_ipd_input_signal(num_samples, envelope_power=envelope_power, tensor=True):\n", + " \"\"\"\n", + " Generate random Interaural Phase Differences (IPDs) and then corresponding spike arrays using\n", + " the function input_signal(idp).\n", + "\n", + " The function generates `num_samples` IPDs, uniformly distributed in the range (-pi/2, pi/2).\n", + " It then generates corresponding spike arrays using the `input_signal` function.\n", + " Optionally, IPDs and spike arrays can be converted to PyTorch tensors.\n", + "\n", + " Parameters\n", + " ----------\n", + " num_samples : int\n", + " The number of IPD samples to generate.\n", + " envelope_power : float, optional\n", + " A parameter controlling the strength of the signal envelope, which modulates\n", + " the spike train generation. Default value is the globally defined `envelope_power`.\n", + "\n", + " tensor : bool, optional\n", + " If True, converts the IPDs and spike arrays to PyTorch tensors before returning them.\n", + " If False, they are returned as NumPy arrays. Default is True.\n", + "\n", + " Returns\n", + " -------\n", + " ipd : ndarray or Tensor\n", + " An array of randomly generated IPDs. Shape: (num_samples, ).\n", + " Returned as a PyTorch tensor if `tensor` is True, otherwise as a NumPy array.\n", + " spikes : ndarray or Tensor\n", + " A binary array indicating spike occurrences along time, generated by `input_signal` based on `ipd`.\n", + " Returned as a PyTorch tensor if `tensor` is True, otherwise as a NumPy array.\n", + " Shaped: (num_samples, duration_steps, 2*anf_per_ear)\n", + "\n", + " Notes\n", + " -----\n", + " - Ensure that the `input_signal` function is defined in your environment as it is called within this function.\n", + " - If `tensor` is True, ensure that PyTorch is installed and configured in your environment.\n", + "\n", + " Examples\n", + " --------\n", + " >>> ipd, spikes = random_ipd_input_signal(50, tensor=False)\n", + " >>> print(ipd.shape, spikes.shape)\n", + " (50,) (50, duration_steps, 2*anf_per_ear)\n", + " \"\"\"\n", + " ipd = np.random.rand(num_samples)*np.pi-np.pi/2 # uniformly random in (-pi/2, pi/2)\n", + " spikes, theta = input_signal(ipd)\n", + " if tensor:\n", + " ipd = torch.tensor(ipd, device=device, dtype=dtype)\n", + " spikes = torch.tensor(spikes, device=device, dtype=dtype)\n", + " return ipd, spikes, theta\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Test the input data generation fuction" + ], + "metadata": { + "id": "90RackY1Ljz_" + }, + "id": "90RackY1Ljz_" + }, + { + "cell_type": "code", + "source": [ + "# Plot for a few true IPDs the generated spike trains of the auditory nerve fibers to show how it looks.\n", + "# The first 100 lines are auditory nerve fiber responses of the righ ear and the others are from the left ear.\n", + "# You note that the IPDs was applied to the left ear's fibers.\n", + "ipd, spikes, _ = random_ipd_input_signal(8)\n", + "plt.figure(figsize=(10, 4), dpi=100)\n", + "for i in range(8):\n", + " plt.subplot(2, 4, i+1)\n", + " plt.imshow(spikes[i, :, :].T, aspect='auto', interpolation='nearest', cmap=plt.cm.gray_r)\n", + " plt.title(f'True IPD = {int(ipd[i]*180/np.pi)} deg')\n", + " if i>=4:\n", + " plt.xlabel('Time (steps)')\n", + " if i%4==0:\n", + " plt.ylabel('Input neuron index')\n", + "plt.tight_layout()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 341 + }, + "id": "Q2i6kOPZLiu3", + "outputId": "25a35939-2256-4986-df72-b0e9a318130d" + }, + "id": "Q2i6kOPZLiu3", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "id": "177a735f", + "metadata": { + "id": "177a735f" + }, + "source": [ + "### Classification helper functions\n", + "\n", + "The objective is to take the input spike data and infer the **Interaural Phase Difference (IPD)** using a neural network. To achieve this, we will:\n", + "\n", + "- **Discretize the IPD range** into categories (segments). \n", + "- **Train a neural network** to predict the category (segment) to which the input belongs.\n", + "\n", + " This classification approach simplifies the continuous IPD estimation problem by transforming it into a discrete class prediction task, making it computationally efficient and suitable for neural network-based learning.\n", + "\n", + "\n", + "\n", + "\n", + "#### We define two helper functions:\n", + "\n", + "Function 1: discretise(ipds)\n", + " - This function discretises the IPD range into $N_c$ classes.\n", + "\n", + "Function 2: continuise(ipd_indices)\n", + " - This function maps IPD indices back to continuous IPD values.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f817078", + "metadata": { + "id": "3f817078", + "outputId": "a91cfbc7-c14a-43ba-86f6-9f41ef30077a", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Number of classes = 12\n" + ] + } + ], + "source": [ + "# classes at 15 degree increments\n", + "num_classes = 180//15\n", + "print(f'Number of classes = {num_classes}')\n", + "\n", + "def discretise(ipds):\n", + " \"\"\"\n", + " Discretize Interaural Phase Differences (IPDs) to generate class labels.\n", + "\n", + " The function maps IPDs, which are continuous values in the range (-pi/2, pi/2),\n", + " to discrete classes in the range [0, num_classes-1]. The resulting discrete values\n", + " are suitable for classification tasks.\n", + "\n", + " Parameters\n", + " ----------\n", + " ipds : Tensor\n", + " A tensor containing continuous IPD values. The values should be in the range (-pi/2, pi/2).\n", + "\n", + " Returns\n", + " -------\n", + " Tensor\n", + " A tensor containing the classification of IPD values, in the range [0, num_classes-1].\n", + "\n", + " Notes\n", + " -----\n", + " - Assumes the input `ipds` is a PyTorch tensor.\n", + " - `num_classes` should be defined in the surrounding scope.\n", + " - The output tensor will have the same shape as the input `ipds`.\n", + "\n", + " Examples\n", + " --------\n", + " >>> ipds = torch.tensor([-np.pi/2, 0, np.pi/2])\n", + " >>> ipd_indices = discretise(ipds)\n", + " \"\"\"\n", + " return ((ipds+np.pi/2)*num_classes/np.pi).long() # assumes input is tensor\n", + "\n", + "def continuise(ipd_indices): # convert indices back to IPD midpoints\n", + " \"\"\"\n", + " This function maps IPD indices, which are discrete values in the range [0, num_classes-1],\n", + " back to continuous IPD values. The resulting continuous values are suitable for\n", + " representing the midpoints of the original IPD ranges in the continuous domain.\n", + "\n", + " Parameters\n", + " ----------\n", + " ipd_indices : array-like\n", + " An array or tensor of IPD indices, which are discrete values obtained from\n", + " discretizing continuous IPDs into `num_classes` bins by the function discretise(ipds).\n", + "\n", + " Returns\n", + " -------\n", + " array-like\n", + " An array or tensor of continuous IPD midpoints, corresponding to the provided\n", + " `ipd_indices`. The midpoints are computed based on the assumed discretization\n", + " strategy, and are in the range (-pi/2, pi/2).\n", + "\n", + " Notes\n", + " -----\n", + " - `num_classes` should be defined in the surrounding scope and should be the same\n", + " value that was used for discretization.\n", + " - The input `ipd_indices` and the output will have the same shape.\n", + " - The output type (e.g., NumPy array, PyTorch tensor) will match the input type.\n", + " \"\"\"\n", + " return (ipd_indices+0.5)/num_classes*np.pi-np.pi/2" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Batch and sample size configuration\n", + "\n", + "sample size = batch_size * n_training_batches" + ], + "metadata": { + "id": "zvdDe_9rizLj" + }, + "id": "zvdDe_9rizLj" + }, + { + "cell_type": "code", + "source": [ + "# Parameters for training. These aren't optimal, but instead designed\n", + "# to give a reasonable result in a small amount of time for the tutorial!\n", + "if my_computer_is_slow:\n", + " batch_size = 64\n", + " n_training_batches = 64\n", + "else:\n", + " batch_size = 128\n", + " n_training_batches = 128\n", + "n_testing_batches = 32\n", + "num_samples = batch_size*n_training_batches\n", + "\n", + "# NOTE 1:A batch is a subset of the training dataset used for a single update of the model parameters.\n", + "# Rather than updating model parameters after processing each individual data point (stochastic gradient descent),\n", + "# batches allow the network to update parameters after processing a group of data points.\n", + "# This approach is called mini-batch gradient descent and is more computationally efficient than stochastic gradient descent.\n", + "# The size of a batch, known as the batch size, is an important hyperparameter and can affect\n", + "# the model's training dynamics and performance.\n", + "\n", + "# NOTE2 : Small batch sizes improve generalization through noisier gradients and\n", + "# require less memory, making them ideal for limited resources, but they may\n", + "# lead to slower computation and less stable convergence due to noisier gradient\n", + "# updates. Conversely, large batch sizes enhance computational efficiency and stability\n", + "# of gradient estimates due to better GPU utilization, but they demand more memory and\n", + "# might result in poorer generalization due to the risk of converging to sharp minima\n", + "# that don't generalize well on unseen data." + ], + "metadata": { + "id": "uykR63jhP3b1" + }, + "id": "uykR63jhP3b1", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Data generator function (iterating over the data in batches)" + ], + "metadata": { + "id": "faW6ygTkP8T9" + }, + "id": "faW6ygTkP8T9" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc5fdc4f", + "metadata": { + "id": "fc5fdc4f" + }, + "outputs": [], + "source": [ + "# Generator function iterates over the data in batches\n", + "# We randomly permute the order of the data to improve learning\n", + "def data_generator(ipds, spikes):\n", + " \"\"\"\n", + " Generate batches of data, iterating over IPDs and spikes in a randomized order.\n", + "\n", + " This generator function yields shuffled batches of interaural phase differences (IPDs) and spikes,\n", + " facilitating mini-batch gradient descent training of a model. The order of the data is randomized\n", + " to improve learning, mitigating the risk of the model memorizing the order of the training data\n", + " (overfitting) and helping the model generalize better to unseen data.\n", + "\n", + " Parameters\n", + " ----------\n", + " ipds : Tensor\n", + " A 1D tensor of IPD values.\n", + " Shape: (n_samples, )\n", + " spikes : Tensor\n", + " A 3D tensor representing a batch of input spike trains.\n", + " Shape: (n_samples, duration_steps, input_size)\n", + "\n", + " Yields\n", + " ------\n", + " spike_batch : Tensor\n", + " A 3D tensor containing a batch of input spike trains.\n", + " Shape: (batch_size, duration_steps, input_size)\n", + " ipd_batch : Tensor\n", + " A 1D tensor containing a batch of IPD values.\n", + " Shape: (batch_size, )\n", + "\n", + " Notes\n", + " -----\n", + " - `batch_size` should be defined in the surrounding scope or passed as an argument.\n", + " - Ensure that `ipds` and the first dimension of `spikes` have the same size.\n", + " - The generator yields `spike_batch` and `ipd_batch` which are randomly shuffled batches of `spikes` and `ipds` respectively.\n", + " \"\"\"\n", + " perm = torch.randperm(spikes.shape[0])\n", + " spikes = spikes[perm, :, :]\n", + " ipds = ipds[perm]\n", + " n, _, _ = spikes.shape\n", + " n_batch = n//batch_size\n", + " for i in range(n_batch):\n", + " spike_batch = spikes[i*batch_size:(i+1)*batch_size, :, :] # spike_batch\n", + " ipd_batch = ipds[i*batch_size:(i+1)*batch_size] # ipd_batch\n", + " yield spike_batch, ipd_batch # yield means that at each function call the function returns the next result of the loop interation" + ] + }, + { + "cell_type": "markdown", + "id": "3bb91016", + "metadata": { + "id": "3bb91016" + }, + "source": [ + "## Construct the Spiking Model\n", + "\n", + "Next we'll implement a version of the model with spikes to see how that changes performance. We'll just add a single hidden feed-forward layer of spiking neurons between the input and the output layers. This layer will be spiking, so we need to use the surrogate gradient descent approach.\n", + "\n", + "\n", + "\n", + "\"Full" + ] + }, + { + "cell_type": "markdown", + "id": "03f5456e", + "metadata": { + "id": "03f5456e" + }, + "source": [ + "#### Surrogate gradient descent setup\n", + "\n", + "First, this is the key part of surrogate gradient descent, a function where we override the computation of the gradient to replace it with a smoothed gradient. You can see that in the forward pass (method ``forward``) it returns the Heaviside function of the input (takes value 1 if the input is ``>0``) or value 0 otherwise. In the backwards pass, it returns the gradient of a sigmoid function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5fabc7b", + "metadata": { + "id": "e5fabc7b" + }, + "outputs": [], + "source": [ + "beta = 5\n", + "\n", + "class SurrGradSpike(torch.autograd.Function):\n", + " \"\"\"\n", + " This class allows for the approximation of gradients for non-differentiable spiking functions, enabling\n", + " the backpropagation of errors in networks that incorporate spiking neurons. The forward method applies\n", + " a thresholding logic, mimicking the firing of a neuron, while the backward method implements the surrogate\n", + " gradient calculation.\n", + "\n", + " Methods\n", + " -------\n", + " @staticmethod\n", + " forward(ctx, input):\n", + " Computes the forward propagation step in the neural network. This method applies a specific logic to\n", + " mimic the all-or-none spiking nature of biological neurons. It generates a binary output corresponding\n", + " to whether each neuron in the input tensor has fired or not.\n", + " Parameters:\n", + " ctx : torch.autograd.function._ContextMethodMixin\n", + " A context object for storing information necessary for the backward computation.\n", + " input : torch.Tensor\n", + " A tensor containing the input data, typically the neuronal activations in form of the membrane potential,\n", + " for which the output firing response will be computed.\n", + " Returns:\n", + " torch.Tensor: A tensor with the same shape as input, filled with binary values indicating whether\n", + " each neuron has fired (1.0) or not (0.0).\n", + "\n", + " @staticmethod\n", + " backward(ctx, grad_output):\n", + " Computes the backward propagation step in the neural network. This method calculates the surrogate\n", + " gradients of the loss function with respect to the input activations. It is designed to work with\n", + " the non-differentiable nature of spiking neurons by approximating the gradients.\n", + " Parameters:\n", + " ctx : torch.autograd.function._ContextMethodMixin\n", + " A context object that has the information stashed during the forward pass.\n", + " grad_output : torch.Tensor\n", + " A tensor containing the gradient of the loss function with respect to the outputs of the forward method.\n", + " Returns:\n", + " torch.Tensor: A tensor containing the surrogate gradients of the loss function with respect to\n", + " the input activations, which can be backpropagated through the rest of the network.\n", + " \"\"\"\n", + " @staticmethod\n", + " def forward(ctx, input):\n", + " ctx.save_for_backward(input)\n", + " out = torch.zeros_like(input)\n", + " out[input > 0] = 1.0\n", + " return out\n", + " @staticmethod\n", + " def backward(ctx, grad_output):\n", + " input, = ctx.saved_tensors\n", + " # Original SPyTorch/SuperSpike gradient\n", + " # This seems to be a typo or error? But it works well\n", + " # grad = grad_output/(100*torch.abs(input)+1.0)**2\n", + " # Sigmoid\n", + " grad = grad_output*beta*torch.sigmoid(beta*input)*(1-torch.sigmoid(beta*input))\n", + " return grad\n", + "\n", + "spike_fn = SurrGradSpike.apply # allows the defined class to be used as a function." + ] + }, + { + "cell_type": "markdown", + "id": "911318ee", + "metadata": { + "id": "911318ee" + }, + "source": [ + "#### Network creation function: init_weight_matrices()\n", + "\n" + ] + }, + { + "cell_type": "code", + "source": [ + "num_hidden = 30\n", + "\n", + "# Weights and uniform weight initialisation\n", + "def init_weight_matrices():\n", + " # Input to hidden layer\n", + " W1 = nn.Parameter(torch.empty((input_size, num_hidden), device=device, dtype=dtype, requires_grad=True))\n", + " fan_in, _ = nn.init._calculate_fan_in_and_fan_out(W1)\n", + " bound = 1 / np.sqrt(fan_in)\n", + " nn.init.uniform_(W1, -bound, bound)\n", + " # Hidden layer to output\n", + " W2 = nn.Parameter(torch.empty((num_hidden, num_classes), device=device, dtype=dtype, requires_grad=True))\n", + " fan_in, _ = nn.init._calculate_fan_in_and_fan_out(W2)\n", + " bound = 1 / np.sqrt(fan_in)\n", + " nn.init.uniform_(W2, -bound, bound)\n", + " return W1, W2" + ], + "metadata": { + "id": "6wt4agZbMldc" + }, + "id": "6wt4agZbMldc", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#### Forward path calculation function: snn()" + ], + "metadata": { + "id": "s5LKfcTBMQKJ" + }, + "id": "s5LKfcTBMQKJ" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b072bb5", + "metadata": { + "id": "7b072bb5" + }, + "outputs": [], + "source": [ + "\n", + "\n", + "# Run the simulation\n", + "def snn(input_spikes, W1, W2, tau=20*ms):\n", + " # First layer: input to hidden\n", + " v = torch.zeros((batch_size, num_hidden), device=device, dtype=dtype)\n", + " s = torch.zeros((batch_size, num_hidden), device=device, dtype=dtype)\n", + " s_rec = [s]\n", + " h = torch.einsum(\"abc,cd->abd\", (input_spikes, W1))\n", + " alpha = np.exp(-dt/tau)\n", + " for t in range(duration_steps - 1):\n", + " new_v = (alpha*v + h[:, t, :])*(1-s) # multiply by 0 after a spike\n", + " s = spike_fn(v-1) # threshold of 1\n", + " v = new_v\n", + " s_rec.append(s)\n", + " s_rec = torch.stack(s_rec, dim=1)\n", + " # Second layer: hidden to output\n", + " v = torch.zeros((batch_size, num_classes), device=device, dtype=dtype)\n", + " s = torch.zeros((batch_size, num_classes), device=device, dtype=dtype)\n", + " v_rec = [v]\n", + " h = torch.einsum(\"abc,cd->abd\", (s_rec, W2))\n", + " alpha = np.exp(-dt/tau)\n", + " for t in range(duration_steps - 1):\n", + " # v = alpha * v + torch.where(h[:, t, :] > 0, h[:, t, :], torch.zeros_like(h[:, t, :])) # VB allow only positive inputs to change the membrane pot.\n", + " v = alpha*v + h[:, t, :]\n", + " v_rec.append(v)\n", + " v_rec = torch.stack(v_rec, dim=1)\n", + " # Return recorded membrane potential of output\n", + " return v_rec" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8b4fce85" + }, + "source": [ + "## Analysis Function\n", + "\n", + "This function computes the training and test accuracy, and plots histograms and confusion matrices to understand the errors it's making." + ], + "id": "8b4fce85" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9cc91c87" + }, + "outputs": [], + "source": [ + "def analyse(ipds, spikes, label, run, plot_analysis=1):\n", + " \"\"\"\n", + " Analyse the performance of a classifier on interaural phase difference (IPD) data.\n", + "\n", + " This function evaluates the accuracy and error of a classifier by comparing its\n", + " output with true IPD values. It computes the mean and standard deviation of the\n", + " classifier's accuracy and the absolute error in degrees. Additionally, it can\n", + " generate histograms and a confusion matrix to visualize the results.\n", + "\n", + " Parameters:\n", + " ipds (array): Array of true IPD values.\n", + " spikes (array): Array of spike data corresponding to the IPDs.\n", + " label (str): Label for the data, used in plot titles.\n", + " run (callable): Function that runs the classifier on a batch of spike data.\n", + " plot_analysis (bool, optional): If True, plot histograms and confusion matrix.\n", + "\n", + " Returns:\n", + " tuple: Tuple containing mean and standard deviation of classifier accuracy,\n", + " and mean and standard deviation of absolute error in degrees.\n", + " \"\"\"\n", + " # Initialize lists to store batch-wise accuracies, true IPD values, and estimated IPD values.\n", + " accs = [] # Stores accuracy for each batch\n", + " ipd_true = [] # Stores the true IPD values\n", + " ipd_estimated = [] # Stores the estimated IPD values\n", + "\n", + " # Initialize the confusion matrix for classifier evaluation\n", + " confusion = np.zeros((num_classes, num_classes))\n", + "\n", + " # Iterate over batches of data (spikes and corresponding IPDs) generated randomly\n", + " for spike_batch, ipd_batch in data_generator(ipds, spikes): #Generate batches of data, iterating over IPDs and spikes in a randomized order.\n", + " # Discretize the IPD values in the batch by mapping them to their respective classes\n", + " ipd_class_batch = discretise(ipd_batch)\n", + "\n", + " # Run the neural network classifier on the spike batch\n", + " output = run(spike_batch)\n", + "\n", + " # Aggregate the network's output over the time dimension\n", + " m = torch.sum(output, 1)\n", + "\n", + " # Use argmax to select the class with the highest score\n", + " _, ipd_class_batch_estimated = torch.max(m, 1)\n", + " # Note: We don’t use softmax(m) in the forward path but only torch.max(m) because:\n", + " # - The task only requires class estimated, not probabilities.\n", + " # - torch.max is sufficient to identify the estimated class index.\n", + " # - Softmax would add unnecessary computational cost without affecting the correctness of the predictions.\n", + "\n", + "\n", + " # Update the confusion matrix with true and estimated class values\n", + " for i, j in zip(ipd_class_batch.detach().cpu().numpy(), ipd_class_batch_estimated.detach().cpu().numpy()): # update the confusion matrix\n", + " confusion[j, i] += 1\n", + " # This code updates a confusion matrix by counting occurrences of true and predicted class pairs for a batch of data:\n", + " # confusion[j, i] += 1:\n", + " # - Increments the matrix cell at (j, i):\n", + " # - j: Predicted class.\n", + " # - i: True class.\n", + " # - Tracks how often class i is predicted as class j.\n", + "\n", + "\n", + " # Append the original IPD values to the true IPD list\n", + " ipd_true.append(ipd_batch) # creates a list of arrays\n", + "\n", + " # Convert the argmax predictions back to continuous values and append to estimated IPDs\n", + " ipd_estimated.append(continuise(ipd_class_batch_estimated.detach().cpu().numpy()))\n", + "\n", + " # Calculate batch accuracy by comparing predictions to labels\n", + " tmp = np.mean((ipd_class_batch == ipd_class_batch_estimated).detach().cpu().numpy()) # compare to labels\n", + " accs.append(tmp) # Append batch accuracy to the list\n", + "\n", + " # Flatten the lists of true and estimated IPDs into single arrays\n", + " ipd_true = np.hstack(ipd_true) # connetecates the arrays in the list horizontally to create a single flattened array\n", + " ipd_estimated = np.hstack(ipd_estimated)\n", + "\n", + " # Compute absolute errors in degrees between true and estimated IPDs\n", + " abs_errors_deg = abs(ipd_true-ipd_estimated)*180/np.pi\n", + "\n", + " # Calculate mean and standard deviation of the classifier accuracy in percentage\n", + " classifier_accuracy_mean = 100*np.mean(accs) # in percent\n", + " classifier_accuracy_std = 100*np.std(accs) # in percent\n", + "\n", + " # Calculate mean and standard deviation of the absolute error in degrees\n", + " absolute_error_mean = np.mean(abs_errors_deg) # in degree\n", + " absolute_error_std = np.std(abs_errors_deg) # in degree\n", + "\n", + " # Print results for the classifier's accuracy and absolute error\n", + " print(f\"{label} classifier accuracy: {100*np.mean(accs):.1f}%\")\n", + " print(f\"{label} absolute error: {np.mean(abs_errors_deg):.1f} deg \\n\")\n", + "\n", + " # If visualization is requested, plot the results\n", + " if plot_analysis:\n", + " plt.figure(figsize=(10, 4), dpi=100)\n", + "\n", + " # Plot histograms of true and estimated IPDs\n", + " plt.subplot(121)\n", + " plt.hist(ipd_true*180/np.pi, bins=num_classes, label='True')\n", + " plt.hist(ipd_estimated*180/np.pi, bins=num_classes, label='Estimated')\n", + " plt.xlabel(\"IPD\")\n", + " plt.yticks([])\n", + " plt.legend(loc='best')\n", + " plt.title(label)\n", + "\n", + " # Normalize the confusion matrix and plot it\n", + " plt.subplot(122)\n", + " confusion /= np.sum(confusion, axis=0)[np.newaxis, :]\n", + " ConfusionMatrix = plt.imshow(confusion, interpolation='nearest', aspect='auto', origin='lower', extent=(-90, 90, -90, 90))\n", + " plt.xlabel('True IPD')\n", + " plt.ylabel('Estimated IPD')\n", + " plt.title('Confusion matrix')\n", + " plt.tight_layout()\n", + "\n", + " # Add a color bar with the label \"Probability\"\n", + " cbar = plt.colorbar(ConfusionMatrix) # Add color bar\n", + " cbar.set_label('Probability') # Set the label for the color bar\n", + " plt.tight_layout()\n", + "\n", + " # Return the computed metrics\n", + " return classifier_accuracy_mean, classifier_accuracy_std, absolute_error_mean, absolute_error_std\n" + ], + "id": "9cc91c87" + }, + { + "cell_type": "markdown", + "id": "0a1662e0", + "metadata": { + "id": "0a1662e0" + }, + "source": [ + "## Test: Training and Analyses\n", + "\n", + "We train it as before, except that we modify the functions to take the two weight matrices into account." + ] + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "R6YKK_5EdBB9" + }, + "id": "R6YKK_5EdBB9" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5d558df", + "metadata": { + "id": "a5d558df", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 683 + }, + "outputId": "900a7496-7334-469c-b9e6-bd0c84cb649e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Want loss for epoch 1 to be about 2.48, multiply m by constant to get this\n", + "Epoch 1: loss=2.23891\n", + "Epoch 2: loss=1.25036\n", + "Epoch 3: loss=0.91745\n", + "Epoch 4: loss=0.80256\n", + "Epoch 5: loss=0.71790\n", + "Epoch 6: loss=0.65367\n", + "Epoch 7: loss=0.64448\n", + "Epoch 8: loss=0.61247\n", + "Epoch 9: loss=0.54778\n", + "Epoch 10: loss=0.54693\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "# TRAINING\n", + "\n", + "# Training parameters\n", + "nb_epochs = 10 # is quick, it won't have converged.\n", + "# Note: An epoch is one complete pass through the entire training dataset.\n", + "# During an epoch, the neural network processes every example in the dataset once.\n", + "# Completing an epoch means that every data point has been used for calculating the loss and updating the model parameters.\n", + "# Multiple epochs are usually required for the network to converge to an optimal set of parameters.\n", + "lr = 0.01 # learning rate\n", + "\n", + "# Generate the training data\n", + "ipds, spikes, _ = random_ipd_input_signal(num_samples) # num_samples = batch_size * num_training\n", + "\n", + "# Initialise a weight matrices\n", + "W1, W2 = init_weight_matrices()\n", + "\n", + "# Optimiser and loss function\n", + "optimizer = torch.optim.Adam([W1, W2], lr=lr)\n", + "log_softmax_fn = nn.LogSoftmax(dim=1)\n", + "loss_fn = nn.NLLLoss()\n", + "\n", + "print(f\"Want loss for epoch 1 to be about {-np.log(1/num_classes):.2f}, multiply m by constant to get this\")\n", + "\n", + "loss_hist = []\n", + "for e in range(nb_epochs):\n", + " local_loss = []\n", + " for spike_batch, ipd_batch in data_generator(discretise(ipds), spikes):\n", + " # Run network\n", + " output = snn(spike_batch, W1, W2)\n", + "\n", + " # Compute cross entropy loss\n", + " m = torch.sum(output, 1)*0.01 # Agregation fuction: Sum across time dimension. Note: We want loss for epoch 1 to be about -np.log(1/num_classes), multiply m by a constant to get this\n", + " loss = loss_fn(log_softmax_fn(m), ipd_batch)\n", + " local_loss.append(loss.item())\n", + "\n", + " # The softmax function transforms the output of a neural network's final layer into a probability\n", + " # distribution over multiple classes in such a way that increasing the score of one class\n", + " # decreases the probabilities of the other classes. It does this by exponentiating each logit\n", + " # and then normalizing these values so that they sum to 1. This is important because it ensures that\n", + " # the predicted values for each class sum up to 1.0. This probability distribution allows us to\n", + " # interpret the network's output as the likelihood of each class being the correct class.\n", + " # Training Objective: The training process aims to increase the probability of the correct class.\n", + " # As the model updates its weights to increase the probability (and hence the log probability) of the\n", + " # correct class, the softmax function inherently decreases the probabilities of the other classes due\n", + " # to the normalization step.\n", + " # Using it with the negative log likelihood loss encourages the model to increase the log probability\n", + " # of the correct class.\n", + " # Interpretability: The softmax function's output can be interpreted as class probabilities, which is\n", + " # valuable not only for making predictions but also for understanding the model's confidence in those\n", + " # predictions. This can be useful for post-processing or decision-making based on the network's output\n", + " # probabilities.\n", + "\n", + " # Update gradients\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " loss_hist.append(np.mean(local_loss))\n", + " print(\"Epoch %i: loss=%.5f\"%(e+1, np.mean(local_loss)))\n", + "\n", + "# Plot the loss function over time\n", + "plt.plot(loss_hist)\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Loss')\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "source": [ + "# ANALYSIS\n", + "\n", + "print(f\"Chance accuracy level: {100*1/num_classes:.1f}%\")\n", + "run_func = lambda x: snn(x, W1, W2)\n", + "results_Train = analyse(ipds, spikes, 'Train', run=run_func, plot_analysis=1)\n", + "ipds_test, spikes_test, _ = random_ipd_input_signal(batch_size*n_testing_batches)\n", + "results_Train = analyse(ipds_test, spikes_test, 'Test', run=run_func, plot_analysis=1)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 790 + }, + "id": "YYmxcDagJkcw", + "outputId": "92581f74-5c57-43f6-faf4-060c50b99b01" + }, + "id": "YYmxcDagJkcw", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Chance accuracy level: 8.3%\n", + "Train classifier accuracy: 81.9%\n", + "Train absolute error: 4.8 deg \n", + "\n", + "Test classifier accuracy: 59.8%\n", + "Test absolute error: 7.3 deg \n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "id": "26e19d66", + "metadata": { + "id": "26e19d66" + }, + "source": [ + "## Project\n", + "Change systematically some of the parameters and record the performance. You can do this for example by running the following code in a loop:" + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Example project: Envelope power\n" + ], + "metadata": { + "id": "Ne6AXxucb3oQ" + }, + "id": "Ne6AXxucb3oQ" + }, + { + "cell_type": "markdown", + "source": [ + "This code performs an analysis of the performance of a spiking neural network (SNN) classifier across a range of envelope power values.\n", + "\n", + "Please note that the current code only plots the result of a single training session.\n", + "\n", + "\n", + "##### Suggestion for improvement:\n", + " \n", + "* Run the example code from the EnvelopePower project multiple times.\n", + "\n", + "* You will notice that the results vary significantly between consecutive training sessions.\n", + "\n", + "* To draw robust conclusions, calculate the average across all training sessions.\n", + "\n", + "* Then, plot a graph with the average and the corresponding standard deviation.\n", + "\n", + "* Apply this same approach to your other projects to obtain reliable results." + ], + "metadata": { + "id": "Dfl7Tyy-mJY0" + }, + "id": "Dfl7Tyy-mJY0" + }, + { + "cell_type": "code", + "source": [ + "from tqdm import tqdm # Import the tqdm library for displaying a progress bar\n", + "\n", + "# Set training parameters\n", + "nb_epochs = 10 # Number of epochs (quick training for demonstration)\n", + "lr = 0.01 # Learning rate\n", + "\n", + "# Flag for whether to plot analysis results\n", + "plot_analysis = 0\n", + "\n", + "# Define a range of envelope powers to test\n", + "Envelop_powers = [0, 1, 2, 3, 4, 5, 10, 30, 40, 50, 100]\n", + "\n", + "# Initialize lists to store results for training and testing accuracy and absolute error.\n", + "# Mean and std are calulated over the different batches\n", + "Train_accuracy_mean = [] # Mean training accuracy\n", + "Train_accuracy_std = [] # Standard deviation of training accuracy\n", + "Train_abs_error_mean = [] # Mean training absolute error\n", + "Train_abs_error_std = [] # Standard deviation of training absolute error\n", + "\n", + "Test_accuracy_mean = [] # Mean testing accuracy\n", + "Test_accuracy_std = [] # Standard deviation of testing accuracy\n", + "Test_abs_error_mean = [] # Mean testing absolute error\n", + "Test_abs_error_std = [] # Standard deviation of testing absolute error\n", + "\n", + "results_Train = [] # Stores results from training data\n", + "results_Test = [] # Stores results from test data\n", + "\n", + "# Loop through each envelope power, showing progress with tqdm\n", + "for i, envelope_power in enumerate(tqdm(Envelop_powers, desc=\"Processing Envelope Powers\")):\n", + "\n", + " # Generate training data: interaural phase differences (IPDs) and spike data\n", + " ipds, spikes, _ = random_ipd_input_signal(num_samples)\n", + " plt.imshow(spikes[0, :, :].T, aspect='auto', interpolation='nearest', cmap=plt.cm.gray_r)\n", + "\n", + " # Initialize weight matrices for the neural network classifier\n", + " W1, W2 = init_weight_matrices()\n", + "\n", + " # Define the optimizer and loss functions\n", + " optimizer = torch.optim.Adam([W1, W2], lr=lr)\n", + " log_softmax_fn = nn.LogSoftmax(dim=1)\n", + " loss_fn = nn.NLLLoss()\n", + "\n", + " # Print the expected initial loss\n", + " print(f\"Want loss for epoch 1 to be about {-np.log(1/num_classes):.2f}, multiply m by constant to get this\")\n", + "\n", + " loss_hist = [] # Track loss over epochs\n", + " for e in range(nb_epochs): # Loop through each epoch\n", + " local_loss = [] # Track batch losses for the current epoch\n", + " for spike_batch, ipd_batch in data_generator(discretise(ipds), spikes): # Generate data batches\n", + " # Run the classifier on the batch\n", + " output = snn(spike_batch, W1, W2)\n", + "\n", + " # Compute cross-entropy loss\n", + " m = torch.sum(output, 1) * 0.01 # Aggregate output over the time dimension\n", + " loss = loss_fn(log_softmax_fn(m), ipd_batch)\n", + " local_loss.append(loss.item())\n", + "\n", + " # Update weights\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " # Append the mean loss for the epoch\n", + " loss_hist.append(np.mean(local_loss))\n", + " print(\"Epoch %i: loss=%.5f\" % (e + 1, np.mean(local_loss)))\n", + "\n", + " # Optionally plot the loss curve over epochs\n", + " if plot_analysis:\n", + " # Plot the loss function over time\n", + " plt.plot(loss_hist)\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Loss')\n", + " plt.tight_layout()\n", + "\n", + "\n", + " # Analyse training data\n", + " print(f\"Chance accuracy level: {100*1/num_classes:.1f}%\")\n", + "\n", + " run_func = lambda x: snn(x, W1, W2) # Define the classifier function\n", + " results_Train = analyse(ipds, spikes, 'Train', run=run_func, plot_analysis=0)\n", + "\n", + " # Generate and analyse test data\n", + " ipds_test, spikes_test, _ = random_ipd_input_signal(batch_size*n_testing_batches)\n", + " results_Test = analyse(ipds_test, spikes_test, 'Test', run=run_func, plot_analysis=0)\n", + "\n", + " # Append training results\n", + " Train_accuracy_mean.append(results_Train[0])\n", + " Train_accuracy_std.append(results_Train[1])\n", + " Train_abs_error_mean.append(results_Train[2])\n", + " Train_abs_error_std.append(results_Train[3])\n", + "\n", + " # Append testing results\n", + " Test_accuracy_mean.append(results_Test[0])\n", + " Test_accuracy_std.append(results_Test[1])\n", + " Test_abs_error_mean.append(results_Test[2])\n", + " Test_abs_error_std.append(results_Test[3])\n", + "\n", + "# Plot training and testing accuracy with error bars\n", + "plt.figure(figsize=(8, 6))\n", + "plt.errorbar(Envelop_powers,Train_accuracy_mean, yerr=Train_accuracy_std, label='Training',fmt='o', ecolor='blue', capsize=5)\n", + "plt.errorbar(Envelop_powers,Test_accuracy_mean, yerr=Test_accuracy_std, label='Test', fmt='o', ecolor='red', capsize=5)\n", + "plt.ylim([0,100])\n", + "plt.xlim([-1,50])\n", + "plt.xlabel('Envelop Power')\n", + "plt.ylabel('Accurancy (mean+/-std (%))')\n", + "plt.legend()\n", + "\n", + "# Plot training and testing absolute error with error bars\n", + "plt.figure(figsize=(8, 6))\n", + "plt.errorbar(Envelop_powers,Train_abs_error_mean, yerr=Train_abs_error_std, label='Training',fmt='o', ecolor='blue', capsize=5)\n", + "plt.errorbar(Envelop_powers,Test_abs_error_mean, yerr=Test_abs_error_std, label='Test', fmt='o', ecolor='red', capsize=5)\n", + "plt.ylim([0,100])\n", + "plt.xlim([-1,50])\n", + "plt.xlabel('Envelop Power')\n", + "plt.ylabel('Abs_error (mean+/-std (deg))')\n", + "plt.legend()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "yf14Jn81LBu3", + "outputId": "eabc8114-8fc2-47a0-d332-0293f39dfff0" + }, + "id": "yf14Jn81LBu3", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\rProcessing Envelope Powers: 0%| | 0/11 [00:00" + ] + }, + "metadata": {}, + "execution_count": 27 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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\n" 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.savefig(\"Proj_EnvelopePower.png\") # Save the plot as a PNG file\n", + "\n", + "!cp Proj_EnvelopePower.png \"/content/drive/My Drive/Mini-Project_ColabNotebooks/SBCP_2024/Results_Figures\"\n", + "\n", + "!ls \"/content/drive/My Drive/Mini-Project_ColabNotebooks/SBCP_2024/Results_Figures\"\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "id": "tZ_SqPO-jWLb", + "outputId": "548720e6-ce13-4225-d564-9825a900fb71" + }, + "id": "tZ_SqPO-jWLb", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "example_plot.png Proj_EnvelopePower.png\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {} + } + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + }, + "colab": { + "provenance": [] + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/teaching/Starting_notebook_annotated_for_teaching.ipynb b/teaching/Starting_notebook_annotated_for_teaching.ipynb index 7fe69cd..3089be9 100644 --- a/teaching/Starting_notebook_annotated_for_teaching.ipynb +++ b/teaching/Starting_notebook_annotated_for_teaching.ipynb @@ -1 +1,4719 @@ -{"cells":[{"cell_type":"markdown","id":"dfb23669","metadata":{"id":"dfb23669"},"source":["# Sound localisation with surrogate gradient descent\n","\n","In this notebook, we're going to use surrogate gradient descent to find a solution to the sound localisation problem we solved by hand in the previous notebook. The surrogate gradient descent approach and code is heavily inspired by (certainly not stolen) from [Friedemann Zenke's SPyTorch tutorial](https://github.com/fzenke/spytorch), which I recommend for a deeper dive into the maths."]},{"cell_type":"markdown","source":["## To setup before you start\n","First, download a copy of this notebook to your personal google drive:\n","1. mount your google drive"],"metadata":{"id":"Dg9b4XdMDjPl"},"id":"Dg9b4XdMDjPl"},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9hL7mQSV_tCi","executionInfo":{"status":"ok","timestamp":1706246907486,"user_tz":-60,"elapsed":1648,"user":{"displayName":"Volker Bormuth","userId":"12240905297707951679"}},"outputId":"1386e755-d447-42da-a386-18e3b3d1cc26"},"id":"9hL7mQSV_tCi","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"markdown","source":["2. Save a copy of the notebook to your drive: \"Files\" => \"Save a copy in Drive\"\n","3. Locate where this copy was saved in your dirve: \"Files\" => \"Locate in Drive\"\n","4. Now you can rename the located file and move it to a location of your choice in your google drive\n"],"metadata":{"id":"mdi_e2fvE1FW"},"id":"mdi_e2fvE1FW"},{"cell_type":"markdown","source":["Optional setup: \n","The commands `load_ext autoreload` and `autoreload 2` are used in Jupyter notebooks to set up an automatic reloading of modules before executing a new line of code. This is particularly useful when you are editing external Python files and want those changes to be reflected in the notebook without restarting the kernel."],"metadata":{"id":"Om5jAA54HtET"},"id":"Om5jAA54HtET"},{"cell_type":"code","source":["%load_ext autoreload\n","%autoreload 2"],"metadata":{"id":"bNF1kW30I7Mf"},"execution_count":null,"outputs":[],"id":"bNF1kW30I7Mf"},{"cell_type":"markdown","source":["## Let's start to run the notebook step-by-step"],"metadata":{"id":"Zq-l_P7uFEY_"},"id":"Zq-l_P7uFEY_"},{"cell_type":"code","execution_count":null,"id":"ee3c91b7","metadata":{"id":"ee3c91b7"},"outputs":[],"source":["import os\n","\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from matplotlib.gridspec import GridSpec\n","\n","import torch\n","import torch.nn as nn\n","\n","dtype = torch.float\n","\n","# Check whether a GPU is available\n","if torch.cuda.is_available():\n"," device = torch.device(\"cuda\")\n","else:\n"," device = torch.device(\"cpu\")\n","\n","my_computer_is_slow = True # set this to True if using Colab\n","\n","import pdb\n","import pandas as pd"]},{"cell_type":"markdown","source":["NOTE: PyTorch is an open-source machine learning library primarily developed by Facebook's AI Research lab (FAIR).\n","PyTorch offers a high-level neural network module called torch.nn for building and training neural networks. It includes various layers, loss functions, and optimization algorithms."],"metadata":{"id":"wVU9vnaNBHor"},"id":"wVU9vnaNBHor"},{"cell_type":"markdown","id":"345a4686","metadata":{"id":"345a4686"},"source":["### Creating sound stimulus and encoding spikes in auditory nerve fibers (= input data)"]},{"cell_type":"markdown","id":"bd312e52","metadata":{"id":"bd312e52"},"source":["The following function creates a set of stimuli that can be used for training or testing. We have two ears (0 and 1), and ear 1 will get a version of the signal delayed by an IPD we can write as $\\alpha$ in equations (``ipd`` in code). The basic signal is a sine wave as in the previous notebook, made positive, so $(1/2)(1+\\sin(\\theta)$. In addition, for each ear there will be $N_a$ neurons per ear (``anf_per_ear`` because these are auditory nerve fibres). Each neuron generates Poisson spikes at a certain firing rate, and these Poisson spike trains are independent. In addition, since it is hard to train delays, we seed it with uniformly distributed delays from a minimum of 0 to a maximum of $\\pi/2$ in each ear, so that the differences between the two ears can cover the range of possible IPDs ($-\\pi/2$ to $\\pi/2$). We do this directly by adding a phase delay to each neuron. So for ear $i\\in\\{0,1\\}$ and neuron $j$ at time $t$ the angle $\\theta=2\\pi f t+i\\alpha+j\\pi/2N_a$. Finally, we generate Poisson spike trains with a rate $R_\\mathrm{max}((1/2)(1+\\sin(\\theta)))^k$. $R_\\mathrm{max}$ (``rate_max``) is the maximum instantaneous firing rate, and $k$ (``envelope_power``) is a constant that sharpens the envelope. The higher $R_\\mathrm{max}$ and $k$ the easier the problem (try it out on the cell below to see why).\n","\n","Here's a picture of the architecture for the stimuli:\n","\n","![Stimuli architecture](https://github.com/neural-reckoning/cosyne-tutorial-2022/blob/main/arch-stimuli.png?raw=1)\n","\n","The functions below return two arrays ``ipd`` and ``spikes``. ``ipd`` is an array of length ``num_samples`` that gives the true IPD, and ``spikes`` is an array of 0 (no spike) and 1 (spike) of shape ``(num_samples, duration_steps, 2*anf_per_ear)``, where ``duration_steps`` is the number of time steps there are in the stimulus."]},{"cell_type":"code","execution_count":null,"id":"5bb26693","metadata":{"id":"5bb26693","outputId":"5bb0af54-b9ea-4d9f-8ccd-d2c418ca8601","colab":{"base_uri":"https://localhost:8080/","height":265}},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# We use the following constants to make equations look nicer below\n","second = 1\n","ms = 1e-3\n","Hz = 1\n","\n","# Stimulus and simulation parameters\n","dt = 1*ms # large time step to make simulations run faster for tutorial\n","anf_per_ear = 100 # number of auditory nerve fibers connected to each ear with independent noise\n","envelope_power = 2 # higher values make sharper envelopes. Easier by eye => But does the network perform better ?\n","rate_max = 600*Hz # maximum Poisson firing rate\n","f = 20*Hz # stimulus frequency\n","duration = .1*second # stimulus duration\n","duration_steps = int(np.round(duration/dt)) # number of simulation steps\n","input_size = 2*anf_per_ear\n","\n","# Generate an input signal (spike array) from array of true IPDs\n","def input_signal(ipd):\n"," \"\"\"\n"," Generate a Poisson spike train based on an input Interaural Phase Difference (IPD) array\n"," and the delays imposed by the individual auditory nerve fibers.\n","\n"," Parameters\n"," ----------\n"," ipd : array-like\n"," An array of true Interaural Phase Differences (IPD). Shape: (num_samples, )\n","\n"," Returns\n"," -------\n"," spikes : ndarray\n"," A binary array indicating spike occurrences, shaped (num_samples, duration_steps, 2*anf_per_ear).\n"," `spikes[i, j, k]` is 1 if a spike occurred at the jth time step for the ith IPD in the kth auditory nerve fiber,\n"," and 0 otherwise.\n","\n"," Notes\n"," -----\n"," - The function first calculates an array of phases (`phi`) to define the sinudoidal auditory stimulus and adds a random\n"," phase offset because we want that the system learns to infer the angular location of the sound source indepent of its distance\n"," to the source.\n"," - An array of theta values is initialized that will hold the transformed phi values according to the phase delay imposed by the\n"," individual auditory nerve fibers and the ipd between the two ears.\n"," - Different phase delays, ranging from 0 to pi/2, are calculated and added with the ipd value to generate theta.\n"," - Poisson spikes are generated based on the theta values and a sinusoidal modulation of the firing rate.\n"," - The spikes are returned as a binary array, indicating the occurrence of spikes across auditory nerve fibers and time.\n"," \"\"\"\n"," num_samples = len(ipd) # corresponds to the number of different locations of the source in the data set\n","\n"," T = np.arange(duration_steps)*dt # array of times over which the auditory signal is constructed\n"," phi = 2*np.pi*(f*T) + 2*np.pi*np.random.rand() # array of phases corresponding to those times with random offset\n"," # because we want that the system learns to infer the angular location of the sound source indepent of its distance\n"," # to the source. The phase in this array increases linearly.\n","\n"," phase_delays = np.linspace(0, np.pi/2, anf_per_ear) # array of phase delays introduced by the auditory nerve fibers.\n"," # For each ear, we have anf_per_ear different phase delays from 0 to pi/2 so\n"," # that the differences between the two ears can cover the full range from -pi/2 to pi/2\n","\n"," theta = np.zeros((num_samples, duration_steps, 2*anf_per_ear)) # 3D array that holds the spike pattern of all auditory nerve fibers for all the interaural phase difference in the data set.\n"," # num_samples = number of different IPD values in our data set\n"," # duration_step = number of time points in our auditory signal\n"," # 2*anf_per_ear = total number of auditory nerve fibers\n","\n"," # Now we set up these theta values. Some numpy vectorisation logic using broadcasting to implements the idea in the text above.\n"," theta[:, :, :anf_per_ear] = phi[np.newaxis, :, np.newaxis]+phase_delays[np.newaxis, np.newaxis, :]\n"," theta[:, :, anf_per_ear:] = phi[np.newaxis, :, np.newaxis]+phase_delays[np.newaxis, np.newaxis, :]+ipd[:, np.newaxis, np.newaxis]\n","\n"," # now generate Poisson spikes at the given firing rate as in the previous notebook\n"," spikes = rate_max*dt*(0.5*(1+np.sin(theta)))**envelope_power > np.random.rand(num_samples, duration_steps, 2*anf_per_ear)\n"," return spikes\n","\n","# Generate some true IPDs from (-pi/2, pi/2) and corresponding spike arrays\n","def random_ipd_input_signal(num_samples, tensor=True):\n"," \"\"\"\n"," Generate random Interaural Phase Differences (IPDs) and then corresponding spike arrays using\n"," the function input_signal(idp).\n","\n"," The function generates `num_samples` IPDs, uniformly distributed in the range (-pi/2, pi/2).\n"," It then generates corresponding spike arrays using the `input_signal` function.\n"," Optionally, IPDs and spike arrays can be converted to PyTorch tensors.\n","\n"," Parameters\n"," ----------\n"," num_samples : int\n"," The number of IPD samples to generate.\n"," tensor : bool, optional\n"," If True, converts the IPDs and spike arrays to PyTorch tensors before returning them.\n"," If False, they are returned as NumPy arrays. Default is True.\n","\n"," Returns\n"," -------\n"," ipd : ndarray or Tensor\n"," An array of randomly generated IPDs. Shape: (num_samples, ).\n"," Returned as a PyTorch tensor if `tensor` is True, otherwise as a NumPy array.\n"," spikes : ndarray or Tensor\n"," A binary array indicating spike occurrences along time, generated by `input_signal` based on `ipd`.\n"," Returned as a PyTorch tensor if `tensor` is True, otherwise as a NumPy array.\n"," Shaped: (num_samples, duration_steps, 2*anf_per_ear)\n","\n"," Notes\n"," -----\n"," - Ensure that the `input_signal` function is defined in your environment as it is called within this function.\n"," - If `tensor` is True, ensure that PyTorch is installed and configured in your environment.\n","\n"," Examples\n"," --------\n"," >>> ipd, spikes = random_ipd_input_signal(50, tensor=False)\n"," >>> print(ipd.shape, spikes.shape)\n"," (50,) (50, duration_steps, 2*anf_per_ear)\n"," \"\"\"\n"," ipd = np.random.rand(num_samples)*np.pi-np.pi/2 # uniformly random in (-pi/2, pi/2)\n"," spikes = input_signal(ipd)\n"," if tensor:\n"," ipd = torch.tensor(ipd, device=device, dtype=dtype)\n"," spikes = torch.tensor(spikes, device=device, dtype=dtype)\n"," return ipd, spikes\n","\n","# Plot for a few true IPDs the generated spike trains of the auditory nerve fibers to show how it looks.\n","# The first 100 lines are auditory nerve fiber responses of the righ ear and the others are from the left ear.\n","# You note that the IPDs was applied to the left ear's fibers.\n","ipd, spikes = random_ipd_input_signal(8)\n","plt.figure(figsize=(10, 4), dpi=100)\n","for i in range(8):\n"," plt.subplot(2, 4, i+1)\n"," plt.imshow(spikes[i, :, :].T, aspect='auto', interpolation='nearest', cmap=plt.cm.gray_r)\n"," plt.title(f'True IPD = {int(ipd[i]*180/np.pi)} deg')\n"," if i>=4:\n"," plt.xlabel('Time (steps)')\n"," if i%4==0:\n"," plt.ylabel('Input neuron index')\n","plt.tight_layout()"]},{"cell_type":"markdown","id":"177a735f","metadata":{"id":"177a735f"},"source":["### Classification approach\n","\n","Now the aim is to take these input spikes and infer the IPD. We can do this either by discretising and using a classification approach, or with a regression approach. For the moment, let's try it with a classification approach.\n","\n","We discretise the IPD range of $[-\\pi/2, \\pi/2]$ into $N_c$ (``num_classes``) equal width segments. Replace angle $\\phi$ with the integer part (floor) of $(\\phi+\\pi/2)N_c/\\pi$. We also convert the arrays into PyTorch tensors for later use. The algorithm will now guess the index $i$ of the segment, converting that to the midpoint of the segment $\\phi_i=a+(i+1/2)(b-a)/N_c$ when needed.\n","\n","The algorithm will work by outputting a length $N_c$ vector $y$ and the index of the maximum value of y will be the guess as to the class (1-hot encoding), i.e. $i_\\mathrm{est}=\\mathrm{argmax}_i y_i$. We will perform the training with a softmax and negative loss likelihood loss, which is a standard approach in machine learning, especially in the context of multi-class classification tasks.\n","\n","\n","\n","\n","\n","\n","#### Note on the use of the softmax function:\n","Probability Distribution: The softmax function transforms the output of a neural network's final layer into a probability distribution over multiple classes. This is important because it ensures that the predicted values for each class sum up to 1.0. This probability distribution allows us to interpret the network's output as the likelihood of each class being the correct class.\n","\n","Differentiability: The softmax function is differentiable, which makes it suitable for training neural networks using gradient-based optimization techniques, such as gradient descent. The gradients of the softmax function can be efficiently computed during backpropagation, enabling the network to learn and update its parameters.\n","\n","Cross-Entropy Loss: In conjunction with the softmax function, the cross-entropy loss (also known as log loss) is often used as the loss function for training neural networks. The cross-entropy loss measures the dissimilarity between the predicted probabilities and the true class labels. It encourages the network to assign high probabilities to the correct class and low probabilities to other classes, effectively optimizing the model's predictions for classification tasks.\n","\n","Multi-Class Classification: For multi-class classification problems, where an input can belong to one of several classes, the softmax function provides a natural way to model and predict class probabilities. It helps in making decisions about which class an input most likely belongs to.\n","\n","Interpretability: The softmax function's output can be interpreted as class probabilities, which is valuable not only for making predictions but also for understanding the model's confidence in those predictions. This can be useful for post-processing or decision-making based on the network's output probabilities."]},{"cell_type":"code","execution_count":null,"id":"3f817078","metadata":{"id":"3f817078","outputId":"34fca1cf-d589-4986-b0f1-68901774195e","colab":{"base_uri":"https://localhost:8080/"}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of classes = 12\n"]}],"source":["# classes at 15 degree increments\n","num_classes = 180//15\n","print(f'Number of classes = {num_classes}')\n","\n","def discretise(ipds):\n"," \"\"\"\n"," Discretize Interaural Phase Differences (IPDs) to generate class labels.\n","\n"," The function maps IPDs, which are continuous values in the range (-pi/2, pi/2),\n"," to discrete classes in the range [0, num_classes-1]. The resulting discrete values\n"," are suitable for classification tasks.\n","\n"," Parameters\n"," ----------\n"," ipds : Tensor\n"," A tensor containing continuous IPD values. The values should be in the range (-pi/2, pi/2).\n","\n"," Returns\n"," -------\n"," Tensor\n"," A tensor containing the classification of IPD values, in the range [0, num_classes-1].\n","\n"," Notes\n"," -----\n"," - Assumes the input `ipds` is a PyTorch tensor.\n"," - `num_classes` should be defined in the surrounding scope.\n"," - The output tensor will have the same shape as the input `ipds`.\n","\n"," Examples\n"," --------\n"," >>> ipds = torch.tensor([-np.pi/2, 0, np.pi/2])\n"," >>> ipd_indices = discretise(ipds)\n"," \"\"\"\n"," return ((ipds+np.pi/2)*num_classes/np.pi).long() # assumes input is tensor\n","\n","def continuise(ipd_indices): # convert indices back to IPD midpoints\n"," \"\"\"\n"," This function maps IPD indices, which are discrete values in the range [0, num_classes-1],\n"," back to continuous IPD values. The resulting continuous values are suitable for\n"," representing the midpoints of the original IPD ranges in the continuous domain.\n","\n"," Parameters\n"," ----------\n"," ipd_indices : array-like\n"," An array or tensor of IPD indices, which are discrete values obtained from\n"," discretizing continuous IPDs into `num_classes` bins by the function discretise(ipds).\n","\n"," Returns\n"," -------\n"," array-like\n"," An array or tensor of continuous IPD midpoints, corresponding to the provided\n"," `ipd_indices`. The midpoints are computed based on the assumed discretization\n"," strategy, and are in the range (-pi/2, pi/2).\n","\n"," Notes\n"," -----\n"," - `num_classes` should be defined in the surrounding scope and should be the same\n"," value that was used for discretization.\n"," - The input `ipd_indices` and the output will have the same shape.\n"," - The output type (e.g., NumPy array, PyTorch tensor) will match the input type.\n"," \"\"\"\n"," return (ipd_indices+0.5)/num_classes*np.pi-np.pi/2"]},{"cell_type":"markdown","source":["### Set training parameters:"],"metadata":{"id":"zvdDe_9rizLj"},"id":"zvdDe_9rizLj"},{"cell_type":"code","execution_count":null,"id":"fc5fdc4f","metadata":{"id":"fc5fdc4f"},"outputs":[],"source":["# Parameters for training. These aren't optimal, but instead designed\n","# to give a reasonable result in a small amount of time for the tutorial!\n","if my_computer_is_slow:\n"," batch_size = 64\n"," n_training_batches = 64\n","else:\n"," batch_size = 128\n"," n_training_batches = 128\n","n_testing_batches = 32\n","num_samples = batch_size*n_training_batches\n","\n","# NOTE 1:A batch is a subset of the training dataset used for a single update of the model parameters.\n","# Rather than updating model parameters after processing each individual data point (stochastic gradient descent),\n","# batches allow the network to update parameters after processing a group of data points.\n","# This approach is called mini-batch gradient descent and is more computationally efficient than stochastic gradient descent.\n","# The size of a batch, known as the batch size, is an important hyperparameter and can affect\n","# the model's training dynamics and performance.\n","\n","# NOTE2 : Small batch sizes improve generalization through noisier gradients and\n","# require less memory, making them ideal for limited resources, but they may\n","# lead to slower computation and less stable convergence due to noisier gradient\n","# updates. Conversely, large batch sizes enhance computational efficiency and stability\n","# of gradient estimates due to better GPU utilization, but they demand more memory and\n","# might result in poorer generalization due to the risk of converging to sharp minima\n","# that don't generalize well on unseen data.\n","\n","\n","\n","# Generator function iterates over the data in batches\n","# We randomly permute the order of the data to improve learning\n","def data_generator(ipds, spikes):\n"," \"\"\"\n"," Generate batches of data, iterating over IPDs and spikes in a randomized order.\n","\n"," This generator function yields shuffled batches of interaural phase differences (IPDs) and spikes,\n"," facilitating mini-batch gradient descent training of a model. The order of the data is randomized\n"," to improve learning, mitigating the risk of the model memorizing the order of the training data\n"," (overfitting) and helping the model generalize better to unseen data.\n","\n"," Parameters\n"," ----------\n"," ipds : Tensor\n"," A 1D tensor of IPD values.\n"," Shape: (n_samples, )\n"," spikes : Tensor\n"," A 3D tensor representing a batch of input spike trains.\n"," Shape: (n_samples, duration_steps, input_size)\n","\n"," Yields\n"," ------\n"," spike_batch : Tensor\n"," A 3D tensor containing a batch of input spike trains.\n"," Shape: (batch_size, duration_steps, input_size)\n"," ipd_batch : Tensor\n"," A 1D tensor containing a batch of IPD values.\n"," Shape: (batch_size, )\n","\n"," Notes\n"," -----\n"," - `batch_size` should be defined in the surrounding scope or passed as an argument.\n"," - Ensure that `ipds` and the first dimension of `spikes` have the same size.\n"," - The generator yields `spike_batch` and `ipd_batch` which are randomly shuffled batches of `spikes` and `ipds` respectively.\n"," \"\"\"\n"," perm = torch.randperm(spikes.shape[0])\n"," spikes = spikes[perm, :, :]\n"," ipds = ipds[perm]\n"," n, _, _ = spikes.shape\n"," n_batch = n//batch_size\n"," for i in range(n_batch):\n"," spike_batch = spikes[i*batch_size:(i+1)*batch_size, :, :] # spike_batch\n"," ipd_batch = ipds[i*batch_size:(i+1)*batch_size] # ipd_batch\n"," yield spike_batch, ipd_batch # yield means that at each function call the function returns the next result of the loop interation"]},{"cell_type":"markdown","id":"3bb91016","metadata":{"id":"3bb91016"},"source":["### Construct the Spiking model\n","\n","Next we'll implement a version of the model with spikes to see how that changes performance. We'll just add a single hidden feed-forward layer of spiking neurons between the input and the output layers. This layer will be spiking, so we need to use the surrogate gradient descent approach.\n","\n","![Full architecture](https://github.com/neural-reckoning/cosyne-tutorial-2022/blob/main/arch-full.png?raw=1)"]},{"cell_type":"markdown","id":"03f5456e","metadata":{"id":"03f5456e"},"source":["#### Surrogate gradient descent\n","\n","First, this is the key part of surrogate gradient descent, a function where we override the computation of the gradient to replace it with a smoothed gradient. You can see that in the forward pass (method ``forward``) it returns the Heaviside function of the input (takes value 1 if the input is ``>0``) or value 0 otherwise. In the backwards pass, it returns the gradient of a sigmoid function."]},{"cell_type":"code","execution_count":null,"id":"e5fabc7b","metadata":{"id":"e5fabc7b"},"outputs":[],"source":["beta = 5\n","\n","class SurrGradSpike(torch.autograd.Function):\n"," \"\"\"\n"," This class allows for the approximation of gradients for non-differentiable spiking functions, enabling\n"," the backpropagation of errors in networks that incorporate spiking neurons. The forward method applies\n"," a thresholding logic, mimicking the firing of a neuron, while the backward method implements the surrogate\n"," gradient calculation.\n","\n"," Methods\n"," -------\n"," @staticmethod\n"," forward(ctx, input):\n"," Computes the forward propagation step in the neural network. This method applies a specific logic to\n"," mimic the all-or-none spiking nature of biological neurons. It generates a binary output corresponding\n"," to whether each neuron in the input tensor has fired or not.\n"," Parameters:\n"," ctx : torch.autograd.function._ContextMethodMixin\n"," A context object for storing information necessary for the backward computation.\n"," input : torch.Tensor\n"," A tensor containing the input data, typically the neuronal activations in form of the membrane potential,\n"," for which the output firing response will be computed.\n"," Returns:\n"," torch.Tensor: A tensor with the same shape as input, filled with binary values indicating whether\n"," each neuron has fired (1.0) or not (0.0).\n","\n"," @staticmethod\n"," backward(ctx, grad_output):\n"," Computes the backward propagation step in the neural network. This method calculates the surrogate\n"," gradients of the loss function with respect to the input activations. It is designed to work with\n"," the non-differentiable nature of spiking neurons by approximating the gradients.\n"," Parameters:\n"," ctx : torch.autograd.function._ContextMethodMixin\n"," A context object that has the information stashed during the forward pass.\n"," grad_output : torch.Tensor\n"," A tensor containing the gradient of the loss function with respect to the outputs of the forward method.\n"," Returns:\n"," torch.Tensor: A tensor containing the surrogate gradients of the loss function with respect to\n"," the input activations, which can be backpropagated through the rest of the network.\n"," \"\"\"\n"," @staticmethod\n"," def forward(ctx, input):\n"," ctx.save_for_backward(input)\n"," out = torch.zeros_like(input)\n"," out[input > 0] = 1.0\n"," return out\n"," @staticmethod\n"," def backward(ctx, grad_output):\n"," input, = ctx.saved_tensors\n"," # Original SPyTorch/SuperSpike gradient\n"," # This seems to be a typo or error? But it works well\n"," # grad = grad_output/(100*torch.abs(input)+1.0)**2\n"," # Sigmoid\n"," grad = grad_output*beta*torch.sigmoid(beta*input)*(1-torch.sigmoid(beta*input))\n"," return grad\n","\n","spike_fn = SurrGradSpike.apply # allows the defined class to be used as a function."]},{"cell_type":"markdown","id":"911318ee","metadata":{"id":"911318ee"},"source":["#### Creat network architecture and update function\n","\n","The code for the updated model is very similar to the membrane only layer. First, for initialisation we now need two weight matrices, $W_1$ from the input to the hidden layer, and $W_2$ from the hidden layer to the output layer. Second, we run two passes of the loop that you saw above for the membrane only model.\n","\n","The first pass computes the output spikes of the hidden layer. The second pass computes the output layer and is exactly the same as before except using the spikes from the hidden layer instead of the input layer.\n","\n","For the first pass, we modify the function in two ways.\n","\n","Firstly, we compute the spikes with the line ``s = spike_fn(v-1)``. In the forward pass this just computes the Heaviside function of $v-1$, i.e. returns 1 if $v>1$, otherwise 0, which is the spike threshold function for the LIF neuron. In the backwards pass, it returns a gradient of the smoothed version of the Heaviside function.\n","\n","The other line we change is the membrane potential update line. Now, we multiply by $1-s$ where ($s=1$ if there was a spike in the previous time step, otherwise $s=0$), so that the membrane potential is reset to 0 after a spike (but in a differentiable way rather than just setting it to 0)."]},{"cell_type":"code","execution_count":null,"id":"7b072bb5","metadata":{"id":"7b072bb5"},"outputs":[],"source":["num_hidden = 30\n","\n","# Weights and uniform weight initialisation\n","def init_weight_matrices():\n"," # Input to hidden layer\n"," W1 = nn.Parameter(torch.empty((input_size, num_hidden), device=device, dtype=dtype, requires_grad=True))\n"," fan_in, _ = nn.init._calculate_fan_in_and_fan_out(W1)\n"," bound = 1 / np.sqrt(fan_in)\n"," nn.init.uniform_(W1, -bound, bound)\n"," # Hidden layer to output\n"," W2 = nn.Parameter(torch.empty((num_hidden, num_classes), device=device, dtype=dtype, requires_grad=True))\n"," fan_in, _ = nn.init._calculate_fan_in_and_fan_out(W2)\n"," bound = 1 / np.sqrt(fan_in)\n"," nn.init.uniform_(W2, -bound, bound)\n"," return W1, W2\n","\n","# Run the simulation\n","def snn(input_spikes, W1, W2, tau=20*ms):\n"," # First layer: input to hidden\n"," v = torch.zeros((batch_size, num_hidden), device=device, dtype=dtype)\n"," s = torch.zeros((batch_size, num_hidden), device=device, dtype=dtype)\n"," s_rec = [s]\n"," h = torch.einsum(\"abc,cd->abd\", (input_spikes, W1))\n"," alpha = np.exp(-dt/tau)\n"," for t in range(duration_steps - 1):\n"," new_v = (alpha*v + h[:, t, :])*(1-s) # multiply by 0 after a spike\n"," s = spike_fn(v-1) # threshold of 1\n"," v = new_v\n"," s_rec.append(s)\n"," s_rec = torch.stack(s_rec, dim=1)\n"," # Second layer: hidden to output\n"," v = torch.zeros((batch_size, num_classes), device=device, dtype=dtype)\n"," s = torch.zeros((batch_size, num_classes), device=device, dtype=dtype)\n"," v_rec = [v]\n"," h = torch.einsum(\"abc,cd->abd\", (s_rec, W2))\n"," alpha = np.exp(-dt/tau)\n"," for t in range(duration_steps - 1):\n"," # v = alpha * v + torch.where(h[:, t, :] > 0, h[:, t, :], torch.zeros_like(h[:, t, :])) # VB allow only positive inputs to change the membrane pot.\n"," v = alpha*v + h[:, t, :]\n"," v_rec.append(v)\n"," v_rec = torch.stack(v_rec, dim=1)\n"," # Return recorded membrane potential of output\n"," return v_rec"]},{"cell_type":"markdown","metadata":{"id":"8b4fce85"},"source":["### Define function to analyse the simulation results\n","\n","This function computes the training and test accuracy, and plots histograms and confusion matrices to understand the errors it's making."],"id":"8b4fce85"},{"cell_type":"code","execution_count":null,"metadata":{"id":"9cc91c87"},"outputs":[],"source":["def analyse(ipds, spikes, label, run, plot_analysis):\n"," \"\"\"\n"," Analyse the performance of a classifier on interaural phase difference (IPD) data.\n","\n"," This function evaluates the accuracy and error of a classifier by comparing its\n"," output with true IPD values. It computes the mean and standard deviation of the\n"," classifier's accuracy and the absolute error in degrees. Additionally, it can\n"," generate histograms and a confusion matrix to visualize the results.\n","\n"," Parameters:\n"," ipds (array): Array of true IPD values.\n"," spikes (array): Array of spike data corresponding to the IPDs.\n"," label (str): Label for the data, used in plot titles.\n"," run (callable): Function that runs the classifier on a batch of spike data.\n"," plot_analysis (bool): If True, plot histograms and confusion matrix.\n","\n"," Returns:\n"," tuple: Tuple containing mean and standard deviation of classifier accuracy,\n"," and mean and standard deviation of absolute error in degrees.\n"," \"\"\"\n"," accs = []\n"," ipd_true = []\n"," ipd_est = []\n"," confusion = np.zeros((num_classes, num_classes))\n"," for spike_batch, ipd_batch in data_generator(ipds, spikes): #Generate batches of data, iterating over IPDs and spikes in a randomized order.\n"," ipd_batch_orig = ipd_batch\n"," ipd_batch = discretise(ipd_batch)\n"," # run network\n"," output = run(spike_batch)\n"," m = torch.sum(output, 1) # agregration function: here Sum over time dimension\n"," _, am = torch.max(m, 1) # argmax over output units to select the classe with the highest score in the output\n","\n"," # construct confusion matrix\n"," for i, j in zip(ipd_batch.detach().cpu().numpy(), am.detach().cpu().numpy()): # update the confusion matrix\n"," confusion[j, i] += 1\n"," ipd_true.append(ipd_batch_orig) # creates a list of arrays\n"," ipd_est.append(continuise(am.detach().cpu().numpy()))\n","\n"," # calculate accuracy\n"," tmp = np.mean((ipd_batch == am).detach().cpu().numpy()) # compare to labels\n"," accs.append(tmp)\n"," ipd_true = np.hstack(ipd_true) # connetecates the arrays in the list horizontally to create a single flattened array\n"," ipd_est = np.hstack(ipd_est)\n"," abs_errors_deg = abs(ipd_true-ipd_est)*180/np.pi\n","\n"," classifier_accuracy_mean = 100*np.mean(accs) # in percent\n"," classifier_accuracy_std = 100*np.std(accs) # in percent\n"," absolute_error_mean = np.mean(abs_errors_deg) # in degree\n"," absolute_error_std = np.std(abs_errors_deg) # in degree\n","\n"," print()\n"," print(f\"{label} classifier accuracy: {100*np.mean(accs):.1f}%\")\n"," print(f\"{label} absolute error: {np.mean(abs_errors_deg):.1f} deg\")\n","\n"," if plot_analysis:\n"," plt.figure(figsize=(10, 4), dpi=100)\n"," plt.subplot(121)\n"," plt.hist(ipd_true*180/np.pi, bins=num_classes, label='True')\n"," plt.hist(ipd_est*180/np.pi, bins=num_classes, label='Estimated')\n"," plt.xlabel(\"IPD\")\n"," plt.yticks([])\n"," plt.legend(loc='best')\n"," plt.title(label)\n"," plt.subplot(122)\n"," confusion /= np.sum(confusion, axis=0)[np.newaxis, :]\n"," plt.imshow(confusion, interpolation='nearest', aspect='auto', origin='lower', extent=(-90, 90, -90, 90))\n"," plt.xlabel('True IPD')\n"," plt.ylabel('Estimated IPD')\n"," plt.title('Confusion matrix')\n"," plt.tight_layout()\n","\n"," return classifier_accuracy_mean, classifier_accuracy_std, absolute_error_mean, absolute_error_std\n"],"id":"9cc91c87"},{"cell_type":"markdown","id":"0a1662e0","metadata":{"id":"0a1662e0"},"source":["### Do the Training and the Analysing\n","\n","We train it as before, except that we modify the functions to take the two weight matrices into account."]},{"cell_type":"markdown","source":[],"metadata":{"id":"R6YKK_5EdBB9"},"id":"R6YKK_5EdBB9"},{"cell_type":"code","execution_count":null,"id":"a5d558df","metadata":{"id":"a5d558df","colab":{"base_uri":"https://localhost:8080/","height":682},"outputId":"fa115c52-e139-41e9-af4b-bdb09f28ee1f"},"outputs":[{"output_type":"stream","name":"stdout","text":["Want loss for epoch 1 to be about 2.48, multiply m by constant to get this\n","Epoch 1: loss=2.17257\n","Epoch 2: loss=1.38754\n","Epoch 3: loss=1.10757\n","Epoch 4: loss=0.96420\n","Epoch 5: loss=0.84949\n","Epoch 6: loss=0.74436\n","Epoch 7: loss=0.73615\n","Epoch 8: loss=0.64543\n","Epoch 9: loss=0.64422\n","Epoch 10: loss=0.57507\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# TRAINING\n","\n","# Training parameters\n","nb_epochs = 10 # is quick, it won't have converged.\n","# Note: An epoch is one complete pass through the entire training dataset.\n","# During an epoch, the neural network processes every example in the dataset once.\n","# Completing an epoch means that every data point has been used for calculating the loss and updating the model parameters.\n","# Multiple epochs are usually required for the network to converge to an optimal set of parameters.\n","lr = 0.01 # learning rate\n","\n","# Generate the training data\n","ipds, spikes = random_ipd_input_signal(num_samples)\n","\n","# Initialise a weight matrices\n","W1, W2 = init_weight_matrices()\n","\n","# Optimiser and loss function\n","optimizer = torch.optim.Adam([W1, W2], lr=lr)\n","log_softmax_fn = nn.LogSoftmax(dim=1)\n","loss_fn = nn.NLLLoss()\n","\n","print(f\"Want loss for epoch 1 to be about {-np.log(1/num_classes):.2f}, multiply m by constant to get this\")\n","\n","loss_hist = []\n","for e in range(nb_epochs):\n"," local_loss = []\n"," for spike_batch, ipd_batch in data_generator(discretise(ipds), spikes):\n"," # Run network\n"," output = snn(spike_batch, W1, W2)\n","\n"," # Compute cross entropy loss\n"," m = torch.sum(output, 1)*0.01 # Agregation fuction: Sum across time dimension. Note: We want loss for epoch 1 to be about -np.log(1/num_classes), multiply m by a constant to get this\n"," loss = loss_fn(log_softmax_fn(m), ipd_batch)\n"," local_loss.append(loss.item())\n","\n"," # The softmax function transforms the output of a neural network's final layer into a probability\n"," # distribution over multiple classes in such a way that increasing the score of one class\n"," # decreases the probabilities of the other classes. It does this by exponentiating each logit\n"," # and then normalizing these values so that they sum to 1. This is important because it ensures that\n"," # the predicted values for each class sum up to 1.0. This probability distribution allows us to\n"," # interpret the network's output as the likelihood of each class being the correct class.\n"," # Training Objective: The training process aims to increase the probability of the correct class.\n"," # As the model updates its weights to increase the probability (and hence the log probability) of the\n"," # correct class, the softmax function inherently decreases the probabilities of the other classes due\n"," # to the normalization step.\n"," # Using it with the negative log likelihood loss encourages the model to increase the log probability\n"," # of the correct class.\n"," # Interpretability: The softmax function's output can be interpreted as class probabilities, which is\n"," # valuable not only for making predictions but also for understanding the model's confidence in those\n"," # predictions. This can be useful for post-processing or decision-making based on the network's output\n"," # probabilities.\n","\n"," # Update gradients\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n","\n"," loss_hist.append(np.mean(local_loss))\n"," print(\"Epoch %i: loss=%.5f\"%(e+1, np.mean(local_loss)))\n","\n","# Plot the loss function over time\n","plt.plot(loss_hist)\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.tight_layout()\n"]},{"cell_type":"code","source":["# ANALYSIS\n","\n","print(f\"Chance accuracy level: {100*1/num_classes:.1f}%\")\n","run_func = lambda x: snn(x, W1, W2)\n","results_Train = analyse(ipds, spikes, 'Train', run=run_func, plot_analysis=1)\n","ipds_test, spikes_test = random_ipd_input_signal(batch_size*n_testing_batches)\n","results_Train = analyse(ipds_test, spikes_test, 'Test', run=run_func, plot_analysis=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":637},"id":"YYmxcDagJkcw","outputId":"e043b59c-f89d-4c58-bd9b-75decb7c59e2"},"id":"YYmxcDagJkcw","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Chance accuracy level: 8.3%\n","\n","Train classifier accuracy: 80.2%\n","Train absolute error: 5.0 deg\n","\n","Test classifier accuracy: 60.7%\n","Test absolute error: 11.1 deg\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","id":"26e19d66","metadata":{"id":"26e19d66"},"source":["### Now play with it\n","Change systematically some of the parameters and record the performance. You can do this for example by running the following code in a loop:"]},{"cell_type":"code","source":["plot_analysis = 0\n","Envelop_powers = [0, 1, 2, 3, 4, 5, 10, 30, 40, 50, 100]\n","\n","Train_accuracy_mean = []\n","Train_accuracy_std = []\n","Train_abs_error_mean = []\n","Train_abs_error_std = []\n","\n","Test_accuracy_mean = []\n","Test_accuracy_std = []\n","Test_abs_error_mean = []\n","Test_abs_error_std = []\n","\n","results_Train = []\n","results_Test = []\n","\n","#for j in range(3)\n","for i, envelope_power in enumerate(Envelop_powers):\n"," plt.imshow(spikes[0, :, :].T, aspect='auto', interpolation='nearest', cmap=plt.cm.gray_r)\n","\n"," # Training parameters\n"," nb_epochs = 10 # quick, it won't have converged\n"," lr = 0.01 # learning rate\n","\n"," # Generate the training data\n"," ipds, spikes = random_ipd_input_signal(num_samples)\n","\n"," # Initialise a weight matrices\n"," W1, W2 = init_weight_matrices()\n","\n"," # Optimiser and loss function\n"," optimizer = torch.optim.Adam([W1, W2], lr=lr)\n"," log_softmax_fn = nn.LogSoftmax(dim=1)\n"," loss_fn = nn.NLLLoss()\n","\n"," print(f\"Want loss for epoch 1 to be about {-np.log(1/num_classes):.2f}, multiply m by constant to get this\")\n","\n"," loss_hist = []\n"," for e in range(nb_epochs):\n"," local_loss = []\n"," for spike_batch, ipd_batch in data_generator(discretise(ipds), spikes):\n"," # Run network\n"," output = snn(spike_batch, W1, W2)\n"," #output = torch.abs(output)\n"," # Compute cross entropy loss\n"," m = torch.sum(output, 1) * 0.01 # Mean across time dimension\n"," loss = loss_fn(log_softmax_fn(m), ipd_batch)\n"," local_loss.append(loss.item())\n"," # Update gradients\n"," optimizer.zero_grad()\n"," loss.backward()\n"," optimizer.step()\n","\n"," loss_hist.append(np.mean(local_loss))\n"," print(\"Epoch %i: loss=%.5f\"%(e+1, np.mean(local_loss)))\n","\n"," if plot_analysis:\n"," # Plot the loss function over time\n"," plt.plot(loss_hist)\n"," plt.xlabel('Epoch')\n"," plt.ylabel('Loss')\n"," plt.tight_layout()\n","\n","\n"," # Analyse\n"," print(f\"Chance accuracy level: {100*1/num_classes:.1f}%\")\n"," run_func = lambda x: snn(x, W1, W2)\n"," results_Train = analyse(ipds, spikes, 'Train', run=run_func, plot_analysis=0)\n"," ipds_test, spikes_test = random_ipd_input_signal(batch_size*n_testing_batches)\n"," results_Test = analyse(ipds_test, spikes_test, 'Test', run=run_func, plot_analysis=0)\n","\n"," Train_accuracy_mean.append(results_Train[0])\n"," Train_accuracy_std.append(results_Train[1])\n"," Train_abs_error_mean.append(results_Train[2])\n"," Train_abs_error_std.append(results_Train[3])\n","\n"," Test_accuracy_mean.append(results_Test[0])\n"," Test_accuracy_std.append(results_Test[1])\n"," Test_abs_error_mean.append(results_Test[2])\n"," Test_abs_error_std.append(results_Test[3])\n","#\n","plt.figure(figsize=(8, 6))\n","plt.errorbar(Envelop_powers,Train_accuracy_mean, yerr=Train_accuracy_std, label='Training',fmt='o', ecolor='blue', capsize=5)\n","plt.errorbar(Envelop_powers,Test_accuracy_mean, yerr=Test_accuracy_std, label='Test', fmt='o', ecolor='red', capsize=5)\n","#plt.xscale('log')\n","plt.ylim([0,100])\n","plt.xlim([-1,50])\n","plt.xlabel('Envelop Power')\n","plt.ylabel('Accurancy (mean+/-std (%))')\n","plt.legend()\n","\n","plt.figure(figsize=(8, 6))\n","plt.errorbar(Envelop_powers,Train_abs_error_mean, yerr=Train_abs_error_std, label='Training',fmt='o', ecolor='blue', capsize=5)\n","plt.errorbar(Envelop_powers,Test_abs_error_mean, yerr=Test_abs_error_std, label='Test', fmt='o', ecolor='red', capsize=5)\n","#plt.xscale('log')\n","plt.ylim([0,100])\n","plt.xlim([-1,50])\n","plt.xlabel('Envelop Power')\n","plt.ylabel('Abs_error (mean+/-std (deg))')\n","plt.legend()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"yf14Jn81LBu3","outputId":"dee52a27-d030-48ad-b964-8ea37e3a3d9d"},"id":"yf14Jn81LBu3","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Want loss for epoch 1 to be about 2.48, multiply m by constant to get this\n","Epoch 1: loss=2.77004\n","Epoch 2: loss=2.57296\n","Epoch 3: loss=2.56450\n","Epoch 4: loss=2.55602\n","Epoch 5: loss=2.56089\n","Epoch 6: loss=2.58390\n","Epoch 7: loss=2.57072\n","Epoch 8: loss=2.56044\n","Epoch 9: loss=2.54922\n","Epoch 10: loss=2.55242\n","Chance accuracy level: 8.3%\n","\n","Train classifier accuracy: 8.7%\n","Train absolute error: 44.7 deg\n","\n","Test classifier accuracy: 8.4%\n","Test absolute error: 45.5 deg\n","Want loss for epoch 1 to be about 2.48, multiply m by constant to get this\n","Epoch 1: loss=2.61462\n","Epoch 2: loss=1.82795\n","Epoch 3: loss=1.49260\n","Epoch 4: loss=1.28227\n","Epoch 5: loss=1.11968\n","Epoch 6: loss=1.01151\n","Epoch 7: loss=0.93219\n","Epoch 8: loss=0.86270\n","Epoch 9: loss=0.81091\n","Epoch 10: loss=0.77063\n","Chance accuracy level: 8.3%\n","\n","Train classifier accuracy: 78.0%\n","Train absolute error: 5.1 deg\n","\n","Test classifier accuracy: 74.0%\n","Test absolute error: 5.6 deg\n","Want loss for epoch 1 to be about 2.48, multiply m by constant to get this\n","Epoch 1: loss=2.15496\n","Epoch 2: loss=1.54471\n","Epoch 3: loss=1.26991\n","Epoch 4: loss=1.08247\n","Epoch 5: loss=0.95155\n","Epoch 6: loss=0.88683\n","Epoch 7: loss=0.81155\n","Epoch 8: loss=0.75849\n","Epoch 9: loss=0.70110\n","Epoch 10: loss=0.69270\n","Chance accuracy level: 8.3%\n","\n","Train classifier accuracy: 70.7%\n","Train absolute error: 5.8 deg\n","\n","Test classifier accuracy: 69.2%\n","Test absolute error: 5.9 deg\n","Want loss for epoch 1 to be about 2.48, multiply m by constant to get this\n","Epoch 1: loss=2.34375\n","Epoch 2: loss=1.60742\n","Epoch 3: loss=1.33732\n","Epoch 4: loss=1.17774\n","Epoch 5: loss=1.03960\n","Epoch 6: loss=0.96242\n","Epoch 7: loss=0.88544\n","Epoch 8: loss=0.81449\n","Epoch 9: loss=0.80581\n","Epoch 10: loss=0.74611\n","Chance accuracy level: 8.3%\n","\n","Train classifier accuracy: 79.3%\n","Train absolute error: 4.9 deg\n","\n","Test classifier accuracy: 68.8%\n","Test absolute error: 5.9 deg\n","Want loss for epoch 1 to be about 2.48, multiply m by constant to get this\n","Epoch 1: loss=2.11549\n","Epoch 2: loss=1.28715\n","Epoch 3: loss=1.00168\n","Epoch 4: loss=0.84978\n","Epoch 5: loss=0.76968\n","Epoch 6: loss=0.68437\n","Epoch 7: loss=0.64780\n","Epoch 8: loss=0.60212\n","Epoch 9: loss=0.57376\n","Epoch 10: loss=0.51894\n","Chance accuracy level: 8.3%\n","\n","Train classifier accuracy: 84.6%\n","Train absolute error: 4.5 deg\n","\n","Test classifier accuracy: 56.7%\n","Test absolute error: 7.6 deg\n","Want loss for epoch 1 to be about 2.48, multiply m by constant to get this\n","Epoch 1: loss=1.99632\n","Epoch 2: loss=1.21324\n","Epoch 3: loss=0.96305\n","Epoch 4: loss=0.83350\n","Epoch 5: loss=0.75726\n","Epoch 6: loss=0.67166\n","Epoch 7: loss=0.61378\n","Epoch 8: loss=0.59108\n","Epoch 9: loss=0.54676\n","Epoch 10: loss=0.51395\n","Chance accuracy level: 8.3%\n","\n","Train classifier accuracy: 82.9%\n","Train absolute error: 4.7 deg\n","\n","Test classifier accuracy: 47.3%\n","Test absolute error: 11.4 deg\n","Want loss for epoch 1 to be about 2.48, multiply m by constant to get this\n","Epoch 1: loss=1.84569\n","Epoch 2: loss=1.03422\n","Epoch 3: loss=0.79383\n","Epoch 4: loss=0.64682\n","Epoch 5: loss=0.57783\n","Epoch 6: loss=0.53457\n","Epoch 7: loss=0.48613\n","Epoch 8: loss=0.44701\n","Epoch 9: loss=0.43875\n","Epoch 10: loss=0.40391\n","Chance accuracy level: 8.3%\n","\n","Train classifier accuracy: 85.8%\n","Train absolute error: 4.4 deg\n","\n","Test classifier accuracy: 46.7%\n","Test absolute error: 39.5 deg\n","Want loss for epoch 1 to be about 2.48, multiply m by constant to get this\n","Epoch 1: loss=1.78420\n","Epoch 2: loss=1.08667\n","Epoch 3: loss=0.84305\n","Epoch 4: loss=0.71055\n","Epoch 5: loss=0.63383\n","Epoch 6: loss=0.56749\n","Epoch 7: loss=0.53407\n","Epoch 8: loss=0.50717\n","Epoch 9: loss=0.46092\n","Epoch 10: loss=0.44702\n","Chance accuracy level: 8.3%\n","\n","Train classifier accuracy: 84.3%\n","Train absolute error: 4.6 deg\n","\n","Test classifier accuracy: 62.4%\n","Test absolute error: 22.8 deg\n","Want loss for epoch 1 to be about 2.48, multiply m by constant to get this\n","Epoch 1: loss=1.52320\n","Epoch 2: loss=0.76583\n","Epoch 3: loss=0.55913\n","Epoch 4: loss=0.44230\n","Epoch 5: loss=0.35985\n","Epoch 6: loss=0.31028\n","Epoch 7: loss=0.29018\n","Epoch 8: loss=0.25092\n","Epoch 9: loss=0.24675\n","Epoch 10: loss=0.23128\n","Chance accuracy level: 8.3%\n","\n","Train classifier accuracy: 93.6%\n","Train absolute error: 4.0 deg\n","\n","Test classifier accuracy: 59.1%\n","Test absolute error: 10.5 deg\n","Want loss for epoch 1 to be about 2.48, multiply m by constant to get this\n","Epoch 1: loss=1.66361\n","Epoch 2: loss=0.84902\n","Epoch 3: loss=0.61970\n","Epoch 4: loss=0.49541\n","Epoch 5: loss=0.42454\n","Epoch 6: loss=0.36200\n","Epoch 7: loss=0.33508\n","Epoch 8: loss=0.31719\n","Epoch 9: loss=0.30413\n","Epoch 10: loss=0.24375\n","Chance accuracy level: 8.3%\n","\n","Train classifier accuracy: 93.3%\n","Train absolute error: 4.0 deg\n","\n","Test classifier accuracy: 61.0%\n","Test absolute error: 11.7 deg\n","Want loss for epoch 1 to be about 2.48, multiply m by constant to get this\n","Epoch 1: loss=1.71752\n","Epoch 2: loss=0.92687\n","Epoch 3: loss=0.65282\n","Epoch 4: loss=0.52519\n","Epoch 5: loss=0.45046\n","Epoch 6: loss=0.39243\n","Epoch 7: loss=0.34650\n","Epoch 8: loss=0.31953\n","Epoch 9: loss=0.31076\n","Epoch 10: loss=0.28762\n","Chance accuracy level: 8.3%\n","\n","Train classifier accuracy: 92.0%\n","Train absolute error: 4.1 deg\n","\n","Test classifier accuracy: 49.6%\n","Test absolute error: 46.5 deg\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":63},{"output_type":"display_data","data":{"text/plain":["
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Cwy9bP5+52yI57e//a1efvllff7zn7/gcTU1NZKk9vb2XBUFAAD4TM4Walu1apXKy8t1yy23XPC4trY2SdKYMWNyUg4mwgGgLgP+k5MGSm9vr1atWqXGxkYNH/7/P+LQoUNau3atZs+erbKyMu3bt09Lly7VjTfeqEmTJuWiKAAAwIdy0kB5+eWXdeTIEd15551p+wsLC/Xyyy/ru9/9rnp6ehSLxdTQ0KCvfvWruSiGpKFZzpqeGGA2JrwD/pPTSbK54nSSrJMGig//uQBkQAMFGHrZ/P3mYYHvsdvDYuY/YDYnHRAaLYA5eFggAAAwTl4mKE56RPSmALPxOAogGEhQAACAcfIyQcmEx68DweVkjhmpCuANEhQAAGCcvExQ3E5L6HUBAOCuvGygZMLEOiC4nNRlOiCANxjiAQAAxiFBeY/dHhapChBcTJYHzEGCAgAAjEOC4gLSEsBszC0B/IcEBQAAGCcvExQnvSnGqAGzkYwAwUCCAgAAjJOXCYrbd+wAyD0n9ZFUBfAfEhQAAGCcvExQmNEP+I/bc8Ko84DZAtVAcXuiKxcwwGx26yPDtYD/MMQDAACME6gEZShuHyZVAczhJBmh3gJmI0EBAADGCVSCkonbvSR6XQAA5B4JCgAAME7gExS7Y9Qsaw+Yzcn8r0yfZT4ZYDYSFAAAYJzAJyh2kZYA/uMkBSEtAcxGggIAAIwT+ATFydwSelhAMFCXAf8hQQEAAMYJfILiBLP8Af+h3gLB4OsGSiQSSXvt9q3CXNSAYOA6APgPQzwAAMA4vm6gxONxWZaV2gAE15/XdTfqvNvfB8Bdvm6gAACAYMq6gbJjxw7NmTNH0WhUoVBIGzZsSHvfsiw9/PDDGjNmjEaOHKm6ujodPHgw7ZgTJ05o/vz5Ki4uVklJiRYsWKDu7m5Hv0g27PacQqFQvw2A/9it86QqgDmybqD09PRo8uTJWrlyZcb3v/GNb+jJJ5/UM888o507d+pDH/qQZs6cqXfffTd1zPz58/XGG29o8+bN2rhxo3bs2KG77rpr8L8FAAAIlJDloIsQCoW0fv16zZ07V9Kfeh/RaFRf+tKX9OUvf1nSn+aJVFRUaPXq1Zo3b57efPNNXXXVVdq1a5emTp0qSdq0aZNmz56tt99+W9FotN/PSSaTSiaTqdeJREKxWEzxeFzFxcWDLf6g2L2FkVsdAbO5XZep88AHSyQSikQitv5+uzoH5fDhw+rs7FRdXV1qXyQSUU1NjVpbWyVJra2tKikpSTVOJKmurk4FBQXauXNnxu9taWlRJBJJbbFYzM1iAwAAw7jaQOns7JQkVVRUpO2vqKhIvdfZ2any8vK094cPH67S0tLUMX01NzcrHo+ntqNHj7pZ7Kwwlg3kl0x1OdP8NLvHAbDHFwu1hcNhhcNhr4sBAACGiKsJSmVlpSSpq6srbX9XV1fqvcrKSh0/fjzt/XPnzunEiROpY/wmUy/J7gbAG0NxNx9JKjB4rjZQqqurVVlZqS1btqT2JRIJ7dy5U7W1tZKk2tpanTx5Unv27Ekds3XrVvX29qqmpsbN4gAAAJ/Keoinu7tb7e3tqdeHDx9WW1ubSktLVVVVpSVLlujrX/+6PvKRj6i6uloPPfSQotFo6k6fK6+8UrNmzdLChQv1zDPP6OzZs1q8eLHmzZuX8Q4e05B6AP7j5A4bJ6kHd/YAg5d1A2X37t2aPn166nVTU5MkqbGxUatXr9YDDzygnp4e3XXXXTp58qRuuOEGbdq0SSNGjEh9Zs2aNVq8eLFmzJihgoICNTQ06Mknn3Th18k9uxcXLkyAObxqZFDngcFztA6KV7K5j9orNFCAYKAuA+7J5u+3L+7iMYmTIR4udAAA2MPDAgEAgHFIUHKEZAQwG2koYDYSFAAAYBwSlAuw28PiwWGA/wzFHXlcB4DBI0EBAADGIUG5ACc9HXpJQH4hLQHcRYICAACMQ4JyAcxBAfzHbt1ze24JAHfRQHEBjRHAHHYbI5kMReMGgD0M8QAAAOOQoFwATzEFgsHtoRvqMpB7JCgAAMA4JCgA8B7mmwDmIEEBAADGCXyCMhTLVNPDAsxGHQX8hwQFAAAYJ/AJylCkJQDM5iT55MGAgDdIUAAAgHECn6DY5XZPh94U4A23H1Fh97PUb8BdJCgAAMA4JCjvcXsFSeaqAOawW29JQQBz0EDJktsT6wCYYyiWJQBgD0M8AADAOCQo7+GWYiAY3B6GJTUFvEGCAgAAjEOCkiWWugfM5nbySV0GvEGCAgAAjEOCkiXSEsBsTlJO5p0B5iBBAQAAxiFBeQ89LCAYnNRR5pgB5iBBAQAAxiFBuQC310kAYA63H28BwF0kKAAAwDh5maCwTgKQX6ijgP9knaDs2LFDc+bMUTQaVSgU0oYNG1LvnT17VsuWLdPVV1+tD33oQ4pGo/rHf/xHHTt2LO07xo0bp1AolLatWLHC8S9jl2VZrm59fxcm0gLecbuOUr8Bb2TdQOnp6dHkyZO1cuXKfu+dPn1ae/fu1UMPPaS9e/fqhRde0IEDB/SpT32q37GPPfaYOjo6Utu99947uN8AAAAETtZDPPX19aqvr8/4XiQS0ebNm9P2ff/739e0adN05MgRVVVVpfYXFRWpsrIy2x+fM04mzBEfA+bgIYBAMOR8kmw8HlcoFFJJSUna/hUrVqisrEzXXHONnnjiCZ07d27A70gmk0okEmkbAAAIrpxOkn333Xe1bNky3XbbbSouLk7tv++++3TttdeqtLRUP//5z9Xc3KyOjg59+9vfzvg9LS0tWr58eS6L6qiXxG3GgDmcLLBIXQbMEbIc1L5QKKT169dr7ty5/d47e/asGhoa9Pbbb2vbtm1pDZS+nn32WX3hC19Qd3e3wuFwv/eTyaSSyWTqdSKRUCwWUzwev+D3DhUuaoDZ3F5dFsDgJBIJRSIRW3+/c5KgnD17Vn//93+v3/72t9q6desHFqKmpkbnzp3Tb37zG11xxRX93g+HwxkbLoPlZFl7xqgBc9DwAILL9QbK+42TgwcP6pVXXlFZWdkHfqatrU0FBQUqLy93uzgAAMCHsm6gdHd3q729PfX68OHDamtrU2lpqcaMGaO/+7u/0969e7Vx40adP39enZ2dkqTS0lIVFhaqtbVVO3fu1PTp01VUVKTW1lYtXbpUt99+uy655BL3frMLcPtx7PTEAHPYrbfUZcBsWc9B2bZtm6ZPn95vf2Njox599FFVV1dn/Nwrr7yim266SXv37tUXv/hFvfXWW0omk6qurtbnPvc5NTU12R7GyWYMyy4aKID/uP0UcuoykFvZ/P12NEnWK04bKFysAHAdAIae55Nk/Yi0BDCbk/rodtICIPd4mjEAADBO4BMUFmgCgmEoUk7qPGAOEhQAAGCcwCcoTsaoAXjDyQRWJ3WZJBUwBwkKAAAwTqASFJa9BoLB7XkkLNQG+A8JCgAAME6gEhQn803oTQHBxZongP+QoAAAAOMEKkGxy+0xatIXwBusEAsEFwkKAAAwTuATFLfXUyAtAczBk4uB4Ap8A8VJw8Pu9wHIPbeHaehsAGZjiAcAABjH1wlKJBJx7bvoOQHmcHuCOmkJ4D8kKAAAwDi+TlDi8biKi4tTr92eEEsPC/CG3brnZJIsdR4wGwkKAAAwjq8TlL7o/QAAEAwkKAAAwDiBSlAycbJ2AmPUgNlYvwgILhIUAABgnMAnKPScgPxCnQeCgQQFAAAYJ/AJil3MNwHMxqqxQH4JfAOFSXRAcLEoGxBcDPEAAADj+DpB6fuwQHpEADLh2gD4DwkKAAAwjq8TlL4YZwaQCdcGwH9IUAAAgHF8naDE43EVFxenXjtZ1h6AOZzUZZIRIBhIUAAAgHF8naD0ZbfnxHg0YDa7C7BlQv0GgiHrBGXHjh2aM2eOotGoQqGQNmzYkPb+HXfcoVAolLbNmjUr7ZgTJ05o/vz5Ki4uVklJiRYsWKDu7m5HvwgAAAiOrBsoPT09mjx5slauXDngMbNmzVJHR0dq+8lPfpL2/vz58/XGG29o8+bN2rhxo3bs2KG77ror+9L30bdhNNAGwGx2661lWf02AMGQ9RBPfX296uvrL3hMOBxWZWVlxvfefPNNbdq0Sbt27dLUqVMlSU899ZRmz56tb37zm4pGo9kW6YKcRMUAcs/t+sjzeYBgyMkk2W3btqm8vFxXXHGF7rnnHr3zzjup91pbW1VSUpJqnEhSXV2dCgoKtHPnzozfl0wmlUgk0jYAABBcrk+SnTVrlm699VZVV1fr0KFD+spXvqL6+nq1trZq2LBh6uzsVHl5eXohhg9XaWmpOjs7M35nS0uLli9f3m//YJe6p+cEmM3tOkqdB7zhJCF1vYEyb9681H9fffXVmjRpksaPH69t27ZpxowZg/rO5uZmNTU1pV4nEgnFYjHHZQUAAGbK+Tool19+uUaPHq329nZJUmVlpY4fP552zLlz53TixIkB562Ew2EVFxenbQCCIdNEV7uTZJkYD5gjU93rW7fj8bjt78t5A+Xtt9/WO++8ozFjxkiSamtrdfLkSe3Zsyd1zNatW9Xb26uamppcFwcAAPhA1kM83d3dqTREkg4fPqy2tjaVlpaqtLRUy5cvV0NDgyorK3Xo0CE98MAD+vCHP6yZM2dKkq688krNmjVLCxcu1DPPPKOzZ89q8eLFmjdvXtZ38PRd6j4Tu7P3meUPmIO77wD/cftvZsjK8hu3bdum6dOn99vf2Niop59+WnPnztX//M//6OTJk4pGo/rkJz+pr33ta6qoqEgde+LECS1evFgvvfSSCgoK1NDQoCeffFIXX3yxrTIkEglFIhEaKEAe4fk8gP9l9fc72waKCQb6BWlkAMHgdl3m2gB4Y6COhZ0GCg8LBAAAxgn8wwLpOQH+42QOSqbPUucBb/Ste++PgNhBggIAAIzj6wTFbisMgLncnvxKagqYw0n99nWCEo/HMy7yxJNNAX9zUpe5DgDB4OsGCgAACCaGeAAYh0XZgGBgkiwAAAgUXycog11JFoA5mCMCIBMSFAAAYBxfJyh9x7HoiQHBwHN3AP9xe8SCBAUAABjH1wlKX8w3AcxGHQWCy056yV08AADA1wKVoDD2DJjD7pLzbh8HwBtu11ESFAAAYJxAJSiZ0OsCvGE3BcmEegv4j9t11NcNlL4LtXFRA/zHSUMGgDm4zRgAAAQeDRQAAGAcGigAAMA4vp6D0he3JgJms1v3qKOA/7g9n4wEBQAAGCdQCQppCeA/PBgQQCYkKAAAwDiBSlDoTQH+Q70FzOZkdKLvcTwsEAAA+FqgEhQA5mBuCRAMduuj2/NAfd1AsRMTcaEDzMHzeQD/sVv3uM0YAAAEnq8TlL4PCwTgDbs9LB4CCPiPV3WZBAUAABjH1wlKX4xRA8FFXQbMMdj6yG3GAADA13ydoPRthdHDAvyHegsEg9vzUrJOUHbs2KE5c+YoGo0qFAppw4YNae+HQqGM2xNPPJE6Zty4cf3eX7FiheNfBgAABEPWCUpPT48mT56sO++8U7feemu/9zs6OtJe/+xnP9OCBQvU0NCQtv+xxx7TwoULU6+LioqyLQoAQ7C+CQC37/bJuoFSX1+v+vr6Ad+vrKxMe/3iiy9q+vTpuvzyy9P2FxUV9TsWAABAyvEk2a6uLv3nf/6nFixY0O+9FStWqKysTNdcc42eeOIJnTt3bsDvSSaTSiQSaZv0p3VQLMtKbQDM9uf19UIbAP/JNL3DiZxOkv33f/93FRUV9RsKuu+++3TttdeqtLRUP//5z9Xc3KyOjg59+9vfzvg9LS0tWr58eS6LCgAADBKyHHRXQqGQ1q9fr7lz52Z8f8KECbr55pv11FNPXfB7nn32WX3hC19Qd3e3wuFwv/eTyaSSyWTqdSKRUCwWYyVZwBBuryRLigKYbbB1+f11UOz8/c5ZgvLf//3fOnDggH76059+4LE1NTU6d+6cfvOb3+iKK67o9344HM7YcAFgBrsNChoeQHD55mGBP/rRjzRlyhRNnjz5A49ta2tTQUGBysvLc1UcAADgI1knKN3d3Wpvb0+9Pnz4sNra2lRaWqqqqipJf4pwnn/+eX3rW9/q9/nW1lbt3LlT06dPV1FRkVpbW7V06VLdfvvtuuSSSxz8KgCGgtuLMZGqAMHg9rUh6wbK7t27NX369NTrpqYmSVJjY6NWr14tSVq3bp0sy9Jtt93W7/PhcFjr1q3To48+qmQyqerqai1dujT1PQAAAI4myXploIcN+fBXAQAgEOxMls9mkiwPCwQAAMbx9cMC+7bAWDIbMAf1EYCRd/EAAAAMlq8TlL7s3oNNLw7IPeoZEAxuLrA40BzSTEhQAACAcQKVoGRCLw7wBuklEAxurxBrFwkKAAAwTuATFACD53YK4uT7SGQAcwxF3aOBAmBAXjUAaIwA5hiK4ZxMGOIBAADGIUEB4JjdxMPN2xUBeMfJ0KxdJCgAAMA4JCgAcsJJqkKCApjDSb0d6GGBdpCgAAAA45CgAHCM+SZAcNmty26noSQoAADAOL5OUPqOY9ETA3KPZAQIBrcTD7frPAkKAAAwjq8TlHg8ruLiYq+LAQQWd9gAwWW3LntV533dQAGQW24/d8ftnwHAXW4vuugEQzwAAMA4JCgAsuJk2IeF2gCz5bqOslAbAADwNV8nKNxmDJjB7VQFgP+wUBsAAAg8Xyco3GYMmIu5JYDZnNyJw108AAAgL/k6QQGQW6xlAuQXr9Y8yYQEBQAAGIcEBcCAnPSmmIMCmM30dYlIUAAAgHFIUADkBGkJ4D8mpSo0UABIcnYRMikWBtCfHye8M8QDAACMQ4IC5CG3H6luUiwM5LuhSEOHIpEhQQEAAMYhQQHykNsP8vNqIScA/TlJLUxKOX3ZQHn/HzCRSHhcEgDZoM4C+aVvnX//tZ2GkC8bKKdOnZIkxWIxj0sCIBuRSMTrIgAYQgPV+VOnTn3g9SBkmZTn2NTb26tjx46pqKhIp06dUiwW09GjR3mysccSiQTnwhCcC3NwLszBufCeZVk6deqUotGoCgouPA3WlwlKQUGBLrvsMkn/P/ZdXFzM/3CG4FyYg3NhDs6FOTgX3rKbpHIXDwAAMA4NFAAAYBzfN1DC4bAeeeQRhcNhr4uS9zgX5uBcmINzYQ7Ohb/4cpIsAAAINt8nKAAAIHhooAAAAOPQQAEAAMahgQIAAIxDAwUAABjH1w2UlStXaty4cRoxYoRqamr0+uuve12kwGtpadF1112noqIilZeXa+7cuTpw4EDaMe+++64WLVqksrIyXXzxxWpoaFBXV5dHJc4fK1asUCgU0pIlS1L7OBdD53e/+51uv/12lZWVaeTIkbr66qu1e/fu1PuWZenhhx/WmDFjNHLkSNXV1engwYMelji4zp8/r4ceekjV1dUaOXKkxo8fr6997WtpD6jjfPiA5VPr1q2zCgsLrWeffdZ64403rIULF1olJSVWV1eX10ULtJkzZ1qrVq2y9u/fb7W1tVmzZ8+2qqqqrO7u7tQxd999txWLxawtW7ZYu3fvtj72sY9ZH//4xz0sdfC9/vrr1rhx46xJkyZZ999/f2o/52JonDhxwho7dqx1xx13WDt37rR+/etfW//1X/9ltbe3p45ZsWKFFYlErA0bNli/+MUvrE996lNWdXW19cc//tHDkgfT448/bpWVlVkbN260Dh8+bD3//PPWxRdfbH3ve99LHcP5MJ9vGyjTpk2zFi1alHp9/vx5KxqNWi0tLR6WKv8cP37ckmRt377dsizLOnnypHXRRRdZzz//fOqYN99805Jktba2elXMQDt16pT1kY98xNq8ebP1iU98ItVA4VwMnWXLllk33HDDgO/39vZalZWV1hNPPJHad/LkSSscDls/+clPhqKIeeWWW26x7rzzzrR9t956qzV//nzLsjgffuHLIZ4zZ85oz549qqurS+0rKChQXV2dWltbPSxZ/onH45Kk0tJSSdKePXt09uzZtHMzYcIEVVVVcW5yZNGiRbrlllvS/s0lzsVQ+o//+A9NnTpVn/nMZ1ReXq5rrrlG//Zv/5Z6//Dhw+rs7Ew7F5FIRDU1NZyLHPj4xz+uLVu26Fe/+pUk6Re/+IVeffVV1dfXS+J8+IUvn2b8hz/8QefPn1dFRUXa/oqKCr311lselSr/9Pb2asmSJbr++us1ceJESVJnZ6cKCwtVUlKSdmxFRYU6Ozs9KGWwrVu3Tnv37tWuXbv6vce5GDq//vWv9fTTT6upqUlf+cpXtGvXLt13330qLCxUY2Nj6t870zWLc+G+Bx98UIlEQhMmTNCwYcN0/vx5Pf7445o/f74kcT58wpcNFJhh0aJF2r9/v1599VWvi5KXjh49qvvvv1+bN2/WiBEjvC5OXuvt7dXUqVP1r//6r5Kka665Rvv379czzzyjxsZGj0uXf5577jmtWbNGa9eu1Uc/+lG1tbVpyZIlikajnA8f8eUQz+jRozVs2LB+dyN0dXWpsrLSo1Lll8WLF2vjxo165ZVXdNlll6X2V1ZW6syZMzp58mTa8Zwb9+3Zs0fHjx/Xtddeq+HDh2v48OHavn27nnzySQ0fPlwVFRWciyEyZswYXXXVVWn7rrzySh05ckSSUv/eXLOGxj//8z/rwQcf1Lx583T11Vfrc5/7nJYuXaqWlhZJnA+/8GUDpbCwUFOmTNGWLVtS+3p7e7VlyxbV1tZ6WLLgsyxLixcv1vr167V161ZVV1envT9lyhRddNFFaefmwIEDOnLkCOfGZTNmzND//u//qq2tLbVNnTpV8+fPT/0352JoXH/99f1ut//Vr36lsWPHSpKqq6tVWVmZdi4SiYR27tzJuciB06dPq6Ag/c/bsGHD1NvbK4nz4Rtez9IdrHXr1lnhcNhavXq19ctf/tK66667rJKSEquzs9ProgXaPffcY0UiEWvbtm1WR0dHajt9+nTqmLvvvtuqqqqytm7dau3evduqra21amtrPSx1/vjzu3gsi3MxVF5//XVr+PDh1uOPP24dPHjQWrNmjTVq1Cjrxz/+ceqYFStWWCUlJdaLL75o7du3z/rbv/1bbmvNkcbGRusv/uIvUrcZv/DCC9bo0aOtBx54IHUM58N8vm2gWJZlPfXUU1ZVVZVVWFhoTZs2zXrttde8LlLgScq4rVq1KnXMH//4R+uLX/yidckll1ijRo2yPv3pT1sdHR3eFTqP9G2gcC6GzksvvWRNnDjRCofD1oQJE6wf/vCHae/39vZaDz30kFVRUWGFw2FrxowZ1oEDBzwqbbAlEgnr/vvvt6qqqqwRI0ZYl19+ufUv//IvVjKZTB3D+TBfyLL+bGk9AAAAA/hyDgoAAAg2GigAAMA4NFAAAIBxaKAAAADj0EABAADGoYECAACMQwMFAAAYhwYKAAAwDg0UAABgHBooAADAODRQAACAcf4PXO9KPwfdjdIAAAAASUVORK5CYII=\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["# save pipe.pkl to output data folder\n","!cp SoundLocalization_SNN_VB.ipynb /content/drive/MyDrive/Mini-Project_ColabNotebooks\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tZ_SqPO-jWLb","outputId":"bb6ec6ae-a9cb-47f7-d45c-862cf00223fe"},"id":"tZ_SqPO-jWLb","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["cp: cannot stat 'SoundLocalization_SNN_VB.ipynb': No such file or directory\n"]}]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.10"},"colab":{"provenance":[{"file_id":"1-43unpBGK093z1hZjSnobrKl4P66W72j","timestamp":1706243181322}]}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Context\n", + "This tutorial on Spiking Neural Networks (SNNs), applied to a sound localization task, is an extensively annotated version of [the excellent tutorial created by Dan Goodman for the 2022 Cosyne Conference](https://neural-reckoning.github.io/cosyne-tutorial-2022/).\n", + "\n", + "Expanding on the original Cosyne tutorial, I have developed this version specifically for a computational project as paort of a neuroscience lecture at the master’s level in Paris that I started together with [Marcus Ghosh](https://neural-reckoning.org/marcus_ghosh.html). While the core code remains unchanged, this version includes additional annotations, additional detailed explanations, and additional plotting to improve accessibility and enhance understanding for students.\n", + "\n", + "As a starting point, I highly recommend watching the comprehensive video lecture delivered by Dan Goodman during the Cosyne workshop. [Watch this video lecture on YouTube.](https://www.youtube.com/watch?v=GTXTQ_sOxak)


\n", + "\n", + "\n", + "\n", + "[Volker Bormuth](https://www.labojeanperrin.fr/?article6) \n", + "Assistant Professor \n", + "Laboratoire Jean Perrin \n", + "Sorbonne Universiy \n", + "Paris, France " + ], + "metadata": { + "id": "YI_z3RB52moV" + }, + "id": "YI_z3RB52moV" + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "---\n", + "# Introduction\n", + "---\n", + "---\n", + "\n", + "Animals localise sounds by detecting location- or direction-specific cues in the signals that arrive at their ears. Some of the most important sources of cues (although not the only ones) come from differences in the signals between two ears, including both level and timing differences. Respectively, termed interaural level difference (ILD) and interaural timing difference (ITD). In some cases humans are able to detect arrival time differences as small as 20 μs.\n", + "\n", + "The classic model of ITD sensitivity is the delay line model of Jeffress ([1948](https://psycnet.apa.org/doiLanding?doi=10.1037%2Fh0061495) ) in which an array of binaural coincidence detector neurons receive inputs from the two ears with different delays. When a neurons’ delays exactly match the acoustic delays induced by the sound location, it will be maximally active. Therefore, the identity of the most active neuron indicates the direction of the sound.\n", + "\n", + "\n", + "\n" + ], + "metadata": { + "id": "40c0tOD7tCKm" + }, + "id": "40c0tOD7tCKm" + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "\n", + "![Screenshot 2024-11-18 at 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WERpbWVuc2lvbj4KICAgICAgICAgPGV4aWY6VXNlckNvbW1lbnQ+U2NyZWVuc2hvdDwvZXhpZjpVc2VyQ29tbWVudD4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24+CiAgIDwvcmRmOlJERj4KPC94OnhtcG1ldGE+CpDQOkgAAAAcaURPVAAAAAIAAAAAAAABfgAAACgAAAF+AAABfgACCi0XZqnyAABAAElEQVR4AezdCbxV0///8VWJUipNMoVEIaWvzEMIkSlCSiRkyEyGkln4ypi5TMmUoUSkRAMNpkwhpYiSNJtF9n+/1/+77+/cc9Y+0z3nnum1Ho97zzn77L322s99zr73sT77s1YVzy+GggACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACORGoQsAuJ+7sFAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAErQMCODwICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACORQgYJdDfHaNAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAE7PgMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAII5FCAgF0O8dk1AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgTs+AwggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkEMBAnY5xGfXCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCBCw4zOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQA4FCNjlEJ9dI4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIEDAjs8AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAjkUIGCXQ3x2jQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAABOz4DCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCORQgIBdDvHZNQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIE7PgMIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIJBDAQJ2OcRn1wgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggQsOMzgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEAOBQjY5RCfXSOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBAwI7PAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAI5FCBgl0N8do0AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAATs+AwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjkUICAXQ7x2TUCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACBOz4DCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQQwECdjnEZ9cIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIELDjM4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBADgUI2OUQn10jgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggQMCOzwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACORQgYJdDfHaNAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAE7PgMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAII5FCAgJ2P/+WXX5rvv//e/PTTT2bJkiX2sUaNGqZx48ZlP5tvvrlp1qxZDk8Vu0YAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEChGgZIM2P32229mwoQJ5tVXXzWvvfaaWbRoUVLnVgG7ww47zP7st99+Zr311ktqO1ZCIBDo27evmThxYvDSPrZq1coMGzas3LJsvJg5c6bp3bt3TNUPPfSQadeuXcxyFuSvwNChQ82DDz4Y08APP/wwZlnkgvHjx5t+/fpFLrLPX3zxRbPlllvGLGdB5gWWL19uDj744JiK+/fvb7p06RKznAUIFJrAzTffbF544YVyzW7QoIHR9YeCAAIIIIAAAggggAACCCCAAAIIIBAuUFIBu7Vr1xoFJ6666iqzYsWKcJUk3qlfv765/vrrzVlnnWWqVauWxBasgoAxRx99tHnppZfKUey6667m3XffLbcsGy8mT55sFGiOLm+99ZbZf//9oxfzOo8FrrvuOnPttdfGtPDff/81VapUiVkeLBgxYoQ54YQTgpdlj8oybtmyZdlrniQvoL8r999/v+nWrZtp2LBhwg2Vxd2kSZOY9R544AH79yTmDRbkTGDcuHGmZs2aZt99981ZGwpxx2effXbMDQUbbbSR+fHHHwvxcGgzAggggAACCCCAAAIIIIAAAgggUGkCJROwmzRpkjnvvPPMrFmzMorbpk0bM3jwYDr0MqpavJURsCvec1uZR0bArjK1w/c1bdo0c84555iPP/7YZmpvsskm4Sv/7x0CdgmJcr6Chsi+6KKLjDJPn3rqKdO9e/ect6mQGkDArpDOFm1FAAEEEEAAAQQQQAABBBBAAIF8EiiJgN2YMWNsZtM///yTNftHH33U9OrVK2v1U3FxCBCwK47zmOujIGCX2zOwdOlSc/nll5vHH3/ceJ5nG6OhlQnY5fa8VHTvf//9t7njjjvMDTfcYDR0tgoBu9RVCdilbsYWCCCAAAIIIIAAAggggAACCCCAgASKPmA3depUc9BBB5k//vgj4RnXMJebbrqp/dHK3333nVmwYEFZx128CjQs5qhRo8wRRxwRbzXeK3EBAnYl/gHI0OETsMsQZBrVzJgxw3Tq1MmsXLmy3NYE7MpxFNyL1atXm913393Mnj27XNsJ2JXjSOoFAbukmFgJAQQQQAABBBBAAAEEEEAAAQQQiBEo6oDdDz/8YFq1ahXTsRoo1KpVy5x++ummd+/eplmzZnaumuC9yMfly5ebr776ytx11112iCzNE+UqmutG84Gp04+CgEuAgJ1LhWWpChCwS1Usc+sPHz7cnHzyyTEVErCLISmoBRoGs2nTpjFtJmAXQ5JwAQG7hESsgAACCCCAAAIIIIAAAggggAACCDgFijpgd8kll9jhraKPvGrVqmbAgAHmggsuMMqqS6V8/vnnRp3lzz//vHMzBeumT5/ufI+FCBCw4zOQCYF0A3affvqpefbZZ2OaoPm6GjVqFLOcBbECFQ3Y/frrr+amm26Kqbhz585m1113jVnOgsoRIGCXOWcCdpmzpCYEEEAAAQQQQAABBBBAAAEEECgtgaIN2K1YscJsscUWRp2j0eXWW281l156afTilF737NnTPPHEE85tlGW3//77O99jYWkLELAr7fOfqaNPN2CXqf2Xcj0VDdiVsl0+HzsBu8ydHQJ2mbOkJgQQQAABBBBAAAEEEEAAAQQQKC2Bog3Y3Xbbbc6g3CmnnGIee+yxCp9lzYm3xx57mE8++SSmLs2ZN378+JjlLECAgB2fgUwIELDLhGJ6dRCwS88t37ciYJe5M0TALnOW1IQAAggggAACCCCAAAIIIIAAAqUlULQBux49ehjNPRNdPv74Y9OmTZvoxWm9/vrrr02LFi2Ma067ZcuWmQYNGqRVLxsVrwABu+I9t5V5ZATsKlO7/L4I2JX3KJZXBOwydyYJ2GXOkpoQQAABBBBAAAEEEEAAAQQQQKC0BIo2YKe5gN5///1yZ7Nu3bpGQ2VqDrtMlV122cV88MEHMdVNmTLF7LPPPjHLWVDaAgTsSvv8Z+roCdhlSjL1egjYpW5WCFsQsMvcWSJglzlLakIAAQQQQAABBBBAAAEEEEAAgdISKNqAnbLbFJyLLAquvffee5GLKvy8f//+5uabb46p54EHHjBnnXVWzPJ0F2guPmXtaSjOhg0b2uy9TAYe021Xvmz3999/m8WLF1ufjTbayNSrV69Sm7Zy5UqzZMkSs/HGGxsFhsNKsQfsPM8zq1atsp9VPW/cuHGln4tI+99//90sXLjQbLjhhqZRo0aRb2X9ub6rugbJY/3117dtqFOnTkZuGCBgF376sumuvRZCwC7fvofB2dL3cdGiRWadddYxuk7re5EvpbIDdsrM17Vh+fLl5p9//rHXB/3dqlGjRqWR6O/mggUL7DVJ/1fo+pSJQsAuE4rUgQACCCCAAAIIIIAAAggggAACpShQtAG7LbbYwnz33Xflzmnt2rXN6tWrM9JhHlQ8ceJEc8ABBwQvTfXq1W0w7fzzzzf9+vUrW57qk1mzZpnRo0ebSZMmmenTp5vffvutXBUK1m2yySamQ4cO5tBDDzUdO3asUGBEc/5Flz333NPoJ52ydOlSM2zYsJhNO3fubJo3bx6zPFjw888/myFDhgQv7aM6EjX3YGT566+/zKuvvmqefPJJM23aNPPTTz8ZdVIHRZ2e7dq1M0cddZQ55phjTLNmzYK3MvL46aefmnvuucfOYaihURWwC4o+Z8rw7NWrl913ZKd0sQXsZP7mm2+acePGGX0XNKejOp8jy7rrrmvPuT6j+qzut99+9nsSuU6mnr/77rvmkUceMRr69ttvvzX6HAZFndH67G299damdevWRp3KmRq2VkEIHb++r2+//bZR578CR9FF31sFKvbee2/Tvn1765HOZzPdgN3s2bPNmDFjoptlTj31VFO/fv2Y5fPmzTOjRo2KWZ6NBSeeeKINeKdSd2W467seOSfphx9+aJ599tmYZg4YMCAmWL/99tubTp06lVtXQav777+/3DK90Nyn6Q7XXNnfw88//9yMHTu23DG0bdvW/j2KXKiA9YgRI8zTTz9t9DdNAarIopsb9t9/f3PkkUca/W1QYL2yiq7ZulYERW0bOHBg8LLssXv37kbHFlk22GADc+aZZ0YuivtcgTFdm3R90I+uT9q/azht/e3S9UnXB/0cfPDBGble6m/myy+/bK/X+l7r75auU2vXri1ru/5/2Wyzzew1+sADD7SfyXRudMhUwG7+/Plm5MiRZe2LfKKbQU4++eTIRTxHAAEEEEAAAQQQQAABBBBAAAEECl/A7+gryuIHBRS9ifnxh6/M6PGuWbPG84fe9PyOJc8PBla47smTJ3uHHHJITLtdxxK5zO9A9K699lrPD3il1YbIuoLnV199dVp1aSO/Q9J5DH7nf9w6/SBLzHatWrUqt42f4eL5wYWY9YJ2Rz9Wq1bN8ztXPT8Drlw96bzwgzIpnR+/s9N74YUXynbld0rHtNsP7pW9n80nfkdxzL5l9dZbb6W8W7+T1/MDsp7OTbR3otdbbbWV98QTT3iqIxPFD4x59957r+d3cqfUFj+AV6HvjNr+0UcfeX5A2KtSpUpK+w6M9Nn0AwLeF198kRKFvutBHZGPfgAgbj1+oMm53Zdffunczr9pwLl+5D4z9dy/McHZBtfCynR/6KGH0jY46aSTYpr/448/Ouvzs7Jj1k20IFffw8cffzzmGM4999yy5vrBIe+yyy7z/ABQzHphnxc/eOcNGjTI07aVUb766quk2xbd5s033zypJuradPfdd3v+zTVp72vLLbf0HnzwQU//a6RT1IYbb7zR87P3Um7Deuut55133nmen72e0q790QVi9uXfqJBSHfqfSs7R9nrtZ7KnfM1MaeesjAACCCCAAAIIIIAAAggggAACCORIQFlJRVnOOOMMZ0ePn8HgqQMr34qfdeG5OrlcnVXxlilA5Gc+pHx4rjrzLWDnZ0B4xx9/vPO8utofvczPprKBxJRx/rfBlVdemfa+/Qwmz88884ohYOcPoebtu+++aVsE52WnnXby/EyPdE+H3U5t2XnnnSvUFn0u/AyglNqh72u3bt3SDtQFBsGjn1Xj+ZlISbehVAN2uXDP14BdLr+H8QJ2Cv7qux18tlN99DOjPT/7LOnvQrorZjtg9/rrr9vAUqrHH7a+n8mfsos/lLbnDwWe9rkI2uJniXvPPfdc0tSu/2VSCdjpxh1/lARnuxXEmzNnTtJtYUUEEEAAAQQQQAABBBBAAAEEEECgkASKNmD3zjvvODt71AGljBh/3pi8OU/+sG7edtttF9reoNMs2Udl7fjDNaZ0fK668ylgp+wCf+i0Chv5w2t6c+fOTclGgbbTTjutwvvu3bt3wQfslGGYTqaG6/OlZTofU6ZMSel8BCsrM1Dbh9WdynJ/Ti3vjTfeCKqO+6hgwl577ZWR/Ua2UVl6/tCLcfcdvFmKAbtcuedjwC7X38OwgJ0/DHVGglQKMqWbURZ8RxI9ZjNg99RTT6WUXRh5HYj33B9KN9Fhlb2vv1uHH354xq5T+r9CmdHJlIoE7BSIVlahy0HZ2cq8oyCAAAIIIIAAAggggAACCCCAAALFKlC0c9j5nT1mt912M++9956exhTNi+Z3Ktl5aDRnS66K5mzbZ599jOZJCiua18fPIrJzsm266abGHw7O+MNwGn8YvXLzz0Rv/9///tf4w5JFL3a+9oMFMcv9gJ3RXFnpFM1l5mdZxGyq+bA0V1FY8TvrjN9ZV+5tf9hFey4j5xsKVvADLWbbbbc1WkdzpWn7mTNnxsz5F6yvRz/70vid8JGL4j4/4YQT7DxMYSvpnOy+++5mjz32MDqfmnNQ8xXpeXTR/ER//vlnucWa707rZ7v4w63auYmi9+MHvuw8UtHLo1/rM6rPquu4gnX97Af7OdX8gZoPSZ9T/Wg+ubCi8zZhwgRbd9g60cs1p5jmBoucfylyHX0uNH/Yf/7zH/vd0Xt+5o911hxkrqL5tHTu/OC56227zP9DYNs5depU5zp+p7ad01Lz0vnDttkfeen4v/nmG/PZZ5/ZeaOcG/sLNZ+Xzkeiku4cdppPTJ/n6CKbli1bRi+2c15pHsh0is6BfmrWrGk0j2OtWrXsvFmuebvk5Q9XHDqHWS7d/SFtzfXXX19GoLnHFi5cWPY6eCI/feYjyxFHHBEzL5o/NK9p0qRJ5Gr2uT8kpv2bFPNG1IJ8+B5qftLoeUV79uxp52bTtT+66Nzr+6jvlubw0/dB12nXZyHYVvPe+VmswcuMP+pvhc5PUDTPnOZ4jC66pvk3KZRbrPMXOa9h5Juaz1Nzdeoz6yqaX9cP+Ntrg+ah1Zynmm9X1wf96HoZPQ9oZD26jvsZzpGLnM/1/03Y3zmdB82Pp+PQdUrXYLVB58W/ccH5+dZO/CEyjX/Di5FJvJLuHHb6XqldmrsuumyzzTZ2/r1E+47ejtcIIIAAAggggAACCCCAAAIIIIBAQQkUayRSx6WMGd0V7p+Q0B9ltfiBFu+WW27x/I5QL9EcUJn0UgaBhrkKa5/fQeXNmDEjtE0a7upafy6rqlWrOuvQsb/55ptJNdnVhnzJsHMdX4sWLbzXXnvNmYWhoTPvvPNOz+8Idbr4ncdJDy328ssvO+uQlx9k8Pzgj9NXc0tp/i/Ny+SyjVxWCHPYKXMm3jxMfge19/333zsttFBzILmGAw0cNCdRsvMk/fbbb6EZGKpP2R1aJ6xoTkE/KOw8L8rgiDcc32OPPebcTt+1Sy+91PM7vMN2a5cr62Xo0KGeH+R11qP2JzPPpr73gV3kY6LrV6pz2KnRqjPVHxfCrbfe6myzMjbD5tAL6skXd7VHWUaR5sFzZUonUyoyh12+fA9dGXau67TmYw37u+oHpzzNexf4RT8qi7Uyi2yj26DXypZLtmj+Pf8GEmc9Gib0xRdftEMjx6tPwz127do1dLhdZc0lKq+88oqzDTvuuKPnBxRD/6dQvf4NJd59993naahgl4eyzROVdDLs9P1p3ry5c58ageCHH35ItFveRwABBBBAAAEEEEAAAQQQQAABBApeoGiHxAzOjDqeXJ1OYcs0xJ6GzLz77rs9PwMgYedasJ90HjVsZVg7NFebn5mTVLUKTDZu3NhZl5b/9NNPCetxtSNfAnaRbfPv8LfBVXWMJip+pkfo8GwK6CUq2kdYB2KHDh2SGlZVw64puBh5DNHPCyFg16VLF+cxKFB10003xe0AjnS+/fbbQ4eK05CnyZSLL77Y2ZY6deokPQ/csmXLQoN29957r7MZCgJqHqbo86fX999/v3ObsIWaR9PPVnTWpc76RKUyA3aJ2pLM+5qfTzdHRNv5GWmen10Zt4p8cldDcxmwy5fvoStgF3luFZAeOXJk3PMavPnMM8+E3ljjZ5MHq2X9MRMBO13fIh2C5372pbdixYqUjiEs6KbvkZ9dH7cuPys2ph26GSHZmyJU+dtvv+35mXcx9eg7+8svv8Tdf6oBOwXjwgKdCjL6Walx98ebCCCAAAIIIIAAAggggAACCCCAQLEIFH3ATidqwIABMZ1OQUdaokcFAZQlMGjQIE+dh4kyWJL9YPjDgoUGk9IJlOnudHXIuY6nf//+CZvl2i6ddgQ7+vjjj51t8YfEDFZxPipDydWWYFmyc+gElSubKtg28lFBtEQlLCNInaH+8GmJNi97Xxl/mpMpcv+Rz/M9YKfPvSvYomWJgi1lCBFP1BHsD5Xo9Eg0h5s+V66s2Q022CBhllZEE+xT1eUP1RjTDmXcukrYZ0md0+kUdbq7OsSVcZaoFFLATvOJ+kPBxjjrO/Dwww8nOlQvn9zV2FwF7PLpexgvYKfv4ueff57wvEauEJZpd+aZZ0aultXnmQjY+UMzx3zOldH99ddfp9X2Hj16xNSn781dd90Vtz7XdS3VeW21A/3fE/m3Knj+6quvxt1/KgE7ZZwqoBnUHfnoD2ns6eYKCgIIIIAAAggggAACCCCAAAIIIFAqAiURsNPJVBZa06ZNnZ1CkR1EiZ4rw+b000/31GGVStAm+gMVdie+hupLJnssuj699uf8cR6fAgCJsvVcx51vATtlPqZTwjoDleUUr/hzG8Z4aojLdIbm8ucuC80sy/eAnT/PU4yDPi/+/FLx+OK+588j6KxTQ8TGKxdeeKFzuwcffDDeZqHvaRhL12d/3rx5MducdNJJMesqaKmh/dItGkrUtf94Q3pqX4USsNPwfmFD6/nzaybFlk/uanCuAnb59D2MF7AbPHhwUuc1cqWwYUL9uUEjV8vq84oG7HTNcH2X/bn+0m63P7eps05dt8LK0qVLndvoRolUi/7WuW7WSHQTULIBO2XO+XMbOtvrz0Ecd3jiVI+F9RFAAAEEEEAAAQQQQAABBBBAAIFCECiZgJ1OxurVq73zzz/fq1mzprODyNXZFm9ZkyZNvBtvvNHO+ZLqyW7durWzDeoITbco+69t27bOehPdXe86znwL2E2ePDktmrDgULysBw2n6TLp169fWm3QRsoWcdWZzwE7ZTe4OmyVITd37ty0LZQRGvY9VHAzrGjewGhDBbnTzXzVvqLr0+sbbrghpgn6bkXP06WhUStSzjvvPOf+EwWFCyFgp6F4t956a+fxaWjHZM9ZPrnrXOciYJdv38OwgJ0y0hPdHBL2fXENiaibJiqrVDRgp7nulF0YfT1JJ1AWHPPy5ctj6lP9+psWVjSXaHQb9Pqaa64J2yTucm2n66FuCFIAMZmMt2QCdgosujIS1da999477c9R3IPhTQQQQAABBBBAAAEEEEAAAQQQQCDPBUoqYBecC3U4KQtAd3C7OrZSXaZO6URDRAX71qM6qlxBEN1pvnbt2shVU37++uuvO49JwzjGK65jzqeAneYWTLdofjHX8cWbH+nmm292bpPqUG+RbZ42bZqzznwO2IUNR5iJoequuOIKp0fY/IKzZs1yrq/gVUVKp06dvIMOOshT8Ezz12mYT2X8uIqyXzUv4WuvveYpCD516lTXakkv09B2rs+mhoaNV/I9YKchf5Ud5To2DQ+r91Mp+eKuNuciYJdP30MZhAXsjj32WL2dVtG8rdGfF2U0V1apaMAuaKcC1dOnT/cUwNOQkhUtypCPdomXtaf/IVxD7WqEgXg3qVS0nZHbJwrY6X+wsJuWNJfpr7/+GlkdzxFAAAEEEEAAAQQQQAABBBBAAIGSESjJgF3k2VWmz5AhQzwNt1i/fv2YjrHojrJ4r5U1kmiYRe37ueeec+5HgaVMFM3PFt1OHVu8jJbo9fU6nwJ2HTt2TJvmmWeeifHQ8SlbIKzss88+MdsoMFvRsvnmm8fUm88Buz59+sS0V3aaf62iJWwYvM6dOzurvummm5xt0Vx0hVoeeeQR5zFpKMl4JZ8DdgoY6HrquqYoaLB48eJ4h1Yp76XrrsblImCXT99DGYQF7HSjQ7rFlYGsLNzKKpkK2GW6va6/Gd27d4+7G8395vr+1a5d2xs6dGjc/wXiVpzkm/ECditWrAgdCUB/51MN5ifZJFZDAAEEEEAAAQQQQAABBBBAAAEECkKg5AN2kWdJAS1lXd1xxx2eMtLSCeAdfvjh3po1ayKrjXl+7rnnOjvTlNmTiRI215EylMKKq3MvnwJ28ebsCTumYPmYMWOc3mFDlelzUL169Zht0p1DL2iHHhWMirbO54Cda8gyDQuZ7jyLkRZ67hpCLiyb8rjjjouxUyZJou9b9D7z4fWff/7pjR8/3jvssMNijkmfjy+//DJuM/M5YHfRRRc5j0nn+tNPP417XNl+s6Lual8uAnb59D2UQVjALpVMc9UTWfr27RvzualWrVrkKll9nm8BO82NqYzf9ddfP8ala9eucS10E1L035nI15tssonXu3dvb+TIkXao8LiVpfFmWMBu5cqV3s477+xsm2400veTggACCCCAAAIIIIAAAggggAACCJSyAAG7OGc/COBpWKuDDz7YW2+99ZwdTZEdYXp+wgknxB3aMiz7JFPDVWmevug26fXYsWNDj9a1fj4F7JRdlW6ZOHGi02PSpEnOKsPmDUp3DqDIncg02jqfA3auoHUm55UKGxbNlam67777xtjttNNOkbx5+Vyd1BoiT5ktF1xwgR0qMtG1pFADdhpqOPrzrdcKvGgY0cos2XBX+3MRsMun76EMwgJ2GvY33aLrq+uzk259qW6Xq4Dd33//bYeqHD16tDdw4EBPw4rqGuuyCJYlCtgpS01zewbrx3vUfKSaM07z1CnrvKLDcsvdFbDT0J4aDjesLRom/M0330z1tLE+AggggAACCCCAAAIIIIAAAgggUFQCBOxSOJ2aV2XEiBGeK3AQ3Qn15JNPhtbcvn37mE6rTGYtaQ6w6PbotebUCSuu9fMpYKdMg3RLqgE7BUtcHpkYsnTYsGExdedrwE4dt665FtW5m6miTFaXtYaqjS6uoV4rMlRqdP0Vea2Mw/fee8979NFHvf79+3uaj0uZJBtuuKHz+FzHHLmsEAN2L730kqfrWORxBM8r8v2Nd14q211tqeyAXb59D2UQFrCLl8Wt7eKVYg/YLVy40Hv55Ze92267zQa0DjzwQK9Zs2aeAmbB9yTZx0QBOzl/9tlnXp06dVKuu0GDBl6PHj3s/zq//PJLvFMW+p4rYJfMsW211VZeuvsMbQxvIIAAAggggAACCCCAAAIIIIAAAgUkQMAuzZOlIJACLWGdUDvuuGPoPDGu4c0ymbWkjnNXu5T9ElZc6+dTwO6xxx4La3rC5akG7JR55/IYPnx4wn0lWkFDkEXXna8Bu2XLlsW0VW1XZ26myoUXXujch2voRFfwS8Nk5rJMmTLF0xC0Gpoz+rxW5HWhBewUrHQN3ScDZRVmuuTKXcdR2QG7fPseyiAsYKdhHNMtxRiwU5an5vXT3I0VuR5Eb5tMwE7nYerUqV7z5s3T3neNGjXsfJQKNKaSeZduwE7HqW0pCCCAAAIIIIAAAggggAACCCCAQKkKELCrwJnXfCuuebWCzjXNneYqmj8mWCd43HPPPV2rprVs5syZMfVrP//9739D6wvaEflYqgG7559/3umnQGhFyxtvvBFTd74G7ObMmRPTVn0+lEGWqXL77bc796Gh2SKL5qmL/GwGz0899dTI1SrluTqu9RmJF7AP2hfvUfMkhnXkF1LAbv78+V7jxo2d50dzeqbS0R/vBOaDu9pX2QG7fPoeBueHgF0g4X7U8Jqay7F27drO70W860Lke7qRx3UzQLIBO7VOw2P269fPq1u3boXaouEso6/L7qN3D4kZeVx6rmHGXXPFKqs7U/P5hrWP5QgggAACCCCAAAIIIIAAAggggEC+ChCwq+CZUSfyHnvs4ewI69Onj7N219wyysjLVNF8WdGdY3r9yCOPhO7CtX6pBuwUaHV5VCTLL4DXHD3RdedrwO7bb7+Naavaft555wWHU+FHZZ9Ee+j1vHnzytWteZ5c62Uy26/cDuO8OPfcc51tcbUvelmTJk287t27e88884y3atUq78EHH3TWVSgBuxUrVngtW7Z0HoPmF8zk8Hb54K6PRWUH7PLpexh8LQjYBRKxj7Nnz/Y0rGT0dz+Z1wrOKTB23XXXebrxRkXXjOhtUwnYBS3UkN7KElcQPdEcmtH7C14rmKa5OBOVRBl2J510kvfPP/94YVmVW2yxhffzzz8n2g3vI4AAAggggAACCCCAAAIIIIAAAkUnUHQBu9dff9279tprPQXLjj32WG+fffbxNASlMnSyVSZPnhzToaYOrrD5tdq2bRuz/gYbbJCx5o0bNy6mfrVHw1qFlaBDLvKxIgG7sKDhqFGjwppgl4d1TlckWJbqkJgffPCB00+fq4oWV0d3vgbsVq9e7XTQEJCZKsr8iPzMBc9dnbX6jgTvB4+aA68yy8CBA2PaELQl8lEd4gpYnXzyyZ6yCJVZuWTJkpimPvDAA876CiFgpwzjsPk8lUX8/fffxxxvugvyxV3tr+yAXb59D2Xguo7p81/qQ2L+8MMPnoJNkdeCsOcbb7yx/R/hsssu8zTnrYYBdv2fstFGG8XUl07ATuctKL/99ps3evRoO/xkqkNmap7KF198MajK+RgvYHfGGWeUZd1qDsoddtgh5vhkduaZZzrrZiECCCCAAAIIIIAAAggggAACCCBQzAJFF7Dr27evs/NHQZhsljp16sTsVx1hrtKhQ4eYddVBtXz5ctfqKS9T9o6rk3DGjBmhdbnWVzAl3TJ27FhnGwohYLdo0SJn23v16pUuR9l2ypyIts7XgJ0avc4668S0N5PZoOqUjfbQvEmusvnmm8esu//++7tWzcoyBY2j2xq8VpuPPvpo79FHH/VmzZrlKSMwmXLrrbc668z3gN2///7rdevWzdn2WrVqeR9++GEyh5/UOvnkrgZXdsBO+8yn76HaQ8BOCuWLAqutW7d2fid0nWjRooUdmlJZ1kuXLi2/cZxXrrkhKxqwi97dggULbAa+vtPJZAdqqE5XcDGoNyxg55rPUjf3KAgYXEuDR2XzjR8/PqiSRwQQQAABBBBAAAEEEEAAAQQQQKAkBIouYPfwww/HdPyoA+jee+/N6glt165dzH7r1avn3Kc624JOqcjHTHVya46xyHqD59HDDEY2Tp1jwXrB4yWXXBK5SkrPn3322Zj6VG8hBOwUbHF5HHDAASkZuFbWnGuBb/CYzwE7V3ZHJrNBNXdj4BA8KjDnKspYC9YJHsPWdW1fkWXKjnPNJaV2aB66jz76KK3qr7jiiphjUp2ff/553PqU7RkYRD4qkBavhH0vEwUIo+sMy4xUx3sm5noM9pdv7mpXLgJ2+fQ9lAEBOymULwpGRX4XI59feOGFdgjI8lskfqUs1sh6gueaOzdbRUNVatQAZcLVrFnTuX+1Y9iwYaFNcAXsGjVqFLp+mJ2ura5s69CKeAMBBBBAAAEEEEAAAQQQQAABBBAocIGiC9hNnTrV2cF08MEHZ/VU6e75oDMteNTQl64SllWTaJgpV12uZYccckhMW9SmePNJuYIRFZmrTAHSwCHysRACdjJ1ZXOFZUy6zkHYsgMPPDDGJZ8Ddp06dYppr85nJrJBNf+jsrEiPx96vvPOOzv5evbsGbOu1tc8ahUpGkZX+1QnuIan0zCVWvb777+XVRv2nVVGjWuoy7INEzxxZRjqmJSlF6/kMmA3ZMgQ53lQuzX8ZyZLvrnr2HIRsMun76EMCNhJ4f+KAmv169d3fi80T2W6ZfHixc46Ndx3ZZSvv/7a23TTTZ1t0HC/YcUVsFPQOaxofj3X3L66pvTu3TtsM5YjgAACCCCAAAIIIIAAAggggAACRSdQdAE7dd6rk8f18/7772flBGoeFteQZWHDVoUFFc8///wKt09z07iG50w0jKFrfrB4HXKJGtqjRw/nOSiUgN3ZZ58d036d44ULFyY69ND3FaByBQLzOWAXNnfYyJEjQ48z2TfeeeedGGN9b8MCxQ899JBzfQXXKlLOPffcmHo1DF1kwK59+/Yx66itFemMV5t32WUXZ72azypeyVXATkPduq51slAnfaZLvrnr+HIRsMun76EMCNhJ4f9K2DypmssxUdbr/9US++zVV191Xh+OOeaY2JWjlvz444/epEmT7A0IutamW8LmdNX8lWEl1YCd6gmbe1fXFr1HQQABBBBAAAEEEEAAAQQQQAABBEpBoOgCdjpphx56qLOTS8sr0nkW9oEICyRcffXVzk0U4NOcV+qIivxZb731vO+//965TbILwzpSr7/++rhVNG7cuFxb1K6KzA+mOW4ijy14XigBuwkTJjjb36dPn7iO8d4Mm1swnwN2GhotOHeRjwoAKwBZkXLKKac4654yZYqz2s8++8y5fkUCy9qRKztW89FFli222MK574p8X5WZ5xp6Vc4zZ86M3H3M81wE7DTsZ+3atZ0OymBOdt6+mIOJsyDf3NXU4cOHOw0092UyRYGUyO9S8FyZnWEln76HamPY35lvvvkm7BASLr/mmmucLgk3zNAK3333nXP/Tz31VMI9hM2zeNpppyXcNt4K55xzjrNNRx55pHMzXZP1d1vDcQefKz0mE+BzVvi/hU2aNClXn+rUdTOspBOwU11hWdS60UVzBFIQQAABBBBAAAEEEEAAAQQQQACBYhcoyoBdWOemOpmUTZPJok4kV3BKcznNnj07dFdh89idfvrpodskekMd5q55vnTcieaoatOmTUyHnIaw0nw2qRYFXLRP10+hBOx03A0aNIg5Bg0dumDBglRJbHCrZcuWMfXJKJ8DdmvWrHF+vtXuJ598MmWHYAMZKost+jOy8cYbhwYCFWzfZpttYrZRECndITo15Ft0G/RaWVSRxZWBqkzWihQF0V371rIZM2bErbqyA3YKZihbyNXeHXbYIWud6fnmrpMSFnifP39+3HMWvJlOwC6fvoc6jmIM2IUNP/noo48Gpy70UUPBur4bd9xxR+g2id5YtmyZ57qRRvvRsNdhRcG56LboGqlhO9Mtmksuus54w4ynG7DTddw1X6P2XdHgZ7rHznYIIIAAAggggAACCCCAAAIIIIBAZQoUZcBOgHvssUdMB1PQ4XTBBRd4f/zxR4WdV65caYMtQb2Rj126dIlb/3vvvedsn4abmzNnTtxtw9684YYbnHW2atUqbJOy5UcddZRz21Tn1VPHsjrwIy0inxdKwE4wrmExdSzpzKkTlpWj+vI5YCeHsHnEtt56a0/nO9WiwJtrLj9ZJAqoh82NqGy9VIvacdBBB8V8VqtXrx4zL95uu+0Ws57a+02aGUXKVtN+VIfrRzcdxCuVGbDTTQnKqHS1U5kvFckyjHeMei/f3NWmF154wWmhDNBkSjoBO9WbT9/DYgzYKUDm+ozfc889CU+rhop1bauMsXSL5qlz1all8bLfn332Wed2jzzySFpNCcs8DBu6WDtJN2CnbZ977jln+3XccqYggAACCCCAAAIIIIAAAggggAACxSxQtAG7sA60oANsq6228lINRkV+EF577TVv++23D+1YSma+PM0BE7Qn8lGd41999VXk7hI+v/POO0PnllKwKFEJm5epdevWSWfPaKhPdVBGHkv080IK2GnIwvr168ccj4Kq8Yavi7b+5JNPvE033TSmnsAm3wN2q1at8lyZTmq/OpV//vnn6EMOfa05Fk866SSnhTLu4mWlqtJff/01Zri3wHHQoEGh+3W9oe9MsG3kY8eOHWNWDwve3nLLLTHrJlqgIJ9rGM7INmjuqnilsgJ2CsiGBVc33HBD7/PPP4/XzAq/l2/uOqDx48c7PzcjRoxI6njTDdjl0/ewGAN2+vsV+R0MnuszmKiEnVNlDGte3VTLzTff7GxL0CbNfRlWdI2sWbNmzPZaphsFUi3dunWLqUvteOWVV0KrqkjATpV27tzZuU+NZqDvAQUBBBBAAAEEEEAAAQQQQAABBBAoVoGiDdjphA0cONDZ6RN0eulRASnNnfPhhx8mPMfffvutN3ToUG+fffaJW++FF16YsC6toIwM17CAapcCJLpTPlFR9ouy+SKPKfL5iSeemKgK+76Gy2rYsKGzHgUWE3U6amjBeFmNQZsKKWAnGGUlBG2PfjzjjDM8dfLGK5r/KOwcB/Xle8BOx6dh4YL2Rj9qmMqPP/44HoN9T8E4ZXtGbx+81ncrmRLvnGg4uERzHWm407vuusvTnJHBvoPHunXrOjNchwwZErOutlGW3PTp05Nptl1HndwKdAX7C3vUsIvxSmUF7MLmGVTn/zvvvBOviRl5L9/cdVD6W+E6b8o2TCbjMiy4k8xNAPnyPSzGgJ3OrevGBH3Hk8nsChsyVvPNJTt3bqK/58HnLt78cTqO4447zvkZ3XLLLb2pU6dqlYRFQ2xfeeWVznr0f1O8Y6powE7zQepaHBxv5GOvXr0Stp0VEEAAAQQQQAABBBBAAAEEEEAAgUIVKOqAnU5Knz59nJ0+kR1AwXPNF9OuXTvvsMMOs/OlaNvjjz/eO+CAAzwN/xesF+9RHWVr165N+vMQ1vEZ7KN79+52Pi3NQRfU+8svv3iTJk3ylFHUvHnz0HY1a9YsYfAisqGXX355aF2ar6t///62k/6HH37w1AYFHJ9//nnrpTn7gjbrUUEqBaIil+l5oQXs5NO+ffuY4wiOS0P2KaigTKOgA1NzIek41bEYrBfvsRACdnIIC97o2GrUqOFddtll9rgXLlyo1W1RcEJBKnX8ah6lMAdl6qVSwrI+VL8CiIMHD/amTZvm/f7772XV6nOrtoTN81ilShVv9OjRZetHPtFxNGrUyNl+Ba+uuOIKT0PkuoqC4QrA6TqifYQZRC5X9l+8UhkBu4ceeii0rTqWq666yrvkkkvs8Hcnn3yyzbbs1KmT16FDh7R/oocDzjd3nROd58hzFf1c12T5KKihbNLoUpGAnerKh+9h2N+tZAKW0R7Ba904E22p15VZXHO5Bm3S/wcainLnnXe2GbLB9T5o39VXX+1sv7bX/xUTJkwIVo151M0Mffv2Db3GBG0IHhXMilc0TK2y+4L1ox/1PZ04caL3008/latG/2Po+v3www97GoUgervg9ciRI8ttF/2iogE71RcWrFcbNMIBBQEEEEAAAQQQQAABBBBAAAEEEChGgcrtDcuBoDqgFHQLOpqy+ajAjjrnUy3q7E+mXbr7f9ttt/Wig2OubZXtkerwV+psrVatWlJtce0zWKb2qUNPc70Fy4LHQgzYqYNdnbTBMYQ9avjMLbbYInQ9ZXRdeumlMe8XSsBOw1kqEBF2/JHL1Vmsz2DksrDnylgNC3aFfZeUiRI2v1nkfjR8acuWLZPKahswYEDY7uxyDVMZWXf0cwXulEGojJrTTz/dO/zwwz0FAFxZO9pWQc4HH3zQa9u2bUy9CkjGK5URsEv2uhTtUJHXrkznfHIPzsl2220Xc85cx62gR3SpaMAuH76HxRqwCxuC1XVuFyxYUO7UKiMtUZa5hkbW9U4BbmW/67my3lz1a5neD5u3M9Gw2e+++669xoTVHSyvVauWnXdW7dD1Mlge9qhrT6KSiYCdAqIKkLraIcdU/2YkajPvI4AAAggggAACCCCAAAIIIIAAAvkgUPQBuwBZGS5hQ1a5OoRSWaaOeg2/qfme0i0aDlBDb6Wy37B1Dz30UG/ZsmVpNUVOFQnaKVin7CaVYgnY6ViUUXjIIYdU6PxoODsFMqPPW6EE7OSgz7jrvEYfUzKvlW2moJCGqEynKHvu6KOPjvFMZt/R6ygTKshgjdeWCy64ICP7UxDx008/tbtyZbYqQ1WfubBSSgE7GeSLe3A+nnzyyaQ+B7oeKsAWWSoasFNduf4eFmvAThme+nsefX1wvXYNk6mbXsKGcnTVEbZMnxvdQKBro+YIdf1voIy+ROXpp59O6ljC2hG9/Lzzzku0S/t+JgJ2qmju3Lmh50OZphQEEEAAAQQQQAABBBBAAAEEEECg2ARKJmCnE6eOr4suuiipu8ijO6rCXnfs2NHT/G2ZKBrCL9Ed+mHt0HINOXjzzTeXDc2Ybptefvllb7PNNku5o69p06be5MmTy3brCuwUYoZdcEDKoFDgYN11103JRsOJquNUpdADdoHFsGHDPJ3veJ/HeO8p+06ZUxUtCrLddtttSWXQudqz4447em+99VbSzVCg5JZbbvF0Tl31JVpWr1497/rrr/d+/fXXsn2++eabzrqeeOKJsnWin5RawC5f3CPPwznnnOM8b9GfgZkzZ0Zu5mUiYBdUmKvvYbEG7OSqYGwy1/g77rgjOA3lHjWf53777ZfUZyP6s6LXyirT/wKRRZl20etqmO5kiobi1Dy00dun8lr7SuV6namAnY5PQ3+HtXXMmDHJELAOAggggAACCCCAAAIIIIAAAgggUDACJRWwC87K0qVLveHDh3sadk7DGIZ1BoUt32GHHTzd3f7JJ58EVWb0UfNoaejBZDoN1cZddtnFzvcSLyMn1QYqK0SBhXhDPAY+Cr5ojrLoIaqKLWAXGC5atMjO3RVvTjbZaMjDrl27evPnzw82LZqAnQ5Iw7/edddddtjHZOZmU+amhovUPHLpZtWVQUY90WevX79+SX1edW4aNGhgh5pLtx2a+0nD5yWbjdqiRQvvuuuu81atWhXVcs/766+/7JyPwfcpeNTNAGGl1AJ2gUOu3YN2BI8KxOv6G5wz1+Ozzz4brG4fMxmwU4W5+B4Wc8BOpu+9954d0taV2RacYwWl4hX9HdcQ1sH68R6VUXvEEUfYuWlddd54443OeqZPn+5a3blsypQp3sEHH+ysx9U2XdsUKLz77rtTHuo7kwE7XaM1B6CrjRo1Ifr/DufBsxABBBBAAAEEEEAAAQQQQAABBBAoEIEqaqffEVKyxc/QMX7nnPGHwjJ+IM/4Q0mW/fgBMNOoUSPTpEkT48/JZX/8ebOM3/leKV5+Fo6ZOHGi8TM0bNv8zmrjd84aP/vN+IE04885Y/w5s4wfQMxqe7R//y598/3335vFixcbmakN+vHndjN+cNH4Q3hltQ35WLnfUWhmzJhh/GG77I8/p5Hxh0OzLvqMdO7c2fgZVfnY9Iy3SZ+LN954w36P9DnVd0mfCX1Ogx9/6E/jd7BmfN/RFX755Zf28/rdd98ZtWX58uV2v/68Yyb48bMDjR9kjN405dc6Tn8uKTNv3jz742fbGn1vdd1o3Lixad68uTnwwAOtQcqVs0GoQL65+0Mh2vO/ZMkS88cff9hrgD5j+vED+6HHkek38ul7mOljy0V9unbMmjXLXkd0vdd3WudU1zQ/6J+wSX6wyV4TdX3QtUGP+jvhzxtn69L/FrvvvrvZe++9jX+DTsL6MrGCPiPffvut0fVRbdGj/rZr/2rPRhttZPy5F41/w4Bp2LBhJnZJHQgggAACCCCAAAIIIIAAAggggAACSQqUfMAuSSdWQwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCArAgTsssJKpQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggkJ0DALjkn1kIAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgKwIE7LLCSqUIIIAAAggggAACCCCAAAIIIIAA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)" + ], + "metadata": { + "id": "e_eLxRnnrtMM" + }, + "id": "e_eLxRnnrtMM" + }, + { + "cell_type": "markdown", + "source": [ + "A simple sound localization task is defined, and input/output data for it are generated.\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "The task is to estimate the ITD of a pure tone (sine wave) at a fixed frequency. This is equivalent to estimating the interaural phase difference (IPD) since the ITD is ambiguous for a sine wave.\n", + "\n", + "An SNN model is defined, and trained on this data, using surrogate gradient descent.\n", + "\n", + "The model consists of three layers. First, a layer of spiking input neurons which fire spikes according to a Poisson process with a time-varying rate determined by the input stimulus. This layer is divided into two subpopulations corresponding to the two ears, with signals to one ear delayed with respect to the other. Each neuron within a subpopulation has a different phase delay. Next, comes a single hidden layer of leaky integrate-and-fire (LIF) neurons, and an output layer of leaky, non-spiking neurons. Each output neuron is associated to a particular IPD, and the estimated IPD of the model is the identity of the most active output neuron.\n", + "\n", + "The input neurons are all-to-all connected to the layer of hidden neurons via a trainable weight matrix. In this way, during training the model is free to select the neurons with the appropriate phase delays to generate the desired properties for the hidden layer neurons. This lets the model learn to make use of delays without having to directly implement trainable delays, as this is a challenging problem" + ], + "metadata": { + "id": "YWULn4CcxiFj" + }, + "id": "YWULn4CcxiFj" + }, + { + "cell_type": "markdown", + "source": [ + 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nj7SiUCjs7OykyXoVF2gCqBZubm6bN2+u7lkAAADg5cV5zwAAAAAA/DGmpqb+/v6TJ08WK2lpadIOLi4u5T7o6elZu3btip6qiEKh6Ny5s9gsKSkJCgqaMWPGunXrIiMjNfviKhSK1q1bu7m5GRn9z6/MO3bsKN0U9+rVq//617+WLVu2Z8+ehISEsrIyQRDq1q3r4eEhC55LS0szMjLEZpMmTaysrMqdm3Sbbt0fql27dtpLou3s7KRNsmcAAAAA+Pti3TMAAAAAAJUzMDCwtLS0trZ2dXXt37+/iYmJ9K4scJUuLJYN0rhx41u3bolPqdVqHTtUi/z9/VNTU6VvefLkSWRkZGRkpCAIbdu27dmzp4eHR926dWUPNmnSZPz48du2bRNPZRYEISkpKSkp6aeffjI1Ne3evftrr73Wpk0b2TTS09M1ybSGjY1NRXNr2rSptJmWlubu7l5uT3Nzc+2i7Hxo6UsBvGzUavX58+cjIyPHjh2r418LGsXFxXFxcZcvX87Ly+vUqVPnzp0tLCz+3Huf41CCIJSWliYnJ8fGxsbGxhYWFtra2trY2PTu3dvS0lL3g/fv39++fbuNjc3o0aOlP/RRKpUxMTFRUVFFRUU9evTo0qVLzZo1//T0/pCMjIzjx4+npaXl5uY+efKkbt26FhYWVlZWHh4ezs7O5R7IrVarw8LC4uPjx44da29vr3v89PT03bt3l5WVjRs3rqI9LQRBUKlUqampmq80KyuradOmNjY23bt3r3T8zMzMbdu2FRYWjhw5sl27dppifn7+kSNHrl275uXl5enpKd3AAwAAvPzIngEAAAAAkPvggw/q168vNo2NjS0tLQ0NDSvqf+/ePfHaxMSk3CXCGg0bNhSz56Kiotzc3ErTDkEQGjRosHr16q1btx45ckT7bmJiYmJiYnBwsI+Pz4QJE2Tz9PX1bdWqVWBgYHZ2tuxBzf/fP3LkSKNGjebNm9eqVatyP5FQcZouCIK1tbW0qWPdc7169bSLhArA38js2bODgoIEQVi6dGlISMjo0aPL7Xbv3r3JkycfP35c9muSli1bLliwYPr06VX5zY3G/fv3J0+efOzYMe2h3n333RkzZlR9KEEQUlNT58yZExER8ezZM9ktY2PjkSNHLliwoEOHDuU+e/bs2R49emimcfbs2W+//VYQhJSUlOnTp2tSZ7GniYmJh4fHmjVrpFtWPF8qlWr37t1r1qy5evVqRX0aNmz49ttvL1q0SBaEBwQEaE5/+OCDD/bu3fvmm29WNEJJScmQIUMuXrwoCEJQUNClS5e0f0L06NGjhQsX7tmzJz8/X3ZLoVD4+PgEBAR4e3uXO75arfby8tL8N/Hzzz+/d++ehYXF4sWLv/76a/EPyNjYuH///sHBwTr+wwoAAF4q7LkNAAAAAIBckyZNbCSsra11BM+CIBgbG4vXpaWl0kXGMuKxzRpVXxtnbGw8bdq0VatW+fr6lvu/4MvKyg4dOrRy5cqnT5/KbrVp0+aLL76YOXNmx44dy/0gmZmZy5cvlx7wLP1EgiAolcqKJlb1T2RmZlbRLQAvP6VSuXv3bs21SqWq6ODngwcPdujQ4ejRo9rbGNy8eXPmzJmTJ0+WJrU6HDp0yNXVNTw8vNyhZs2aNWnSpCoOpVKpgoKCnJ2dDx48qB08C4JQUlKyc+dOd3f3b7/9Vq1Wa3cICwsTp7Fp06aSkpJLly55eHhERETI5lBcXHzixAlPT8/Q0NCqzO2Pys3N9fLyeuutt3QEz4IgZGVlLV++3N3dvaCgQCzm5+drUnNBENRq9YcffqjjP1ihoaGa4FkQhNu3bx87dkzW4dChQ87OzsHBwdrBs2b8w4cPa+LnkpIS7Q7Jycnij7EKCwtPnjy5efPmNWvWSP+ASkpKfv311wEDBuj4pAAA4KVC9gwAAAAAgL6aNWsmXpeWlmZmZlbUU3rL3Nzc1NT0D72odevWEydO/O677z7//PM333xTtuZYEITY2Nhdu3ZpP1i7du3XX3992bJlmzdvnj9/vru7u+xkaKVSGRgYqDk9WvaJBEG4e/duVT6RoHN3btkbAfy9XLhw4dGjR2IzIiKiuLhY1mf37t2+vr65ubk6xtmyZYuXl1deXp7u1+3Zs+eNN97QPdTWrVs9PT0rHUqtVg8fPnzWrFnSFLZcSqXy7bffXrp0qfatkydPitfPnj1bt25dz549Hzx4UNFQJSUlkydPlu0hoT+VSvX666///vvvVewfGxs7d+5csalQKKQbcV+7dq3c7TQ0Nm7cKG3K/ovz4YcfvvHGG/fv3690DoGBgcOGDdMOuU+fPi1trlu3btasWeWOcPny5SdPnlT6IgAA8DLgb30AAAAAAOjLxsbm/7F3nwFNZO3/8Cf0qoiIVEEFKSJNkR5EUEAFwa7YC+qquxYsq+LaG+ruWtaylrVjXUVFKYoUKWIFQWkiCFIEReklyfNifv955p5AQELT/X5e5Vxz5pqTEAnmmnNOXFwc1czJyWHsgkyqqqqiV2qb3C2VQm4mqqamRpaEWSxW7+Ay5j0AACAASURBVN69e/fuPXny5IyMjKCgIPo3+I8fP54zZw61Dm1+fn5OTo6pqSk5I1lGRsbOzs7Ozq6ioiIuLi4gIIBetklISHBxcSEIQllZWUJCgpqpJqD2/PbtW8ZL0cwnBQDt48yZM/S6aWP4l0xgYBT/OBwOY9mDqqqqVatW8Z9oZmY2atSo1NTUO3fukFd59uyZ4GtVV1c3lmrkyJFpaWlUqufPnwtORRDE5cuXb9y4QY+Ym5svW7bM1NS0vLz8yZMnW7dupVeRd+7cyWazGXNtGbOlfX19qccWFhZGRkaJiYnPnj2jz5kuLS09ePDgzp07mxxh80VHR7948YIecXZ2Hjp0qImJia6u7sePH588efLbb7/R7xI4ffr07t27yf0d5OTkHBwcQkNDqaMBAQENzirOzs6mT3RWUFCwtLSkmq9evdq2bRu9v4aGxrp16wYNGiQmJvby5cs9e/a8evWKOhoUFLRnzx7Gz/Tdu3f0Znh4eGPP2tXVtcFdGwAAAKATQu0ZAAAAAAAAQFiMgmtERISVlRX/LqR3796lL14tYBNlOi6Xu2LFig8fPhAEsX79evpepCwWS1dX9+effy4pKUlJSSGDnz59KiwsVFFRIQji0aNHv//+O0EQenp6mzdvpi+4LSsr6+TkpKqqumHDBiqYkpJC1p5FREQ0NDSounJRUVFKSoqhoSFjbLW1tffu3RPwUgBAh8vPz8/Pz2+HCx04cICx43vfvn1DQkL69OlDNqurqzdt2tScWuzBgwcZt7z06dMnJCSkb9++VKrNmzfv2LGjyVQVFRX0OjFBENOnT//777+pnQWsrKwmTZrk6ur69OlTqs/ChQszMzMF77ZAptq0aZO2tjbZPH/+/PTp0+kTfK9du7Zjx45v2pRasLNnz9KbixYt2r9/PzWVWVdX18bGRldXd8SIEVQfLpcbFBQ0ffp0sjlx4kR67fnff/89evSolJQU40LU+uokLy8vcXFx8jGPx1uyZAl9IXQbG5vbt29369aNbJqamk6cOHHmzJmXLl2i+qxbt27GjBn8y3UwGBgYrFy50s3NrbCw8NmzZ1FRUcrKyvSp2wAAANDJYc1tAAAAAAAAAGFZWFioqalRzSdPnty+fZvRJzc3NzAwkGqKi4s3cwPL7OxssvBMEMTvv//OPwWZxWKRE9ooVAEgJiaGfJCamnr06FH+TUwZW0dTJxIEMXr0aPqh33///cuXL/QIj8cLCAigz66zt7dXVFRszpMCgB9MZWXl9u3b6REJCYnLly9ThWeCIKSkpLZt2+bh4dFkKsacWgkJiStXrlCFZzLV1q1bGb+mGrRz5076wtcqKioHDhxgbGmvpKR07NgxeoU4Ozs7LCxMcOaBAwceO3aMKjwTBOHt7W1lZUXvk5GRwfjNKST6/OxZs2YdOHCAvoY2aciQIYxaclJSEvV44sSJ9O0eysrK7t69y3+hCxcu0JuzZ8+mHl+7do0+k15MTOz48eP0jw+CIKSkpA4dOkT/RKivr//nn38EPDUy1eXLl2fNmqWiomJiYjJr1qyTJ0/u3LmT/gkLAAAAnRxqzwAAAAAAAADCEhcX9/HxoUfOnTt35syZlJQUDodTUlLy4MGDX3/9lb7V6Lhx48ipyU3q1auXgoIC+biiomLr1q2MLZbT09Ppa27r6OiQy2sTBEGfJP3gwYNz587RZ6pxudzTp0/TU/Xv3596bGNjY2ZmRjU/f/68ZcuW0NDQkpKSurq6jIyMffv20avpsrKyM2bMaM4zAoAfT1paGqPIun37dnNzc0Y3ERGRs2fPCi4lpqen029qIQhi27ZtDaY6c+ZMk1XJ69ev05tLlixpcPVmc3PzYcOG0SP//vuvgLSSkpIBAQHUL1uKiYkJI1JWViZ4hN9k5cqV5GeHvr7+/v37G5xRLS0t7eDgQI8UFxdTj+Xk5KZMmUI/ypjiTBBEcnJyYmIi1dTV1bW1taWajJd0zJgxBgYG/MPo3r373Llz6RHBLylBED4+PkZGRoL7AAAAQCeHNbcBAAAAAADgv44xAY4gCP5yQpOMjIyGDBlCTQXjcDiBgYGBgYFiYmKMLVEJgtDQ0GBM12PUD+hNUVFRNptNVXk/ffq0ePFiNTU1PT09WVnZV69eMbbM9PT0pB7b2NicPHmSWuj75s2bwcHBurq6ffv2zc/Pf/XqFX2HVw0NjUGDBtHHMG/evKVLl1K7Pr979+7o0aPkkOg1bNLUqVOpGnmTT6qxIP8EPgAQkri4eHN+p9FvjmmBjIwMRmTkyJEN9uzSpYu9vT19NWZhUrHZbP7SKaWuri49PZ0ecXR0bKzzwIEDQ0JCqGZmZmZjPQmC6N27t46ODn+cv/bM2CdbSPb29u/fv3/79q2Ojg7/L8yKiorIyMjQ0NDk5GR6vKSkhN708fEhf5mTbt26VV5eLicnR0UuXrx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+ ], + "metadata": { + "id": "ckVj4tm_xkCz" + }, + "id": "ckVj4tm_xkCz" + }, + { + "cell_type": "markdown", + "source": [ + "# Our objective:\n" + ], + "metadata": { + "id": "zLsc3TCSqY3-" + }, + "id": "zLsc3TCSqY3-" + }, + { + "cell_type": "markdown", + "source": [ + "You will define a project and will play with the model to investigate ow to improve the performance of the network?\n", + "\n", + "Some project ideas for you:\n", + "\n", + "- Change time constant\n", + "- Mean or max of output neurons membrane potential\n", + "- Heterogeneous time constants\n", + "- Change number of hidden units\n", + "- Only positive weights\n", + "- Adaptive thresholds\n", + "- What is better, to use the mean or the max of the output neurons membrane potential time variation?\n", + "…\n", + "\n", + "- Can we understand what the network is doing?\n", + "Ablate neurons after training and see how this affects the test performance\n" + ], + "metadata": { + "id": "nefPqmJPqW8L" + }, + "id": "nefPqmJPqW8L" + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "---\n", + "# Before Starting: Setting Up Your Environment\n", + "---\n", + "---\n", + "\n", + "\n", + "1. Download a copy of this notebook to\n", + "your personal google drive\n", + "In the Colab menue select:\n", + "\n", + " \"Files\" -> \"Save a copy in Drive\"\n", + "\n", + " This will create a folder \"Colab Notebooks\" where the notebook will be saved.\n", + "\n", + " you can locate where this copy was saved in your dirve:\n", + " \n", + " \"Files\" => \"Locate in Drive\"\n", + "\n", + " Now you can rename the located file and move it to a location of your choice in your google drive\n", + "\n", + "2. Close the original notebook\n", + "\n", + "\n" + ], + "metadata": { + "id": "vjbKLMEHG_VW" + }, + "id": "vjbKLMEHG_VW" + }, + { + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "id": "gF0MTqoCHJ43", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "b80b4e81-f289-49fe-8879-c63962f11bbf" + }, + "id": "gF0MTqoCHJ43", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + " To check if your google drive is mounted" + ], + "metadata": { + "id": "NJU3BW2s31el" + }, + "id": "NJU3BW2s31el" + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "\n", + "# Check if Google Drive is mounted\n", + "if os.path.exists('/content/drive'):\n", + " print(\"Google Drive is already mounted.\")\n", + "else:\n", + " print(\"Google Drive is not mounted. Please execute the mount command.\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "g67gPWhv3_E3", + "outputId": "89f351cc-7a5c-4917-dac7-a81411b14fc8" + }, + "id": "g67gPWhv3_E3", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Google Drive is already mounted.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + " Create a plot and save it into your Google Colab virtual workspace. Note this workspace will be deleted when you exit Google Colab.\n", + "\n", + " In left menue click on the folder symbole. You will see the example_plot.png" + ], + "metadata": { + "id": "w_qSEwtx4Fic" + }, + "id": "w_qSEwtx4Fic" + }, + { + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Example plot\n", + "x = [1, 2, 3, 4]\n", + "y = [10, 20, 25, 30]\n", + "plt.plot(x, y)\n", + "plt.title(\"Example Plot\")\n", + "\n", + "# Save the plot as an image file\n", + "plt.savefig('example_plot.png')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "QbM_popw4J55", + "outputId": "bf40c5f6-60d1-482b-bf8f-f936e52a49c7" + }, + "id": "QbM_popw4J55", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + " Now copy example_plot.png to you Google Drive:\n", + "\n", + " Note: You have to change adpat the file path after /content/drive/ to define where you want to save your figures." + ], + "metadata": { + "id": "Xk8gAmCq4NCH" + }, + "id": "Xk8gAmCq4NCH" + }, + { + "cell_type": "code", + "source": [ + "!cp example_plot.png \"/content/drive/My Drive/Mini-Project_ColabNotebooks/SBCP_2024/Results_Figures\"" + ], + "metadata": { + "id": "r2zp3Ub54QZx" + }, + "id": "r2zp3Ub54QZx", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + " Check if it was well copied" + ], + "metadata": { + "id": "3Y6XwEn24WkC" + }, + "id": "3Y6XwEn24WkC" + }, + { + "cell_type": "code", + "source": [ + "!ls \"/content/drive/My Drive/Mini-Project_ColabNotebooks/SBCP_2024/Results_Figures\"" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MdCxsZxy4ZcH", + "outputId": "ad0fbdf4-ddee-4340-a1b6-a3463346a9f4" + }, + "id": "MdCxsZxy4ZcH", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "example_plot.png Proj_EnvelopePower.png\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "---\n", + "# Let's start: Run the notebook\n", + "---\n", + "---\n" + ], + "metadata": { + "id": "uU4xuSxrHpO7" + }, + "id": "uU4xuSxrHpO7" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee3c91b7", + "metadata": { + "id": "ee3c91b7" + }, + "outputs": [], + "source": [ + "# Import necessary libraries\n", + "import os # Provides functionalities to interact with the operating system (e.g., file management).\n", + "import numpy as np # Used for numerical computations and working with arrays.\n", + "import matplotlib.pyplot as plt # For creating visualizations and plots.\n", + "from matplotlib.gridspec import GridSpec # To create complex subplot layouts.\n", + "from matplotlib.colors import ListedColormap\n", + "\n", + "import torch # PyTorch library for machine learning and deep learning.\n", + "import torch.nn as nn # PyTorch module for building neural network layers.\n", + "\n", + "# Specify the default data type for PyTorch tensors\n", + "dtype = torch.float # Setting the default data type to 32-bit floating point.\n", + "\n", + "# Check whether a GPU is available\n", + "if torch.cuda.is_available():\n", + " device = torch.device(\"cuda\") # If a GPU is available, set PyTorch to use it.\n", + "else:\n", + " device = torch.device(\"cpu\") # Otherwise, default to using the CPU.\n", + "\n", + "# Flag to indicate whether the current environment is resource-constrained\n", + "my_computer_is_slow = True # Set this to True if using a slower system, like Google Colab, to optimize resource usage.\n", + "\n", + "# Importing the Python Debugger module\n", + "import pdb # The built-in Python Debugger, used for step-by-step debugging of code.\n", + "\n", + "# Importing pandas for data manipulation and analysis\n", + "import pandas as pd # A powerful library for working with structured data, such as DataFrames and Series." + ] + }, + { + "cell_type": "markdown", + "id": "345a4686", + "metadata": { + "id": "345a4686" + }, + "source": [ + "## Generation of input signals" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Introduction to Input Signal Generation" + ], + "metadata": { + "id": "H6HPbDl81boK" + }, + "id": "H6HPbDl81boK" + }, + { + "cell_type": "markdown", + "source": [ + "The following function creates a set of auditory stimuli that can be used for **training or testing**. These stimuli are modeled based on interaural differences between two ears, capturing the essence of how the auditory system processes spatial sound localization cues. Below is a detailed explanation of the structure and logic behind the signal generation process.\n", + "\n", + "---\n", + "\n", + "#### **Key Components of the Stimuli**\n", + "\n", + "##### **1. Two Ears**\n", + "- The simulation involves **two ears** (labeled `0` and `1` in the code).\n", + "- Ear 1 receives a delayed version of the signal based on a given **Interaural Phase Difference (IPD)**, denoted as $\\alpha$ in equations (referred to as `ipd` in the code).\n", + "\n", + "---\n", + "\n", + "##### **2. The Base Signal**\n", + "- The base auditory signal is a **sine wave**, made positive for firing rate modulation:\n", + " \\[\n", + " \\frac{1}{2}(1 + \\sin(\\theta))\n", + " \\]\n", + " - $\\theta$ represents the phase angle for each neuron at each time step.\n", + " - This signal is then modulated to generate Poisson spike trains for each auditory nerve fiber.\n", + "\n", + "---\n", + "\n", + "##### **3. Auditory Nerve Fibers (ANFs)**\n", + "- Each ear is connected to **$N_a$ auditory nerve fibers** (referred to as `anf_per_ear` in the code).\n", + "- Each ANF generates **Poisson spike trains** at an instantaneous firing rate proportional to the base signal.\n", + "- These spike trains are **independent** across neurons, introducing natural variability.\n", + "\n", + "---\n", + "\n", + "##### **4. Phase Delays Across Neurons**\n", + "- To make the simulation biologically realistic, each auditory nerve fiber is assigned a unique **phase delay**. These delays:\n", + " - Are uniformly distributed between $0$ and $\\pi/2$ for neurons within each ear.\n", + " - Allow differences between the two ears to cover the full range of possible IPDs, from $-\\pi/2$ to $\\pi/2$.\n", + "\n", + "The phase angle for neuron $j$ in ear $i \\in \\{0, 1\\}$ at time $t$ is computed as:\n", + "$$\n", + "\\theta = 2\\pi f t + i \\cdot \\alpha + j \\cdot \\frac{\\pi}{2N_a}\n", + "$$\n", + "Where:\n", + "- $f$ is the signal frequency.\n", + "- $\\alpha$ is the IPD applied to ear 1.\n", + "- $j \\cdot \\frac{\\pi}{2N_a}$ is the phase delay for the $j$th neuron.\n", + "\n", + "---\n", + "\n", + "##### **5. Poisson Spike Generation**\n", + "- Each auditory nerve fiber generates Poisson spike trains based on a **modulated firing rate**:\n", + "$$\n", + "R(t) = R_\\mathrm{max} \\left( \\frac{1}{2}(1 + \\sin(\\theta)) \\right)^k\n", + "$$\n", + "Where:\n", + "- $R_\\mathrm{max}$ (`rate_max`) is the **maximum firing rate**.\n", + "- $k$ (`envelope_power`) sharpens the envelope of the firing rate.\n", + "- Higher values of $R_\\mathrm{max}$ and $k$ make the problem easier to solve because the firing patterns become more distinct.\n", + "\n", + "---\n", + "\n", + "#### **Output Structure**\n", + "\n", + "The functions return the following arrays:\n", + "\n", + "1. **`ipd`**:\n", + " - A 1D array of length `num_samples` representing the true IPD for each stimulus.\n", + "\n", + "2. **`spikes`**:\n", + " - A 3D binary array of shape `(num_samples, duration_steps, 2 * anf_per_ear)`, where:\n", + " - `num_samples`: The number of generated stimuli.\n", + " - `duration_steps`: The number of time steps in the stimulus.\n", + " - `2 * anf_per_ear`: Total number of auditory nerve fibers (including both ears).\n", + "\n", + "---\n", + "\n", + "#### **Simplified Explanation**\n", + "\n", + "1. For **each ear**, the function generates a **delayed signal** based on the IPD and the phase delay for each neuron.\n", + "2. The **spike trains** are computed independently for each neuron, following Poisson statistics.\n", + "3. The resulting **binary spike array** represents the auditory nerve responses over time, for each IPD and ear.\n", + "\n", + "---\n", + "\n", + "#### **Visualizing the Stimulus Architecture**\n", + "\n", + "Here’s a diagram that provides an overview of the stimuli generation process:\n", + "\n", + "![Stimuli architecture](https://github.com/neural-reckoning/cosyne-tutorial-2022/blob/main/arch-stimuli.png?raw=1)\n", + "\n", + "---\n", + "\n", + "#### **What to Experiment With**\n", + "- Adjust **$R_\\mathrm{max}$** and **$k$** (`rate_max` and `envelope_power`) to observe their effect on the signal sharpness and the ease of classification.\n", + "- Explore how different IPDs affect the spike patterns for the two ears, particularly in relation to the phase delays introduced by the auditory nerve fibers.\n", + "\n", + "By understanding these parameters, you can better interpret the generated spike trains and their relevance to spatial sound localization." + ], + "metadata": { + "id": "uqYDcWy61FdF" + }, + "id": "uqYDcWy61FdF" + }, + { + "cell_type": "markdown", + "source": [ + "### The data generation function" + ], + "metadata": { + "id": "T1dl7HlK2Blm" + }, + "id": "T1dl7HlK2Blm" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5bb26693", + "metadata": { + "id": "5bb26693" + }, + "outputs": [], + "source": [ + "# We use the following constants to make equations look nicer below\n", + "second = 1\n", + "ms = 1e-3\n", + "Hz = 1\n", + "\n", + "# Stimulus and simulation parameters\n", + "dt = 1*ms # large time step to make simulations run faster for tutorial\n", + "anf_per_ear = 100 # number of auditory nerve fibers connected to each ear with independent noise\n", + "envelope_power = 2 # higher values make sharper envelopes. Easier by eye => But does the network perform better ?\n", + "rate_max = 600*Hz # maximum Poisson firing rate\n", + "f = 20*Hz # stimulus frequency\n", + "duration = .1*second # stimulus duration\n", + "duration_steps = int(np.round(duration/dt)) # number of simulation steps\n", + "input_size = 2*anf_per_ear\n", + "\n", + "# Generate an input signal (spike array) from array of true IPDs\n", + "def input_signal(ipd, envelope_power=envelope_power):\n", + " \"\"\"\n", + " Generate a Poisson spike train based on an input Interaural Phase Difference (IPD) array\n", + " and the delays imposed by the individual auditory nerve fibers.\n", + "\n", + " Parameters\n", + " ----------\n", + " ipd : array-like\n", + " An array of true Interaural Phase Differences (IPD). Shape: (num_samples,)\n", + " Represents the angular difference in the phase of sound waves reaching the two ears.\n", + " envelope_power : float, optional\n", + " A parameter controlling the strength of the signal envelope, which modulates\n", + " the spike train generation. Default value is the globally defined `envelope_power`.\n", + " Returns\n", + " -------\n", + " spikes : ndarray\n", + " A binary array indicating spike occurrences, shaped (num_samples, duration_steps, 2*anf_per_ear).\n", + " `spikes[i, j, k]` is 1 if a spike occurred at the jth time step for the ith IPD in the kth auditory nerve fiber,\n", + " and 0 otherwise.\n", + "\n", + " Notes\n", + " -----\n", + " - The function first calculates an array of phases (`phi`) to define the sinudoidal auditory stimulus and adds a random\n", + " phase offset because we want that the system learns to infer the angular location of the sound source indepent of its distance\n", + " to the source.\n", + " - An array of theta values is initialized that will hold the transformed phi values according to the phase delay imposed by the\n", + " individual auditory nerve fibers and the ipd between the two ears.\n", + " - Different phase delays, ranging from 0 to pi/2, are calculated and added with the ipd value to generate theta.\n", + " - Poisson spikes are generated based on the theta values and a sinusoidal modulation of the firing rate.\n", + " - The spikes are returned as a binary array, indicating the occurrence of spikes across auditory nerve fibers and time.\n", + " \"\"\"\n", + " num_samples = len(ipd) # corresponds to the number of different locations of the source in the data set\n", + "\n", + " T = np.arange(duration_steps)*dt # array of times over which the auditory signal is constructed\n", + " phi = 2*np.pi*(f*T) #+ 2*np.pi*np.random.rand() # array of phases corresponding to those times with random offset.\n", + " # The random offset ensures that the system learns to infer the angular location of the sound source indepent of its distance\n", + " # to the source.\n", + "\n", + " phase_delays = np.linspace(0, np.pi/2, anf_per_ear) # array of phase delays introduced by the auditory nerve fibers.\n", + " # For each ear, we have anf_per_ear different phase delays from 0 to pi/2 so\n", + " # that the differences between the two ears can cover the full range from -pi/2 to pi/2\n", + "\n", + " theta = np.zeros((num_samples, duration_steps, 2*anf_per_ear)) # 3D array that holds the spike pattern of all auditory nerve fibers for all the interaural phase difference in the data set.\n", + " # num_samples = number of different IPD values in our data set\n", + " # duration_step = number of time points in our auditory signal\n", + " # 2*anf_per_ear = total number of auditory nerve fibers\n", + "\n", + " # Now we set up these theta values. Some numpy vectorisation logic using broadcasting to implements the idea in the text above.\n", + " theta[:, :, :anf_per_ear] = phi[np.newaxis, :, np.newaxis]+phase_delays[np.newaxis, np.newaxis, :]\n", + " theta[:, :, anf_per_ear:] = phi[np.newaxis, :, np.newaxis]+phase_delays[np.newaxis, np.newaxis, :]+ipd[:, np.newaxis, np.newaxis]\n", + "\n", + " # now generate Poisson spikes at the given firing rate as in the previous notebook\n", + " spikes = rate_max*dt*(0.5*(1+np.sin(theta)))**envelope_power > np.random.rand(num_samples, duration_steps, 2*anf_per_ear)\n", + " return spikes, theta\n", + "\n", + "# Generate some true IPDs from (-pi/2, pi/2) and corresponding spike arrays\n", + "def random_ipd_input_signal(num_samples, envelope_power=envelope_power, tensor=True):\n", + " \"\"\"\n", + " Generate random Interaural Phase Differences (IPDs) and then corresponding spike arrays using\n", + " the function input_signal(idp).\n", + "\n", + " The function generates `num_samples` IPDs, uniformly distributed in the range (-pi/2, pi/2).\n", + " It then generates corresponding spike arrays using the `input_signal` function.\n", + " Optionally, IPDs and spike arrays can be converted to PyTorch tensors.\n", + "\n", + " Parameters\n", + " ----------\n", + " num_samples : int\n", + " The number of IPD samples to generate.\n", + " envelope_power : float, optional\n", + " A parameter controlling the strength of the signal envelope, which modulates\n", + " the spike train generation. Default value is the globally defined `envelope_power`.\n", + "\n", + " tensor : bool, optional\n", + " If True, converts the IPDs and spike arrays to PyTorch tensors before returning them.\n", + " If False, they are returned as NumPy arrays. Default is True.\n", + "\n", + " Returns\n", + " -------\n", + " ipd : ndarray or Tensor\n", + " An array of randomly generated IPDs. Shape: (num_samples, ).\n", + " Returned as a PyTorch tensor if `tensor` is True, otherwise as a NumPy array.\n", + " spikes : ndarray or Tensor\n", + " A binary array indicating spike occurrences along time, generated by `input_signal` based on `ipd`.\n", + " Returned as a PyTorch tensor if `tensor` is True, otherwise as a NumPy array.\n", + " Shaped: (num_samples, duration_steps, 2*anf_per_ear)\n", + "\n", + " Notes\n", + " -----\n", + " - Ensure that the `input_signal` function is defined in your environment as it is called within this function.\n", + " - If `tensor` is True, ensure that PyTorch is installed and configured in your environment.\n", + "\n", + " Examples\n", + " --------\n", + " >>> ipd, spikes = random_ipd_input_signal(50, tensor=False)\n", + " >>> print(ipd.shape, spikes.shape)\n", + " (50,) (50, duration_steps, 2*anf_per_ear)\n", + " \"\"\"\n", + " ipd = np.random.rand(num_samples)*np.pi-np.pi/2 # uniformly random in (-pi/2, pi/2)\n", + " spikes, theta = input_signal(ipd)\n", + " if tensor:\n", + " ipd = torch.tensor(ipd, device=device, dtype=dtype)\n", + " spikes = torch.tensor(spikes, device=device, dtype=dtype)\n", + " return ipd, spikes, theta\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Test the data generation function and plot some examples" + ], + "metadata": { + "id": "S9BlEkbwZEpa" + }, + "id": "S9BlEkbwZEpa" + }, + { + "cell_type": "code", + "source": [ + "# Plot for a few true IPDs the generated spike trains of the auditory nerve fibers to show how it looks.\n", + "# The first 100 lines are auditory nerve fiber responses of the righ ear and the others are from the left ear.\n", + "# You note that the IPDs was applied to the left ear's fibers.\n", + "ipd, spikes, theta = random_ipd_input_signal(8)\n", + "plt.figure(figsize=(10, 4), dpi=100)\n", + "for i in range(8):\n", + " plt.subplot(2, 4, i+1)\n", + " plt.imshow(spikes[i, :, :].T, aspect='auto', interpolation='nearest', cmap=plt.cm.gray_r)\n", + " plt.title(f'True IPD = {int(ipd[i]*180/np.pi)} deg')\n", + " if i>=4:\n", + " plt.xlabel('Time (steps)')\n", + " if i%4==0:\n", + " plt.ylabel('Input neuron index')\n", + "plt.tight_layout()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 383 + }, + "id": "Og9fwc42ZDTN", + "outputId": "562b865a-30b1-4742-8381-7b5fd8d284ed" + }, + "id": "Og9fwc42ZDTN", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Inspect the generated data\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "metadata": { + "id": "8kybZ97vTV6Q" + }, + "id": "8kybZ97vTV6Q" + }, + { + "cell_type": "markdown", + "source": [ + "The objective of this section is to gain a deeper understanding of how the data generation function operates and to closely examine the generated output. This exploration will help us intuitively grasp what the dataset represents and how it encodes auditory nerve responses to stimuli with varying **Interaural Phase Differences (IPDs).** Let's generate a set of data and plot them with more details:" + ], + "metadata": { + "id": "E91lRqrehiIX" + }, + "id": "E91lRqrehiIX" + }, + { + "cell_type": "code", + "source": [ + "# To experiment with different envelope powers, modify the value in the function arguments.\n", + "# This approach avoids altering the globally defined `envelope_power` variable, ensuring consistency elsewhere in the code.\n", + "ipd, spikes, theta = random_ipd_input_signal(1, envelope_power=10)\n", + "\n", + "i = 0 # Select the index of the IPD sample to analyze or visualize.ipd, spikes, theta = random_ipd_input_signal(1, envelope_power=10)\n", + "\n", + "# Plot for each ear the input signal and the spike transformed signal in the first auditory nerf fiber of each ear\n", + "fig, ax = plt.subplots(2,2, figsize=(10, 4))\n", + "ax[0,0].set_title('Sound arriving at right ear')\n", + "ax[0,0].plot(0.5*(1+np.sin(theta[i,:,0])),color='blue', label='input sound wave')\n", + "ax[0,0].plot((0.5*(1+np.sin(theta[i,:,0])))**envelope_power,color='blue', linestyle='--', label='envelop pow applied')\n", + "\n", + "index = np.argmin(np.abs(theta[i, :, 0] - (np.pi/2 + (2 * np.pi if theta[0, 0, 0] > np.pi/2 else 0))))\n", + "ax[0,0].axvline(x=index, color='r', linestyle='--') # Plot a vertical dashed line at this position\n", + "ax[0,0].legend()\n", + "ax[0,1].set_title('Right ear spiking \\n 1st auditory nerf fiber')\n", + "ax[0,1].imshow(spikes[i, :, [0]].T, aspect='auto', interpolation='nearest', cmap='Blues')\n", + "\n", + "ax[1,0].set_title('Sound arriving at left ear')\n", + "ax[1,0].plot(0.5*(1+np.sin(theta[i,:,100])),color='orange')\n", + "ax[1,0].plot((0.5*(1+np.sin(theta[i,:,100])))**envelope_power,color='orange',linestyle='--')\n", + "\n", + "index = np.argmin(np.abs(theta[i, :, 100] - (np.pi/2 + (2 * np.pi if theta[0, 0, 100] > np.pi/2 else 0))))\n", + "ax[1,0].axvline(x=index, color='r', linestyle='--') # Plot a vertical dashed line at this position\n", + "ax[1,0].set_xlabel('Time (steps)')\n", + "\n", + "ax[1,1].set_title('Left ear spiking \\n 1st auditory nerf fiber')\n", + "ax[1,1].imshow(spikes[i, :, [100]].T, aspect='auto', interpolation='nearest', cmap='Oranges')\n", + "ax[1,1].set_xlabel('Time (steps)')\n", + "\n", + "plt.tight_layout()\n", + "\n", + "\n", + "\n", + "# Plot the spike transformed signal in all auditory nerf fibers, where each\n", + "# fiber adds an increasing delay Create the figure and axes\n", + "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(3, 3)) # Two vertically stacked subplots\n", + "\n", + "# Plot the upper half with blue colormap\n", + "ax1.set_title(f'True IPD = {int(ipd[i]*180/np.pi)} deg')\n", + "ax1.imshow(spikes[i, :, : anf_per_ear].T, aspect='auto', interpolation='nearest', cmap='Blues')\n", + "# Customize the appearance of the axis\n", + "ax1.set_ylabel(\"Rigth Ear \\n nerf fibers\", fontsize=9) # Add label with a custom font size\n", + "ax1.tick_params(left=True, labelleft=True, bottom=False, labelbottom=False) # Hide ticks but keep the label\n", + "\n", + "# Plot the lower half with orange colormap\n", + "ax2.imshow(spikes[i, :, anf_per_ear:].T, aspect='auto', interpolation='nearest', cmap='Oranges')\n", + "ax2.set_ylabel(\"Left Ear \\n nerf fibers\", fontsize=9) # Add label with a custom font size\n", + "ax2.tick_params(left=True, labelleft=True, bottom=True, labelbottom=True) # Hide ticks but keep the label\n", + "ax2.set_xlabel(\"Time (step)\", fontsize=9) # Add label with a custom font size\n", + "\n", + "# Adjust spacing to remove gaps between the plots\n", + "plt.subplots_adjust(hspace=0)\n", + "\n", + "# Show the final result\n", + "plt.show()\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 700 + }, + "id": "stdqcA2L8yc7", + "outputId": "a3c9f78e-9bd5-4129-b297-4edf3571cdc6" + }, + "id": "stdqcA2L8yc7", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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WaMuzeldtWaCZqiGZ6l0lHQvPQKGHa3IKoPYuJKQhBKYugWnrafnGYc0ykcLUGDolBa4S5S0Rue9g87uQcDgcTi1ckzOAEsHAUrAGivK8VGUDoVmXsqamZ8m1Q2KT5TyE2+QUwJiQUzJPlcYpoTZasgGZGrahpnNJri3uJDIOzXKVBwMrSP0fJGsaEkngr6H2SfqTO0cbeGvJ0MQmmtqHsX5ovKSkGqBSIUvWNq+0cE3OAMbi5GiXJDTahFw9JZayWv9R18fUWDgllrJc6CgL1+QUJDzmLdmdgVm1NWNtsJRRMrDV0mFZVtPOR20ZJR9kcv2baiKwJQ86zYuguCZnACU2zVQjvIAvSdSBVHuuX17JBxmfK2mkrmvmoaU8hITD4XAAM2ty69evR48ePdCpUyecPXsWEyZMgJOTEzZu3IjOnTsTt5OUlIRdu3bh4sWLcHV1Ra9evbB8+XK0a9dOLBMTE4OjR4/q1Zs0aRLWrFlD1IexTTNpvWTGyhqCJtXLljUAEg2XdjlHi1JhIkqGylgbpN/dYkNIWrZsidOnT8Pf3x9DhgxBly5d4O7ujiNHjuDIkSPE7QwZMgRjx45F9+7dUVVVhXnz5iE1NRUXLlxA06ZNATwUcm3btsXixYvFem5ubsRLT7Xi5GpRI4WH1s4jV0YLmOqYUTPkREmTBC1aeChabFqXp6cniouLUV5ejsDAQNy+fRuOjo7w9/dHfn4+c7t37txBQEAAjh49in79+gF4KOSioqKwYsUKpjaVzl01V/qP0mhJq2B1zCjxYDG1vC0/rKSwWO+qj48P0tPTkZqaim7dusHFxQXl5eWoqakxqd2ioiIAQLNmzfSOb9myBZs3b0ZQUBCGDx+OxMREuLm5SbZRUVGBiooK8XNxcTGAht5Vmh+dEh7Nxg4UteYbhyZujbVNtTUypTzvtoxZhdwbb7yBqKgoAMCmTZsAAMePH0dkZCRzmzU1NZg+fTp69+6NTp06icdffPFFREREICQkBCkpKZg9ezbS0tKwa9cuyXaSkpKwaNGiBsezfvxA7zONHUxOILGGHigBq3C0tmUOjUalhI2Opk0lYhtNDQa2Fcy6XC0rK8ONGzfg4OCAli1bAgAuXbqEyspKPQFFw5QpU/D999/j+PHjCAsLM1ju8OHDGDhwIDIyMtCqVasG56U0OZ1Op9rOwHLnWINPlUjFsmRBxpoLqmTbNHFySlxDS54PtZH77ha5XK2uroafnx+Ki4vh5OQkHm/bti1zmwkJCdi7dy+OHTsmK+AAIDo6GgAMCjkXFxe4uLgQ900bJ0WjpbFmStCeU6K8OTFVM5LTrGk1KymU0sS1Frjd2FDFyVVVVSE6Ohrl5eXUHTk4OKB169YmORhqEQQBCQkJ2L17Nw4fPowWLVoYrZOcnAwACA4ONrl/DodjPVAvVyMiIpCWloYmTZpQd7Z69Wp8+eWXmD17NnQ6Hezt/ydju3TpQtzO1KlTsXXrVuzZs0cvNs7Lywuurq64fPkytm7diqFDh8LX1xcpKSmYMWMGwsLCGsTOGYJ0F5K6sC5zjLXDUQ81Qklo2q5bjjZ32ZZRNYRkzZo1SElJwfLly+Hh4UE1sLpCTW8Qdnaorq4mbsfOzk7y+IYNGzBx4kRkZ2dj/PjxSE1NRVlZGXQ6HZ599lnMnz9f8Tg5JXaFYPWgqhlCoMWbSknbmqnXhz/kHsJ6HVUVcj4+PigpKYEgCPDw8NATXEosRS0F0l1I6kITO8eq5RkbA4dtHlidPSR9GKunlJdUa95VuYe4qkJObrnXv39/ojZycnKQnZ2Nxx9/nKZrs2IsrasW2h8yrVHZVK2CNlNCCzdHfeScREo4HEhg9a6TLGVthbrfnSZB36whJDdv3sTYsWNx4sQJNGnSBKWlpdi+fTsOHDiAzz4jexu2uTDFJkfreSWBRICZSwMw11KWRniby46lZr+0wtiWUT2tKzMzEydPnsTdu3dRt/rrr78uW2/kyJFiPmlwcDAKCgqQl5eH7t2748qVK7TDUBWW7c/VDPg19UduSxoArfZqDLmHlRJ9sKZ12TKqxsnt3LkTEyZMQPv27XH+/Hl07NgRqamp6NOnj1Ehd+LECezcuROOjo6i88DX1xd5eXm0w+BwOBwiqIXcwoULsWnTJjz//PPw8fHBH3/8gfXr1+PPP/80WtfLywv5+fkICAgQj12/fh2BgYG0wzAbxnYGltOQaDUANb15arWlJjQaWd0yhrRmuTLm1sjr1icZJyvWMtdSyM1D5qGlxO1QL1c9PT1RVFQEOzs7+Pj4oKCgAFVVVQgLC0Nubq5s3bfffhunTp3Chx9+iH79+uH06dOYOXMmevXqhXnzGnowGxOWdzzUP1cXGqeEEgZya/xRq4WSDiFTY+eksGZBZE7qXnOhupJ4PzlqTa5Zs2bIz8+Hr68vQkNDkZycDF9fX6IsiLfffhvz5s1Dr169UFZWhm7dumHy5MmYNWsW7TAaDRovmdw5JTQH1tg7LUAjGEy9rqy2Nf4QUo+sHz9AoC+Zs5JayI0ZMwb79+/HCy+8gL/+9a+IiYmBo6Mjxo4da7wzR0e89957eO+993D37l34+voaDOy1FMJj3oKdg7P4mdVgTQKJV1ZLuahSkDpIlPICs+a8kqCEU8JWtDzaufLu+hpx2yaHkJw4cQLFxcUYMmQIkcAqLi7G3r17kZOTg7CwMAwdOhReXl6mDEEVjC1Xa1EjTo50aUoTsqA17yqLl1MJG6k5vOM8rUsfqe/eKDsDFxQUwMfHR7bMiRMnMGLECAQGBiIiIgJZWVnIzc3FN998g969eysxDMWoFXIunV+VdDyYGgslhy3+kNVAzgxQH1ohp2QQMavGZ8u/E5p3PBDvQlLXIwoAsbGxep8jIiKMthEfH4+lS5fiwoUL+P7773H+/HksW7YMU6dOJR0Gh8PhUEGsyXl4eKCkpET8XOuAMHTeUBtFRUV6+a7V1dXw9vY2WtfckKZ1SUHrnCAxWPOAUXpoMkJINSZTNSvWrAYlsyi0AI0mR+x4MGZvI7HHDRo0CPv27cPQoUPFY/v378fgwYNJh2F2jMXJsdp55CD9QdZvi/Qm1qKdh+TBQGrUlrsurNfOHClmUljb3KphO6b2rtIyc+ZM8f/NmjXD6NGj8cQTTyAiIgKZmZk4fPgwXnzxRbWHYRKsthbaMBNaew/Nk1/uhtUCJNoW7TUkqafETUkTBkNbz9ogtU+qEkJSWVmJlStXip/Ly8v1Pj948ECyXkFBgd7nMWPGAADu3bsHf39/jBkzxuS3dalJ1o8fEG+1VIucUKSNk6Ppl6VcY6CEYDBVMyK51qzxjrT92XK8IyvhMW8RlyW2ycXExBhdktK8INrSYUnQr3+uLqZmNdBqHNZ2M5gjs4P1+tDa62i1PC2ZDUyF9CFAY5NTfaulkpIScQfh2neZSkH7Riy1MRRCwpqeRVK/fjvG6tmaBkArpEwVHqbaT5W0g3JBqI9Fva0rJCRE9Jx6e3s30AYFQaDe/pzD4XBIUV3IXbhwQfz/1atXFWlz4cKFDV4E3a5dO1y8eBHAQ3vhm2++iW3btqGiogKxsbH45JNPmHY7MfRyaSmUshORnmNdHlkbSi3npeqRQJvxoGSmhBY0OKW+Q/0EfVJUF3LR0dG4ceMGAGDRokVYv369Iu127NgRBw8eFD87Ov7vq8yYMQPfffcdduzYAS8vLyQkJGDUqFE4ceIEU1+sXlLWflidC9Z8I8ihZg4rTbqdXNu0Y5JqwxbnjxVVE/RpqaioQGlpKdzd3bFz507FhJyjoyOCgoIaHC8qKsLnn3+OrVu34oknngDw8C1ekZGR+PnnnxV7rwSrl9TUQGEOOUp5UOXOyWlrSghAW5t/UjumnH2/PqoLueeeew7NmzdHeHg47t+/j65du0qW+/3336naTU9PR0hICJo0aYKePXsiKSkJ4eHhOHPmDB48eIBBgwaJZdu3b4/w8HCcOnXKoJCrqKhARUWF+LnuRaT1oMndAEouZbSOUstxVm1NyeUxSXC2lmD1LNNmgpBALeTOnz+PadOm4cyZMygtLQUg7zxYt24d4uLicOXKFbz22mt44403TBow8HAJvHHjRrRr1w43b97EokWL0LdvX6SmpiI3NxfOzs7w9vbWqxMYGCi7qWdSUlIDO18tcoKMFBqtgjaLQg5rWwrR3hxyZWluoLrtsdrdaLzrjZkpYQ6UiOeUezDQ7AxMLeTi4uLQrVs3LFmyBE2bNiWq07t3b/Tu3RuFhYWIi4uj7bIBTz31lPj/Ll26IDo6GhEREdi+fTtcXV2Z2pw7d65edkZxcTF0Ol2D/eRYYU3BMfXHraWbg1UwKJlKZ6ivum0o+SCytvljhTYtj2a5yrT9eWFhoV6SvSXQvXt3DBo0CIMHD8bAgQNRUFCgp81FRERg+vTpmDFjBlF7aiXo09YzNb5KCmvVDqSgtZvVr8d6jhUtXHNTkdPWSQOvVdlqqZZevXrphYVYAqWlpbh8+TKCg4PRrVs3ODk54dChQ+L5tLQ0ZGVloWfPno04Sg6H0xgQaXJ1c1Rzc3OxefNmjB8/voF309grCZXirbfewvDhwxEREYEbN25gwYIFSE5OxoULF+Dv748pU6bgv//9LzZu3AhPT09MmzYNAHDy5EniPpTeGViqPA1KeOpsBRJNlcYTTltPrr6paN1hURe5eVR8Z+ABAwYYHZCdnR0OHz7c4PiqVauQkPBwsBkZGWjdurXRtowxduxYHDt2DHl5efD390efPn3w7rvvolWrVgD+Fwz85Zdf6gUDS4WcGKJuWlddmxzNj5tULTeH7cgakXMusAgy0jI0HnQpTBWAtiTI6kN6z2QeWmr+7c8N4eX1cCDAQ3sejcGwMTEk5OQgsS/QBK8aK68l21otrPmpdTE0D0o6LljHJoeW5rEupnqbDcXJqZa72rlzZ5w7d67B8aioKCQnJzc4HhYWhqVLl6JTp06oqqrCt99+Cym5OmLECNqhmA0lY3doJ9xWUrdqUTJYljZch6Y/WueEFueKFFM95lK/fZq0LmpNztA25/W3Q6/l9OnTmD9/Pq5evYpr165Bp9M1HISdHa5cuUIzDNUxZJNjXVqyeuzU8K5aG0o9WOpCYz6gnUcltXWO6ZocsZCrjSH75JNPGrx45sqVK7h+/Tp+++032TaCg4Nx8+ZNku4aHWP7yZFAm7qlhiDTknA0VTCQ2shoAo2NtUUzLi3MESu010CV5WrtDr81NTV6u/3a29ujQ4cO+PDDD2XrV1VVITw8HOXl5WjSpAlptxwOh2MS1MvVjz/+WAzJoCUiIgJpaWlWIeRIvau0XjJTA4T50/4hSl8H0rmi6dfc4SXWDO3vW9WdgbOysiSPu7i4GN2vbc2aNUhJScHy5cvF3YItFRbvai1qLjdt0ZajlHeUNqZRDtoYOhLBqWTOqyVBc11I7Z+q2ORqsbe3F3f3rU3Mr8XZ2RmjR4/GypUr4ePj06Cuj48PSkpKIAgCPDw89FLDpJwWjYkpQk4KpVKNpNrk6GPIpkYb8CsHf5CpA+l3V1WTW79+PXbv3o0FCxYgPDwcmZmZWLJkCYYNG4bOnTtj3rx50Ol0+OKLLxrUPXr0qMF2+/fvTzMM1VHbu2rLhms1bmKSIFJSTY413lGpAGWto8QDW1Uh16pVKyQnJ+stN4uKitC1a1dcvnwZN27cQNeuXWW3NbIGlM54qH9OCu5d1Yd17IbmhjY2UQ4SDdBcy05rmWOlwqgAuowH6mDgwsJC3L9/X0/IlZeXo7CwEAAQFBSEe/fuSdZ98OABlixZgi1btuDOnTsoKirCvn37cPnyZcTHx9MOxeIxNdCUpQ1Tx2BJsAb6muqsobGf0aLGfFjzHNdCe11p3rtKvQvJX/7yFzz99NPYvXs3fv31V+zatQvPPPMMRo8eDQA4duyYmENan1mzZuH48eNYs2aNaMuLjIzE2rVraYfB4XA4RFAvVysrK/HOO+/gyy+/xI0bNxASEoIXXngB8+fPh4uLC+7cuYPq6mrJZPiwsDCcO3cOPj4+ehkS3t7eoiZoKZhik6N9KmnhSawmauQ+1j8nBatTQq4ta1laqgGtB9VQfcDCEvTrEhISgqtXr8LFxUUUcqWlpYiMjER2dra5hkGEoZdL12KqvY11SWqpnjdLunlZxsLqXFDCiG5J104OU22kSppjFHc8lJSUiDY4uV1EjHU2fvx4hISE4L333hOFXGJiInJychR7i5dSkGpytMZsU3/IPJSEHFYtoT5K5K5aiyBTCiUf4qbmrhI5HkJDQ0Xh5u3trRcbB8i/yKYu//znPzFixAj4+/ujuLgYoaGh0Ol02Lt3L8kwLBLa5YuSP3ZbuWGkkPNkGpoT0huv/s3FWo82G0ZLkIZP1f/uUtfCkHeVFCIhd/78efH/V69eJW68Pv7+/jh16hR+/fVXZGZmQqfToXv37hb3voi61H+RDUmYgFRZpZayWrshTNV+aJaNSmY8SLVT2z7JHKmh5Vs6rMHuhjQ5UhSzyeXk5CA0NFSJpiwCYxkPrEsgS4qvshVYl/i0Gln9trlpQVlYHQ/UKlReXh5qamrEz7m5uZg2bRratGljtG5OTg5eeeUVPPLII2jZsqXeH4fD4agBcTDwmTNnMGrUKFy/fh1+fn7YtWsXUlJSMGvWLAwYMEDy/Q71GT9+PNzc3DB79mzid7ZK0bx5c2RmZjY4PnXqVKxevRoxMTENUsgmTZqENWvWMPdZC00IiRJpRPXb0rotRwpWRw6N3U6JFDwpGx7J2LQ0j6baI5UyI9SFeLk6YMAA9OjRA3FxcfjXv/6Fbdu2ITQ0FOvWrcOjjz5K1Jmnpyfu3r0LZ2fTEt5rY/FqSU1NxeDBg3HkyBHExMQgJiYGbdu2xeLFi8Uybm5uVJtfkm6aKecNkoJWWLF6CBv7xmnMsRgSYLRzVb+sVB+sgpebJPSh/e6qbJp57tw5HDhwAI6Ojnj33Xfx0Ucf4cyZMwgODiZtAh07dkRubi7Cw8OJ60jh7++v93nZsmVo1aqVXpK/m5sb1du5DFHf8SB3c7AGncpB84O3pJtD7bHQOIBIkNPySO1ulnT9rQ2S61n3HM07HoiFXGVlJRwdHxZ3dXWFl5cXlYADgFGjRmHEiBGIj49vsPcc64tsKisrsXnzZsycOVMvtGXLli3YvHkzgoKCMHz4cCQmJsLNzc1gOxUVFaioqBA/1/XekHj3ALrlCutS1pYxNQSE1aNJOlemamJammslVipy57J+/ACBvp8RjYVKyNV9yXRFRYXeZ8D4y6U/+eQTAMDSpfoxLnZ2dsxC7uuvv0ZhYSEmTpwoHnvxxRcRERGBkJAQpKSkYPbs2UhLS8OuXbsMtpOUlIRFixY1OJ714wfUXjLWSdXSj1wOWu2HVrDQaM2sDxhar6pcm1pcprI+xEnr0SToE9vkYmJiGgQB6zVk4OXSahMbGwtnZ2d8++23BsscPnwYAwcOREZGhsHNA6Q0OZ1OZ3CrJTl4MKj5sKQMBFNtctaMmtdaqr6q+8lZEpmZmWjZsqW4E4ohysrK4O7ujn379iE2NpaobWO5q1Jo9QdsKuYWKIY0AFpbqRrjVaNta4YkEN7UtC7LTTUgYMOGDQgICMCwYcNky9W+9JrWhsjhcKwfYpucpVFTU4MNGzYgLi5OdIgAwOXLl7F161YMHToUvr6+SElJwYwZM9CvXz906dKFup+sHz/Q+0yTncAK6VPeWjRHmpxOOUif8obaZ7WVyZUnnWva8paMkuYYc/x2rXa5un//fsTGxiItLQ1t27YVj2dnZ2P8+PFITU1FWVkZdDodnn32WcyfP58pTo5m+3NWw3N9SOO5LF24NRY03m0Sz6sSc20tDyRLQu6a2YxNTk0MBQOz3EBSx5SICNf6DaOUYCB5QNAKK2PlaNC6AFTjoUEj5Kx2uWpO1A4JqF+eNP1IiygpxGlCT0iXq3LCkdW7amvCkXS8cteaJk6Oa3IGIH3vKuuShC9JlUVJOyhNXisrtqyRs+ausnpXuZAzgDGbnBTmSN3S6s1hqm1Mrk0lYXWi0DistDSvUtA+4KXOqbrVEofD4VgT3CbHSGOFAijxlG9sjYFUI5NL+SFpkwbSJSnN0stQ+7aO3FyzOiXk4EKOEdY4KSnM7VywlhuOxDYmB4ln2tSEeyXtddYyL+ZCbv69u75G3A4XcgSo7Xmz5R83S1CvsXr1kZorNeynNLF3tgSr9qsU3PFgAEOvJKyFNfCX9cduyzeJFCzxaqyxcPya06NGKBCPk1OJ8Ji39BL0acILWCdaqh73xulDE3rAqu2RtGWuBH9LgnYZT1JPbo5NvY5ckzOAKZqcVn/caqBkPmstJJocqzFbqXknfQA29oOM9kHNKvRpBaBQXUmsyfEQEg6Ho2m4JmcA0txVVluOVoN6zQVLVoISS0sajcMWTQysAdu0pgQaTY4LOQMYWq6a6tXjDgd9SOyYptrkDNWR6t+Uc1p3WKj5W6S9V7jjQUXqT4aaBlOl2rBkWNOkaqHRzkicE8bGoPWHjhym2k1JHHJqXFeuyRnA0PbnNPmUpOdoDLpav7lYhY2cBkhyzZXIYOHCUR4S7VeOuvV4gr4CGLPJ0dxcdeE/dvXtkUqleHHPuToo8fDny1WFMVfEPO1YrPWGY80EMFVrJrWfki5rDfXHIcdUcwUJPISEw+FoGk0vV1evXo33338fubm5eOSRR/Dxxx+jR48eRHVZQkhYYQmHsAWU8nyyhjVIoabdzRbnmjWDiIeQAPjqq6/w0ksvYc2aNYiOjsaKFSuwY8cOpKWlISAgwGh90hfZNOYP0hLGQEJjhR6wel7VEGRat9Oymh1YH0T8RTYAoqOj0b17d6xa9fBC1dTUQKfTYdq0aZgzZ47R+gUFBWjWrBkc246FvaOL4uO78N2SBsc6DJtv8JyWqP2eAPt3rdtGfeq3KXVd6x+TGhNJPdIxan1O5SC5ZlLzaWgeAaCmqgJVl7YhPz8fPj4+8gMQNEhFRYXg4OAg7N69W+/4Sy+9JIwYMUKyTnl5uVBUVCT+HT58WADA//gf/7Pgv19++cWoPNCkd/Xu3buorq5GYGCg3vHAwEBcvHhRsk5SUhIWLVrU4Hj2pbPw9PD4X7m+bQz2O/endP02JcrWlpFrh7S8XH/1z2kBue9Hck7qmtBeV0NlpPpRco7lvoPWkfruC3u3xj+v1KB169ZG62tyuXrjxg2Ehobi5MmT6Nmzp3h81qxZOHr0KE6fPt2gTkVFBSoqKsTPxcXF0Ol0mNPaAS4Odg3KL/z95sN/uwYTjYmmfG3ZxqB2fI05hsag7rzUn6u614Lm+pC2yaFn7iNBWJZRbbtxcn5+fnBwcMCtW7f0jt+6dQtBQUGSdVxcXODi0tD2NvendHh6/k+Tqy+kpH6srD9kKUFYvw25c5JtUo5FizefnLAiKS8nrGT7lRGOtG2yzotWhercn9KxLLglUVlNxsk5OzujW7duOHTokHispqYGhw4d0tPsOByO9tGkJgcAM2fORFxcHB577DH06NEDK1asQFlZGV5++WWqdpL6tkHS2dwGx0meurIamYxWYW7t0Jqh1dLEMpTLTZp2xDERzj/NmGix9LmW+10rpYVq0iZXy6pVq8Rg4KioKKxcuRLR0dFEdWvj5IzZ5ORgXXaSnpMbixaXKaZeM+IlO6GdtUE9FcwHWofVxllRLRDb5DQt5EyhVsgV3byiZ5OrD6tthfZG4jeFPCTzQKo9k5yTm0fahw4XfPTYvONBSZL6ttHT5GiWnYbKGTunhOOB3zhk105qaUnUtoxwIxV8ROcITBmWTmMLeC7kCFByAiRvABV+BNZ4M7BAc+1oPcysgk9uLPX7IK0v159cm5YAq4BXCi7kKCFZrohlKZc5xtqQ+kyKpToeTI07M1bOaJusGhaJIFNSoyNwVFjqHJuKqdqeJkNIOBwOpxbueDCAMccDzdLEUDlDsEbKs/bLNQBltETWrAZrWXZaEjSOBy7kDGAohITEuKyG55Q7EsgxdP2VSM+SastQOTUegJyHFBeXwCu4JRdyplBXk/swpq3BcqRPfaU8oUpoXdYmME0dL4lgUeOhpVUNmRVWDVeqPBdyCmAsGJgWkqUMf8qzY2pOL234D6vgJGmTz7lxuJBTgLpCrm5aF2ugrxpalxoJ3VqC1dbFmvEg26aNaN1KIvcg4hkPCkBqk2NVvanDC7iWJwmNQ0bJkB4ll16GytK0rxVIvzuNJsdDSDgcjqbhmpwBWJarNCiRDqYlaENkaBwHNP1L1WcN+JZqny9b5SHV5HjuqsKQxq2ZKgBll7Ia/ZFTLeNIBQrDNaKdR9ZlJ6uZwprnXc40oFRGjxxcyDFCNGFyGoAKKUrWCLNmwyLIFLCDkrSllJCkKWfJmPodpAQ9zc7AXMgZYe5P6XpxciRJ30o+rdX4kWtBO6CBRFixCh3mZS5jVotUG9Y2j+YeL7fJGYAlTk7NgF8SD6E1Y+7votRGCyRt047FlmD97jytSwGMhZCInxUIPeC5i/LQhoCwBOWS2s9I6pFgS2EirIJM7vrP/PESDyHhcDgcwMw2ufXr16NHjx7o1KkTzp49iwkTJsDJyQkbN25E586didtJSkrCrl27cPHiRbi6uqJXr15Yvnw52rVrJ5aJiYnB0aNH9epNmjQJa9asMek7qGk/U8pAq0RbloTctaaxt6nh7GHWLjU0P8ZQyrnE6oQzq5BbsmSJ+GLn2bNnY8iQIXB3d8frr7+OI0eOELdz9OhRxMfHo3v37qiqqsK8efPw5JNP4sKFC2jatKlY7tVXX8XixYvFz25ubtRjNvTeVUuyu9iKTUdySUmZ7la/DIkAlTUtKCjITI0VtGbUtDmb1Sbn6emJ4uJilJeXIzAwELdv34ajoyP8/f2Rn5/P3O6dO3cQEBCAo0ePol+/fgAeanJRUVFYsWIFU5vm3GrJUDscckjmgzVtjtXuSooW55vVe0w6DzS5q2bV5Hx8fJCeno7U1FR069YNLi4uKC8vR01NjUntFhUVAQCaNWumd3zLli3YvHkzgoKCMHz4cCQmJhrU5ioqKlBRUSF+Li4uBmA4hIQENSPfTWlDi8jeOATzoEQwsK1o1CTQmhjUDJ8yqyb34YcfIjExEQCwadMmjB49GgcPHkRiYiJOnTrF1GZNTQ1GjBiBwsJCHD9+XDy+bt06REREICQkBCkpKZg9ezZ69OiBXbt2SbazcOFCLFq0qMFxQ/vJqfGUZ71x+M1FhpLBwHL1TdVY5Nqyloec2uO02BCSsrIy3LhxAw4ODmjZ8mG08qVLl1BZWYlOnToxtTllyhR8//33OH78OMLCwgyWO3z4MAYOHIiMjAy0atWqwXkpTU6n0ym+XOWCTB1IQkhotTWifintZ6Yuqy0Bc9iXJc/VzXiwRCFXXV0Nd3d3FBcXw8nJSZE2ExISsGfPHhw7dgwtWrSQLVtWVgZ3d3fs27cPsbGxRtsmfceDubH0G6CxoBUecrGJNBqcEpkL9fvlc/wQq4uTc3BwQOvWrU1yMNQiCAISEhKwe/duHD582KiAA4Dk5GQAQHCwtnNBORyOPmZdrq5evRpffvklZs+eDZ1OB3v7/8nYLl26ELczdepUbN26FXv27NGLjfPy8oKrqysuX76MrVu3YujQofD19UVKSgpmzJiBsLCwBrFzhjD0jgca7UAOraZnmRvZUBCKTBI1dgdRUsvTAqxhIlJlLHb787pCTW8Qdnaorq4mbsfOTjqXdMOGDZg4cSKys7Mxfvx4pKamoqysDDqdDs8++yzmz59v9ILUQprWJQWr3cWSYu8sAaVtlbT5qVLn5Po1NT9ZC5jrN0kj5MwaQmJqqEgtxuSyTqcj1tiMYTAY2MSQBalzrGhV2BEJC8qwBJJ6NGVY0WrAryVm7VAJuaqqKqxduxZ//etf0aRJE+ZOc3JykJ2djccff5y5DXOR1LcNsXdVrozJN5yU5mBlN4CSKCXYlUrvkmqLOXnfhudVDaiEnKOjI/7xj38gPj6eqbObN29i7NixOHHiBJo0aYLS0lJs374dBw4cwGeffcbUptoY0uRMhTRFiaQNWwxMZX0w1D9H24es5siYTqaluaK95qzaclLfNsRlqW1yY8aMwauvvopBgwZRDQoARo4cibZt22Lx4sUIDg5GQUEB8vLy0L17d1y5coW6PTUx9nJp2hACkqBOLf3YSaGJKaSNO2MdB2u8I02bWg0vUdJWKR6TmA+aEBJqm5yHhwdGjhyJJ598EuHh4XrOhA8//FC27okTJ7Bz5044OjqKzgNfX1/k5eXRDoPD4XCIoNbkXn75ZYPnNmzYIFu3devWOHnyJAICAtCsWTPk5+fj+vXreOKJJ3Dp0iWaYaiOsbd1mZpkTKvl2QpKfHdDGpiS4T7Gytk6JBkoppyj0eTMGkLy9ttv49SpU/jwww/Rr18/nD59GjNnzkSvXr0wb948cw2DCJbtz+WQmzCSevzm0odlCaRExgPJPNrifMhh6lK7UePkSkpK9MI5jHVWu/fbp59+irKyMjRt2hSTJ09GUlISHB0t6506pMHAcrDGXhkrZ6uokWCvZDusApcjjdw9o6qQy8zMxKuvvorjx4/rJbQDoArovXv3Lnx9fQ0G9jY2LPvJ1YfU+KrGk87abibWQGjxs4IBuKZ6XuXatJb5UAOlfueAysvVp59+Gk2bNsXcuXPRv39/HDt2DAsXLsSwYcPwt7/9zWj94uJi7N27Fzk5OQgLC8PQoUPh5eVFMwSzQOpd1TumwpLSlsNESFAyvU6uTaXali2v0flUUrjVQrNpJrWQ8/X1RWZmJtzd3eHt7Y3CwkLk5eWhb9++uHDhgmzdEydOYMSIEQgMDERERASysrKQm5uLb775Br1796YZhuqY8kpCUx0PHHpYQ0BYU/A45LDmEMvZP1XdGdjBwQEuLi4AHtrg8vPz4eXlhezsbKN14+PjsXTpUkyaNEk89tlnn2Hq1Kk4e/Ys7VA4HA7HKNSa3JNPPok333wTsbGxGDduHO7fvw83Nzekp6eLL6kxhIeHB4qKivRi66qrq+Ht7Y2SkhK2b6AShkJIajHVJsedDMrAGtLD3B9j6haJN1dLIUTMKW2EGrmqwcCff/65mGj/0UcfYe7cuSguLsamTZuM1h00aBD27duHoUOHisf279+PwYMH0w7DrLCGe9AuoUjGYK0/ehaUzg6gNRHIpW5JpeKRPMgk+5EZp9YhmgeJc3MfCSLug1rI6XQ68f9+fn5Gc05nzpwp/r9Zs2YYPXo0nnjiCURERCAzMxOHDx/Giy++SDsMs6JkEKmpeZRah1gwmKj9mGq3qz8OqX5tZc6kIPaSm+GaEQu5mTNn6qVtbdq0CXFxceLnMWPG4KuvvmpQr6CgQO/zmDFjAAD37t2Dv78/xowZo9gWTGow96d0RfJT65fhSEN7c5jjeirpFbRpwUepPbMG0NeH2CZX+87UWmrTsgydt3aMhZDUouSP1haXpCSY6iWVaodVk2PNQNH63Jrj+6keJ+fh4aHnHPDx8dHT0gwJuZKSEnh4PNyqSE4Iku7Yay6MhZDQhCAYQ6s/fKUhFTasAduG+qNN+eIanDRKZqyo4nion5lAmqkQEhIiCkdvb+8G9QRBoN7+nMPhcEihcjzUzVUVBEH8LKcM1g0Qvnr1KuMw9ZF6EXS7du1w8eJFAEB5eTnefPNNbNu2DRUVFYiNjcUnn3yCwMBARfo3Oj6FPHccfSSXgRSOHEn7KeMyV7J9AgeFkqlf1vZ7odaaZezfxcXkIWfEQq60tBTe3t7iZ0EQxM+12pgU0dHRuHHjBgBg0aJFWL9+PfHg5OjYsSMOHjwofq6b4D9jxgx899132LFjB7y8vJCQkIBRo0bhxIkTivRNCrV6boO2HDlYdhrROyYT0lO/jGSbUjY5OS+5mefIVn4TUt9TlZ2BMzMzjZaJiIhocKxuGphSzomFCxfi66+/Ft+lWpeioiL4+/tj69atGD16NADg4sWLiIyMxKlTp4jfK0HqeNAbl0KpQloIBmWFOhSE4lqzxjuSlJUak1ZzVxtL41Q9rUtKgJHw3HPPoXnz5ggPD8f9+/fRtWtXyXK///47Vbvp6ekICQlBkyZN0LNnTyQlJSE8PBxnzpzBgwcP9LZnb9++PcLDw2WFXEVFhd6uKnWFMekPmTSeigVruQFMhTph3sS4Q1M3VWCNoWPtzxKgFt4KP6wAumBgs2yaeeLECVy5cgWvvfYa1qxZI1mmbsydMb7//nuUlpaiXbt2uHnzJhYtWoScnBykpqbi22+/xcsvv9xgG6gePXpgwIABWL58uWSbUnY+ACi6eUX2lYSsHiPa6HtbxFQBRNIOSXu2rFmbG9L7yWJfLv3xxx9j2rRpirdbWFiIiIgIfPjhh3B1dWUSclKanE6nayDkamFOz+ICUBJTl0CsApHZSaSgA8HWUCK43mK3P1eT7t27Y9CgQRg8eDAGDhyIgoICPUdJREQEpk+fjhkzZhC1V9cmR/pyaZJUI1NvAFvWKlgDcOXKsLbNkUft3z6NkLOXPSvBl19+KXl827ZttE0pRmlpKS5fvozg4GB069YNTk5OOHTokHg+LS0NWVlZ6NmzZ6ONkcPhNA7UmpwhD2n9NC81eeuttzB8+HBERETgxo0bWLBgAZKTk3HhwgX4+/tjypQp+O9//4uNGzfC09NTXCKfPHmSuA/S7c/VtuVofenDGj9m6nLV1H5J++HIQ7NxRX3Hg+Le1VrBVjcIuJbLly/DyclJst6qVauQkJAAAMjIyEDr1q1JuzTI9evX8cILLyAvLw/+/v7o06cPfv75Z/j7+wMA/vnPf8Le3h7PPfecXjCwGpDuQsG6dNLSjcMcWqGQTU0JL7ncmEjMFVpHjYcybQhQfYg1OXt7e4MBv/b29liwYAHmz5/f4JyXlxeKiooAWFcSvzFNTvxsJm1NqxqdUkKKqC9KTdBcuahanVsSWMN9FI+TO3v2LK5evQpBEBAdHY1ffvlFPGdvbw9/f380adJEsm5YWBiWLl2KTp06oaqqCt9++61kGtiIESNIhtLosHrjxHOMT3mt3gBKGKHrt2NojkyNVTQ2Dpp4MKn0JS1Aa1owNRaRBCJNrq4G1qZNG6SnpxN3cPr0acyfPx9Xr17FtWvX9DbdFAdhZ4crV65QDFt9jL13VQpbfiKTQLtsNLUtqrGZOXrfGjE1k0PyHOODSPG3dYWEhGDnzp3o1KkTwsLCkJOTI6mNGessODgYN29ax4/DlHc8UEfta+jGYRVWsmUo7V+G6rGmdaktlFnbbGxINTKlhGPd+jTBwETL1UWLFmHQoEFisGzd+DOAbLukqqoqhIeHo7y83ODSlsPhcJSG2PFQVVWFmzdvIjIyEufPn5csYyy/NSIiAmlpaVYh5AwFA9dC4pWzRQ+qGqiZDSHbjok5l7YMq5ZH6gBSNeMhOTkZUVFRNFVE1qxZg5SUFCxfvlzcLdhSMeXl0uJnufASFSLCrRGa78x6PaXq0JwzVM7YmGwZUuHPOkc0cXJMaV0bNmzAli1bcPv2baSkpODo0aO4desWnn/+edl6Pj4+Yoydh4eH3vtXzRVITIqxtC7xswI/bC3aa+QwVVjpladwPLAKORLPrWR/KtusLBnWh7hFaHLvvvsuvvrqK7z++ut46623UFhYiEuXLmHcuHH49ddfZesePXrU4Ln+/fvTDEN1WDQ5ObT0A1YTWucAa9tKadZck3sIiyPIULn65aXKKO5drUuLFi3w008/ISwsTHyZTU1NDfz8/CxOGzMFY5tmqukl5TeOPqba1Ei0AyW0i/rnbBlaIUd7zVRdrgYEBODmzZtwcHAQ81UrKirQvHlzo+EhDx48wJIlS7BlyxbcuXMHRUVF2LdvHy5fvoz4+HiaYagO1+QsB1MDTGmFpFJLS/6wegjJXNHOMY0mR70LyeOPP94gD3T9+vXo3bu30bqzZs3C8ePHsWbNGjFFLDIyEmvXrqUdBofD4RBBrclduXIFAwcORLNmzXDu3Dk89thjuHXrFg4ePIgWLVrI1g0LC8O5c+fg4+Ojt2uJt7c3CgsLmb+EGhjLeLCV4F5LhTWImKQ+q8ODBC38NmizTVijB+SuvyrvXa2lZcuWuHDhAr777jsxTevpp59G06ZNjdatqamBm5ub3rHS0lKLDidJ6ttGOuNBoZxLU9qyRKzlJiZZJtUvK9mODecgK/HdaWIa64eQkEIt5ADA1dVVfBMWAFRWVqJly5ZG80+feOIJJCYm4r333hOPLV++HIMHD2YZhlmY+5N0nq6SicimagyWJFjMNQaa68mqtVnC9bREWNOzqOuZaFutRZHtzysqKuDq6oqamhrZcnfu3MGIESOQkZGBgoICBAYGQqfTYe/evfDz8zN1GIpiKHdV1ac8XwIrEkJiaI5Yc1dJ+2c1sNsKVrNcNYShvebq4u/vj1OnTuHXX39FZmYmdDodunfvrhcUbInQPuXVSCOyyZuCMa2qfn01kvdpz8mNSYuBwsTXkzG+zuyvJKyoqICbm5tsgr61wfIiGxp4eMFDaIzYtMGkJPXUEDBa1ciV0lRps1rqlwVU2IUEAFauXGnwXFVVFVEbOTk5SExMxJkzZ1BSUqJ3ztL2k+NwONqAWJMbMGCA0TJHjhwx2oabmxvGjRvXwBv7zDPPkAwDANC8eXNkZmY2OD516lSsXr0aMTExDVLIJk2aZPDF1lIYy3iQw9yagxYgceSwtil+ZrSfsgavcqRRInfZYt+76unpibt378LZ2dmkdu7cuaO3NE5NTcXgwYNx5MgRxMTEICYmBm3btsXixYvFMm5ubkYvRl1IhZy5lyRaXebSxlex2nLk+jNUT8l8WmsRhEqOkzaurkEZAzY5xd/WpQQdO3ZEbm4uwsPDTWqn9q1ctSxbtgytWrXSS/J3c3NDUBC5cbKiokLcFBSAwRfuyN5UMqELSgWPWvrNUQutvYb2YUHrMTU4Tin7EoUGyNqfpaNGsDPt/MvVm/tTOpYFtyQak1k1uffffx9btmxBfHw8AgMD9c6xvsimsrISISEhmDlzJubNmwcAiImJwfnz5yEIAoKCgjB8+HAkJiY2CESuy8KFC7Fo0aIGx2kS9MVzVvK0tiSUjKJnzS+t3zZNGY4+tM4FWi3PYperhtK+THmRzfbt2/Hiiy8iKysLISEhAIB169YhIiICISEhSElJwezZs9GjRw/s2rXLYDtSmpxOpzMYJ6dULJVkPY0uSWkxVViJ5xjj5KTaJtJK+UNOD1OD5KXqqbrVkqURGxsLZ2dnfPvttwbLHD58GAMHDkRGRgZatWpF1K6x967yH7L6sIYsGBJmJGUMlec0RImMHlYaJRi4McjMzMTBgwdlNTQAiI6OBgAqIVfL3J/S9RP05TQGLvgkUcpLSiqkTE0HInFmsEbva+m3QfzdVVj+0wQDW3aqgRE2bNiAgIAADBs2TLZccnIygIevRORwOLaF1WpyNTU12LBhA+Li4uDo+L+vcfnyZWzduhVDhw6Fr68vUlJSMGPGDPTr1w9dunSh7sfgLiQKPuVpsEZ7Hc04lVza0NiCqENQKB1P1jJXJCipxaqhNdfHaoXcwYMHkZWVhVdeeUXvuLOzMw4ePIgVK1agrKwMOp0Ozz33HObPn6/KOJS4KakM7FZ4s7CmZ1HdAJT1DZWVPEf5YLFmz7tSqWym2k/l+qDF6h0PamHsvask2HLoAa3XmaSebH8KBgobaodjHNbAX9rfBI131Wo1OXOR1LeNpHdV/EzqYbIxr6ypmkDdY1JtEgkyhdK6SMdrqF/SNm0F1swHVriQo0SpCHuOPrRR8DRtkixpSerrHSOx89ngXBPFjxJeT5o25eDLVQOQvq3L1LAIU9rQIkpoVIa0LNqgXtJx0oxXC9AGQtMGwDc4J1GPJnfVqkNIOBwOxxh8uUoJlcdOQc1BSxCnV1Gk0OnVU8GrakofWoFZ61XIjs0KF3KM0OYscuH2P1gNz6amZSkhXG35YUVzPWVta4zxh6xwIccI0e4VjPFAtgiN/c3YOZokermxsIY82Aq03mraoGClrjF3PBjA2KaZtFH0Wr8paIzLrDmotG0ya3IU34FrecahCQWSq1cXHienIjTami39yIk8mYzpVZL9UcTJmRyKQhkmYivzrkQmiDkeDFzIGcFY7irtj1zrT3kijU4uqFdBeybrslPrc0QC6zxKnjNRAEr1QbMzMA8h4XA4mobb5Axgzk0zbU1zUDI/VeocS7/c7GAaSgVVk9rraIKBuZAzAMvLpfnNQf9jN1XgkTgeWPNMbTmrgQTieDeFvNx161vs27qsEaVtchxpWINypcrQXH9WexGH3iYn+UBiDK6ngQs5SljDGjjqQOI4YHZAcG1dFlItmGQeWAPESeDLVQMY20+Oe+zYYU3PIrXXkPRDU4+2X61C891Z7Z9S5aTK2NTbutTCUDCwLf64WZALktUrRyDI6pc11Jax/rhzQX2IMoEoNTmp+aR5WxcPIeFwOJqG2+QIoF0Sca1AGhqNTrI+az2CgFaenqUMpjpySFcANPDlqgH4ctU4SuTm0uzcQmvolhqnwXHweTUJ1tAqGgHGGkLChZwBioqK4O3tjexLZ7F6aNcG5+f+lN4Io7Jukvq2ASB97WrP1aW2nFQ9qfLG6kudYx2vrVD32sldB6XmQ66/uufi//s7dG0fQWFhIby8vGT75ULOANevX4dOp2vsYXA4HBmys7MRFhYmW4YLOQPU1NQgLS0NHTp0QHZ2tlGV2JIoLi6GTqezunED1jt2ax03YJ1jFwQBJSUlCAkJgb29vP+UOx4MYG9vj9DQUACAp6en1Ux+Xax13ID1jt1axw1Y39iNLVNr4SEkHA5H03Ahx+FwNA0XcjK4uLhgwYIFcHFxaeyhUGGt4wasd+zWOm7AusdOAnc8cDgcTcM1OQ6Ho2m4kONwOJqGCzkOh6NpuJDjcDiahgs5A6xevRrNmzdHkyZNEB0djV9++aWxh9SApKQkdO/eHR4eHggICMDIkSORlpamV6a8vBzx8fHw9fWFu7s7nnvuOdy6dauRRizNsmXLYGdnh+nTp4vHLHncOTk5GD9+PHx9feHq6orOnTvjt99+E88LgoC3334bwcHBcHV1xaBBg5Ce3rj5r9XV1UhMTESLFi3g6uqKVq1a4Z133kFdv6MljlsRBE4Dtm3bJjg7Owvr168Xzp8/L7z66quCt7e3cOvWrcYemh6xsbHChg0bhNTUVCE5OVkYOnSoEB4eLpSWloplJk+eLOh0OuHQoUPCb7/9Jjz++ONCr169GnHU+vzyyy9C8+bNhS5dughvvPGGeNxSx52fny9EREQIEydOFE6fPi1cuXJF+OGHH4SMjAyxzLJlywQvLy/h66+/Fs6ePSuMGDFCaNGihXD//v1GG/e7774r+Pr6Cnv37hWuXr0q7NixQ3B3dxc++ugjix63EnAhJ0GPHj2E+Ph48XN1dbUQEhIiJCUlNeKojHP79m0BgHD06FFBEAShsLBQcHJyEnbs2CGW+fPPPwUAwqlTpxprmCIlJSVCmzZthAMHDgj9+/cXhZwlj3v27NlCnz59DJ6vqakRgoKChPfff188VlhYKLi4uAhffvmlOYYoybBhw4RXXnlF79ioUaOEcePGCYJgueNWAr5crUdlZSXOnDmDQYMGicfs7e0xaNAgnDp1qhFHZpyioiIAQLNmzQAAZ86cwYMHD/S+S/v27REeHm4R3yU+Ph7Dhg3TGx9g2eP+5ptv8Nhjj+Evf/kLAgIC8Oijj+Kzzz4Tz1+9ehW5ubl6Y/fy8kJ0dHSjjr1Xr144dOgQLl26BAA4e/Ysjh8/jqeeegqA5Y5bCXiCfj3u3r2L6upqBAYG6h0PDAzExYsXG2lUxqmpqcH06dPRu3dvdOrUCQCQm5sLZ2dneHt765UNDAxEbm7D1yyak23btuH333/Hr7/+2uCcJY/7ypUr+PTTTzFz5kzMmzcPv/76K15//XU4OzsjLi5OHJ/U76cxxz5nzhwUFxejffv2cHBwQHV1Nd59912MGzcOACx23ErAhZxGiI+PR2pqKo4fP97YQzFKdnY23njjDRw4cABNmjRp7OFQUVNTg8ceewxLly4FADz66KNITU3FmjVrEBcX18ijM8z27duxZcsWbN26FR07dkRycjKmT5+OkJAQix63EvDlaj38/Pzg4ODQwJN369YtBAUFNdKo5ElISMDevXtx5MgRvQ0Eg4KCUFlZicLCQr3yjf1dzpw5g9u3b6Nr165wdHSEo6Mjjh49ipUrV8LR0RGBgYEWOW4ACA4ORocOHfSORUZGIisrCwDE8Vna7+fvf/875syZg7Fjx6Jz586YMGECZsyYgaSkJACWO24l4EKuHs7OzujWrRsOHTokHqupqcGhQ4fQs2fPRhxZQwRBQEJCAnbv3o3Dhw+jRYsWeue7desGJycnve+SlpaGrKysRv0uAwcOxLlz55CcnCz+PfbYYxg3bpz4f0scNwD07t27QZjOpUuXEBERAQBo0aIFgoKC9MZeXFyM06dPN+rY792712BzSQcHB9TU1ACw3HErQmN7PiyRbdu2CS4uLsLGjRuFCxcuCK+99prg7e0t5ObmNvbQ9JgyZYrg5eUl/Pjjj8LNmzfFv3v37ollJk+eLISHhwuHDx8WfvvtN6Fnz55Cz549G3HU0tT1rgqC5Y77l19+ERwdHYV3331XSE9PF7Zs2SK4ubkJmzdvFsssW7ZM8Pb2Fvbs2SOkpKQIzzzzTKOHYsTFxQmhoaFiCMmuXbsEPz8/YdasWRY9biXgQs4AH3/8sRAeHi44OzsLPXr0EH7++efGHlIDAEj+bdiwQSxz//59YerUqYKPj4/g5uYmPPvss8LNmzcbb9AGqC/kLHnc3377rdCpUyfBxcVFaN++vbBu3Tq98zU1NUJiYqIQGBgouLi4CAMHDhTS0tIaabQPKS4uFt544w0hPDxcaNKkidCyZUvhH//4h1BRUSGWscRxKwHfaonD4WgabpPjcDiahgs5DoejabiQ43A4moYLOQ6Ho2m4kONwOJqGCzkOh6NpuJDjcDiahgs5DoejabiQ4zQaS5cuxQsvvGDWPidPnoxPPvnErH0CwLVr19C+fXtUVFSYvW9bh2c8cFTD3d1d/P/9+/fh6OgIJycnAEDfvn3x/fffm3U8GRkZGDBgAC5fvgxnZ2fZsjExMRg5cqTeeydM5eWXX0ZUVBTeeOMNxdrkGIdrchzVKC0tFf/69u2L5cuXi5/NLeAAYM2aNRgzZoxRAacWcXFxWLVqVaP0bctwIcdpNBYuXIiRI0eKn+3s7LBq1Sp06NABTZs2xYQJE1BQUIAxY8bA09MTjz76qN7uzKWlpUhISEB4eDgCAgLw0ksviVvAS/HNN9/giSeeED/n5+fj2WefhY+PD7y9vdGtWzdkZmbizTffxE8//YTZs2fD3d1d3CJcrr9r167Bzs4On332GZo3bw5fX19MnToVlZWVYn+9e/fG9evX8eeffyp1CTkEcCHHsSj27NmD48ePIz09Hfv370f//v0xbdo05OfnIyoqCrNmzRLLvvLKK8jPz0dKSgquXr2KBw8eICEhQbLde/fuIT09He3btxePffDBB6iqqkJOTg7y8vLw+eefw8PDA//3f/+np3nWap0k/e3evRvJyck4d+4cTp48KW5KCQBOTk5o3bo1kpOTFbxiHGNwIcexKN566y00a9YMISEh6N+/Pzp27Ig+ffrA0dERf/nLX/D7778DAO7cuYP//Oc/WL16Nby9vdG0aVMsXrwYX331Faqrqxu0W1BQAADw9PQUjzk5OSEvLw/p6elwcHBAVFSU+BKg+pD2t3DhQnh7eyMkJARz587Fv//9b712PD09xbFwzAN/xwPHoqj7IhU3Nze9l9m4ubmhtLQUwMPlYU1NTYPdkO3t7ZGbm4vQ0FC94z4+PgAe7nbr5+cH4OGW4OXl5Xj++edRVFSEMWPGYNmyZXB1dW0wLmP91VK7Q3Dt/3NycvTKFxcXi2PhmAeuyXGsEp1OB3t7e9y4cQOFhYXiX3l5eQMBBzwUkG3atNGz6bm7u2P58uVIS0vDqVOncOjQITG8pP5W4aT9ZWZmiv/PysrSO/fgwQNkZGQgKipKqcvAIYALOY5VEhQUhJEjRyIhIQF3794F8PC1ert37zZYZ/jw4Thy5Ij4ee/evbh06RJqamrg6ekJJycnODo+XNwEBgbi8uXL1P0tXrwYhYWFuHHjBpKSksRX/gHAyZMnERoaisjISNMvAIcYLuQ4VsvGjRvh7e2N7t27w9PTE3379sWZM2cMlp80aRK2bduGBw8eAHgYNzdkyBB4eHigQ4cO6NmzJ6ZMmQIAmD59Og4ePAhvb288/fTTxP0988wziIqKQqdOnRAdHY158+aJ57744gvEx8crfRk4RuDBwBybYtKkSYiKihKFmVJcu3YNLVq0QEFBQYOXYgMPl7FDhgxBcnIyXFxcFO2bIw8XchyOAhgTcpzGgy9XORyOpuGaHIfD0TRck+NwOJqGCzkOh6NpuJDjcDiahgs5DoejabiQ43A4moYLOQ6Ho2m4kONwOJqGCzkOh6Np/h834JI9ra146gAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "##### **Key Observations:**\n", + "\n", + "1. **Examining the Input Signal and Spike Train for a Single IPD:**\n", + " - For a specific IPD, the input signal and its corresponding transformed spike train are plotted for the **first auditory nerve fiber of each ear**.\n", + " - The **IPD** is applied to the left ear, causing a **phase shift** in its spike pattern relative to the right ear. This phase shift is critical for encoding spatial auditory information.\n", + "\n", + "2. **Effect of `envelope_power`:**\n", + " - Values of `envelope_power` greater than 1 introduce a **nonlinearity** to the signal, sharpening the envelope of the stimulus.\n", + " - Sharper envelopes make the patterns more distinguishable, which can potentially help the neural network in learning to classify the IPDs more accurately.\n", + "\n", + "3. **Spike Train Visualization for All Nerve Fibers:**\n", + " - For a selected IPD value, the spike trains of all auditory nerve fibers are displayed:\n", + " - The **first 100 rows (blue)** correspond to the right ear.\n", + " - The **next 100 rows (orange)** correspond to the left ear.\n", + " - **Phase Delays by Fiber Index**:\n", + " - Nerve fibers with **higher indices** introduce **stronger phase delays**. This is modeled to reflect how real auditory nerve fibers respond differently based on their spatial distribution.\n", + " - The **IPD application** to the left ear introduces a phase shift in its fibers relative to the right ear's fibers." + ], + "metadata": { + "id": "W7iEqftShXbi" + }, + "id": "W7iEqftShXbi" + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "---\n", + "## Classification Approach\n", + "\n", + "---\n", + "---\n", + "\n", + "\n", + "\n", + "\n" + ], + "metadata": { + "id": "DFlMjrfZih-O" + }, + "id": "DFlMjrfZih-O" + }, + { + "cell_type": "markdown", + "id": "177a735f", + "metadata": { + "id": "177a735f" + }, + "source": [ + " The objective is to take the input spike data and infer the **Interaural Phase Difference (IPD)** using a neural network. To achieve this, we will:\n", + "\n", + " 1. **Discretize the IPD range** into categories (segments). \n", + " 2. **Train a neural network** to predict the category (segment) to which the input belongs.\n", + "\n", + " This classification approach simplifies the continuous IPD estimation problem by transforming it into a discrete class prediction task, making it computationally efficient and suitable for neural network-based learning.\n", + "\n", + " ---\n", + "\n", + " ### **Step 1. Discretizing the IPD Range**\n", + "\n", + " To prepare the data for classification, we divide the continuous IPD range $[- \\pi/2, \\pi/2]$ into $N_c$ equal-width segments. Each segment corresponds to a class, effectively mapping the continuous IPD into discrete categories.\n", + "\n", + " For a given angle $\\phi$, the **class index** is calculated as:\n", + " $$\n", + " \\text{Class Index} = \\text{floor} \\left( \\frac{(\\phi + \\pi/2) \\cdot N_c}{\\pi} \\right)\n", + " $$\n", + "\n", + " #### **Here:**\n", + " - $N_c$: Number of classes (or segments). \n", + " - $\\phi$: Continuous IPD value to be discretized. \n", + "\n", + " ---\n", + "\n", + " ### **Step 2. Neural Network Prediction**\n", + "\n", + " The neural network takes the **input spike data** and outputs a **probability vector** $\\mathbf{y}$ of length $N_c$, where: \n", + " - Each element $y_i$ represents the network’s confidence that the input belongs to class $i$. \n", + " - The predicted class is the index of the maximum value in $\\mathbf{y}$: \n", + " $$\n", + " i_{\\text{est}} = \\text{argmax}_i \\, y_i\n", + " $$\n", + "\n", + " ---\n", + "\n", + " ### **Step 3. Reconstructing the IPD**\n", + "\n", + " To reconstruct the continuous IPD value $\\phi$ from the predicted class index $i_{\\text{est}}$, we compute the midpoint of the corresponding segment: \n", + " $$\n", + " \\phi_i = a + \\left(i + \\frac{1}{2}\\right) \\frac{(b - a)}{N_c}\n", + " $$\n", + "\n", + " #### **Here:**\n", + " - $a = -\\pi/2$ and $b = \\pi/2$ are the bounds of the IPD range. \n", + "\n", + " ---" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f817078", + "metadata": { + "id": "3f817078", + "outputId": "ec9f0a43-7900-4d02-952a-aef22390bebe", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Number of classes = 12\n" + ] + } + ], + "source": [ + "# classes at 15 degree increments\n", + "num_classes = 180//15\n", + "print(f'Number of classes = {num_classes}')\n", + "\n", + "def discretise(ipds):\n", + " \"\"\"\n", + " Discretize Interaural Phase Differences (IPDs) to generate class labels.\n", + "\n", + " The function maps IPDs, which are continuous values in the range (-pi/2, pi/2),\n", + " to discrete classes in the range [0, num_classes-1]. The resulting discrete values\n", + " are suitable for classification tasks.\n", + "\n", + " Parameters\n", + " ----------\n", + " ipds : Tensor\n", + " A tensor containing continuous IPD values. The values should be in the range (-pi/2, pi/2).\n", + "\n", + " Returns\n", + " -------\n", + " Tensor\n", + " A tensor containing the classification of IPD values, in the range [0, num_classes-1].\n", + "\n", + " Notes\n", + " -----\n", + " - Assumes the input `ipds` is a PyTorch tensor.\n", + " - `num_classes` should be defined in the surrounding scope.\n", + " - The output tensor will have the same shape as the input `ipds`.\n", + "\n", + " Examples\n", + " --------\n", + " >>> ipds = torch.tensor([-np.pi/2, 0, np.pi/2])\n", + " >>> ipd_indices = discretise(ipds)\n", + " \"\"\"\n", + " return ((ipds+np.pi/2)*num_classes/np.pi).long() # assumes input is tensor\n", + "\n", + "def continuise(ipd_indices): # convert indices back to IPD midpoints\n", + " \"\"\"\n", + " This function maps IPD indices, which are discrete values in the range [0, num_classes-1],\n", + " back to continuous IPD values. The resulting continuous values are suitable for\n", + " representing the midpoints of the original IPD ranges in the continuous domain.\n", + "\n", + " Parameters\n", + " ----------\n", + " ipd_indices : array-like\n", + " An array or tensor of IPD indices, which are discrete values obtained from\n", + " discretizing continuous IPDs into `num_classes` bins by the function discretise(ipds).\n", + "\n", + " Returns\n", + " -------\n", + " array-like\n", + " An array or tensor of continuous IPD midpoints, corresponding to the provided\n", + " `ipd_indices`. The midpoints are computed based on the assumed discretization\n", + " strategy, and are in the range (-pi/2, pi/2).\n", + "\n", + " Notes\n", + " -----\n", + " - `num_classes` should be defined in the surrounding scope and should be the same\n", + " value that was used for discretization.\n", + " - The input `ipd_indices` and the output will have the same shape.\n", + " - The output type (e.g., NumPy array, PyTorch tensor) will match the input type.\n", + " \"\"\"\n", + " return (ipd_indices+0.5)/num_classes*np.pi-np.pi/2" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Let's test these two functions and display the found classes and the midpoints in a table\n" + ], + "metadata": { + "id": "33amh8o4i1DB" + }, + "id": "33amh8o4i1DB" + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "\n", + "ipds, spikes, _ = random_ipd_input_signal(num_samples=10)\n", + "ipd_indices = discretise(ipds)\n", + "classes_ipd_midpoints = continuise(ipd_indices)\n", + "\n", + "data = {'ipds':ipds,\n", + " 'classes':ipd_indices,\n", + " 'classes_ipd_midpoints':classes_ipd_midpoints}\n", + "data_df = pd.DataFrame(data)\n", + "data_df" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "id": "Lmfd4Zm2Z94a", + "outputId": "c178fe11-9892-4961-dc22-d22997bab9bc" + }, + "id": "Lmfd4Zm2Z94a", + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " ipds classes classes_ipd_midpoints\n", + "0 -0.115562 5 -0.130900\n", + "1 -0.737785 3 -0.654499\n", + "2 0.239584 6 0.130900\n", + "3 -1.261090 1 -1.178097\n", + "4 -1.454595 0 -1.439897\n", + "5 0.980046 9 0.916298\n", + "6 -0.171898 5 -0.130900\n", + "7 -0.274938 4 -0.392699\n", + "8 -0.512133 4 -0.392699\n", + "9 0.220898 6 0.130900" + ], + "text/html": [ + "\n", + "
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These models will allow us to explore different computational approaches to neural processing:\n", + "\n", + "1. **Artificial Neural Network (ANN)**: \n", + " - This network uses continuously operating neurons. Each neuron functions as a *leaky integrator*, meaning its membrane potential integrates incoming inputs over time but naturally decays back to its resting potential with a specific time constant. \n", + " - Unlike biological neurons, ANN neurons do not produce action potentials (spikes); instead, their activity remains continuous, making them mathematically simpler to model. \n", + " - These neurons operate under differentiable dynamics, enabling us to train the network using standard backpropagation techniques, as in traditional machine learning models.\n", + "\n", + "2. **Spiking Neural Network (SNN)**: \n", + " - In this model, we incorporate neurons that emit *spikes* when their membrane potential crosses a fixed threshold. After firing a spike, the membrane potential is reset to its resting state, mimicking the behavior of biological neurons. \n", + " - This spiking mechanism introduces a time-sensitive aspect to the computation, enabling the network to potentially detect coincident inputs more effectively than an ANN. \n", + "\n", + "By comparing the performance of these two models, we will explore whether the spiking mechanism in the SNN offers advantages for solving tasks that require precise timing, such as sound localization. \n", + "\n", + "Before diving into the implementation, let’s start with some general background to build intuition and motivation for this approach." + ], + "metadata": { + "id": "VxTwe_n3vBTr" + }, + "id": "VxTwe_n3vBTr" + }, + { + "cell_type": "markdown", + "source": [ + "## Background:" + ], + "metadata": { + "id": "bxucf5IKyLwc" + }, + "id": "bxucf5IKyLwc" + }, + { + "cell_type": "markdown", + "source": [ + "In this section, we will introduce the fundamentals of artificial neural networks (ANNs) and how they are trained. Neural networks are a class of machine learning models that mimic the structure and function of biological neural systems, using interconnected layers of computational units (neurons) to process data. These models are particularly effective for tasks involving complex, non-linear patterns, such as sound localization." + ], + "metadata": { + "id": "sLHyKiv-8WVt" + }, + "id": "sLHyKiv-8WVt" + }, + { + "cell_type": "markdown", + "source": [ + "### ### **Slide 1: Anatomy of a Single Neuron in an ANN**\n", + "\n", + "\n" + ], + "metadata": { + "id": "INmyCJMW8cfM" + }, + "id": "INmyCJMW8cfM" + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "\n", + "![Screenshot 2024-11-18 at 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)" + ], + "metadata": { + "id": "H_w0n9sD3ZKH" + }, + "id": "H_w0n9sD3ZKH" + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "1. **Inputs and Weights**: \n", + " - Each neuron receives inputs ($x_1, x_2, \\ldots$) representing features of the data, such as sound frequency or amplitude in our sound localization task.\n", + " - Each input is assigned a weight ($w_{11}, w_{21}, \\ldots$), which determines its contribution to the neuron’s output. These weights are learned during training.\n", + "\n", + "2. **Summation**: \n", + " - The neuron calculates a weighted sum of its inputs: \n", + " $$\n", + " z = \\sum w_i x_i\n", + " $$\n", + " This summation is a linear operation, forming the basis for the neuron’s computation.\n", + "\n", + "3. **Activation Function**: \n", + " - To capture complex, non-linear relationships, the weighted sum is passed through an activation function ($f(z)$), such as Sigmoid or ReLU (Rectified Linear Unit). \n", + " - The activation function introduces non-linearity, allowing the network to model intricate patterns in the data.\n", + "\n", + "4. **Prediction**: \n", + " - The output of the neuron, $y'$, represents the prediction or decision of the network for this step. It will be compared to the actual value ($y$) during training.\n", + "\n", + "5. **Loss Function**: \n", + " - To measure how far off the prediction ($y'$) is from the true value ($y$), a loss function is used. For example, the Mean Squared Error (MSE) calculates the average squared difference between $y$ and $y'$: \n", + " $$\n", + " L = \\frac{1}{2}(y' - y)^2\n", + " $$\n", + "\n", + "6. **Training the ANN: Backpropagation and Weight Updates**: \n", + " - Network training aims to minimize the error between the prediction and the true value. This is achieved by minimizing the loss function using gradient descent.\n", + " - During training, the network adjusts the weights by calculating the gradient of the loss function with respect to each weight ($\\partial L / \\partial w$). \n", + " - During training, the network updates its weights to reduce the error between its predictions and the true values. This is done by calculating the gradient of the loss function with respect to each weight ($\\partial L / \\partial w$). The gradient tells us the direction and magnitude of the change needed in the weight to minimize the loss function.\n", + "\n", + " To control how much the weights are adjusted in each step, a **learning rate** ($\\eta$) is introduced. The learning rate is a small positive value that determines the step size for weight updates. Combining the gradient and the learning rate, the weight update rule can be written as:\n", + "\n", + " $$\n", + " w_{\\text{new}} = w_{\\text{old}} - \\eta \\frac{\\partial L}{\\partial w}\n", + " $$\n", + "\n", + " Here:\n", + " - $w_{\\text{new}}$: The updated weight.\n", + " - $w_{\\text{old}}$: The current weight before the update.\n", + " - $\\eta$: The learning rate.\n", + " - $\\frac{\\partial L}{\\partial w}$: The gradient of the loss function with respect to the weight.\n", + "\n", + " The learning rate plays a crucial role in training:\n", + " - If $\\eta$ is too large, the updates may overshoot the optimal weights, causing the training process to diverge.\n", + " - If $\\eta$ is too small, the updates will be very slow, and the training may take a long time to converge.\n", + "\n", + " By iteratively adjusting the weights using this rule, the network gradually reduces the loss function, improving its predictions over time. This process is known as **gradient descent**.\n", + "\n", + "\n", + "\n", + "\n" + ], + "metadata": { + "id": "tXGMrKFu9C2G" + }, + "id": "tXGMrKFu9C2G" + }, + { + "cell_type": "markdown", + "source": [ + "### **Slide 2: Multi-Layer Networks**" + ], + "metadata": { + "id": "RbbnjUqN_mGv" + }, + "id": "RbbnjUqN_mGv" + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "![Screenshot 2024-11-18 at 02.26.03.png](data:image/png;base64,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)" + ], + "metadata": { + "id": "wBNMiqoI4XVv" + }, + "id": "wBNMiqoI4XVv" + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "This slide expands the single-neuron concept into a multi-layer network, introducing the idea of layers:\n", + "\n", + "1. **Input Layer**: \n", + " - The input layer receives raw data ($x_1, x_2, x_3, x_4$), which are features relevant to the task at hand.\n", + "\n", + "2. **Hidden Layers**: \n", + " - Hidden layers are composed of multiple interconnected neurons. Each layer processes the data and extracts features at increasingly abstract levels. \n", + " - The combination of weighted summation, activation functions, and interconnected neurons enables the network to approximate complex functions.\n", + "\n", + "3. **Output Layer**: \n", + " - The output layer produces the final predictions ($y_1, y_2, \\ldots$) based on the processed information from the hidden layers. \n", + " - The number of neurons in the output layer corresponds to the number of classes or outputs in the task (e.g., e.g., in our case, the classes represent the segments of the interaural phase difference, which are related to the angles of sound source localization).\n", + "\n", + "4. **Non-Linearity and Power of Deep Networks**: \n", + " - By stacking multiple layers, the network gains the capacity to model highly non-linear and intricate relationships in data, which are essential for tasks like sound localization.\n" + ], + "metadata": { + "id": "Cyr6_4dW_YGj" + }, + "id": "Cyr6_4dW_YGj" + }, + { + "cell_type": "markdown", + "id": "2b857913", + "metadata": { + "id": "2b857913" + }, + "source": [ + "## Implementation 1: Membrane only (no spiking) neural network (ANN)\n", + "\n", + "Before we get to spiking, we're going to warm up with a non-spiking network that shows some of the features of the full model but without any coincidence detection, it can't do the task. We basically create a neuron model that has everything except spiking, so the membrane potential dynamics are there and it takes spikes as input. The neuron model we'll use is just the LIF model we've already seen. We'll use a time constant $\\tau$ of 20 ms, and we pre-calculate a constant $\\alpha=\\exp(-dt/\\tau)$ so that updating the membrane potential $v$ is just multiplying by $\\alpha$ (as we saw in the first notebook). We store the input spikes in a vector $s$ of 0s and 1s for each time step, and multiply by the weight matrix $W$ to get the input, i.e. $v\\leftarrow \\alpha v+Ws$.\n", + "\n", + "We initialise the weight matrix $W$ uniformly with bounds proportionate to the inverse square root of the number of inputs (fairly standard, and works here).\n", + "\n", + "The output of this will be a vector of $N_c$ (``num_classes``) membrane potential traces. We sum these traces over time and use this as the output vector (the largest one will be our prediction of the class and therefore the IPD).\n", + "\n", + "![Membrane only architecture](https://github.com/neural-reckoning/cosyne-tutorial-2022/blob/main/arch-membrane.png?raw=1)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Functions to construct the model and calculte output form input samples\n", + "\n", + "1. **The `init_weight_matrix()` function**: \n", + " - This function initializes the weight matrix, which defines the connections between neurons in the network. By specifying all the synaptic connections, it sets up the neural network's architecture. \n", + " - The implementation is done using PyTorch, where vectors and matrices are represented as **tensors**. Tensors are more than simple arrays; they also store information about gradients, enabling efficient computations during backpropagation and optimization. \n", + " - ```\n", + " W = nn.Parameter(torch.empty((input_size, num_classes), device=device, dtype=dtype, requires_grad=True))\n", + " ```\n", + "\n", + " - Each element of W represents a weight associated with a specific connection between an input unit and an output neuron.\n", + " - The element in the i-th row and j-th column corresponds to the weight connecting the i-th input to the j-th output neuron.\n", + " - If you need to visualize the weight matrix or any output vector, remember to **detach** the tensor first. Detaching removes the gradient-tracking capabilities, converting the tensor into a plain array suitable for visualization or further non-gradient-based processing: \n", + " \n", + "\n", + "\n", + " ```\n", + " numpy_array = tensor_A.detach().numpy()\n", + " ```\n", + "\n", + "\n", + "\n", + "2. **The `membrane_only(input_spikes, W, tau=20*ms)` function**: \n", + " - This function simulates the neural network's activity by calculating its output for a given input configuration. It models how the network processes input spikes using the defined weight matrix (`W`) and a membrane time constant (`tau`), capturing the temporal dynamics of the neurons." + ], + "metadata": { + "id": "fdyjjx9X0C7w" + }, + "id": "fdyjjx9X0C7w" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c7c60d9", + "metadata": { + "id": "7c7c60d9" + }, + "outputs": [], + "source": [ + "# Weights and uniform weight initialisation\n", + "def init_weight_matrix():\n", + " \"\"\"\n", + " Initialize a weight matrix for a neural network layer using uniform distribution.\n", + "\n", + " The function initializes a weight matrix, `W`, with dimensions `(input_size, num_classes)`.\n", + " The matrix is initialized using a uniform distribution over `[-bound, bound]`, where\n", + " `bound` is computed as the inverse of the square root of the fan-in (number of input units).\n", + "\n", + " This initialization method helps in achieving faster convergence during training by\n", + " setting initial weights in a range that's inversely proportional to the square root\n", + " of the number of input units.\n", + "\n", + " Returns\n", + " -------\n", + " W = nn.Parameter\n", + " A tensor representing the initialized weight matrix. The tensor has the attribute\n", + " `requires_grad=True`, indicating that gradients will be computed with respect to this tensor\n", + " during the backward pass.\n", + "\n", + " Notes\n", + " -----\n", + " - `input_size` and `num_classes` should be defined in the surrounding scope.\n", + " - The tensor is moved to the device specified by the `device` variable and has the data type\n", + " specified by the `dtype` variable. Both `device` and `dtype` should be defined in the surrounding scope.\n", + " - The `requires_grad=True` argument in `nn.Parameter` ensures that gradients are computed for\n", + " this tensor, enabling learning of its values during optimization.\n", + "\n", + " Examples\n", + " --------\n", + " >>> W = init_weight_matrix()\n", + " >>> print(W.shape)\n", + " (input_size, num_classes)\n", + " \"\"\"\n", + " # Note that the requires_grad=True argument tells PyTorch that we'll be computing gradients with\n", + " # respect to the values in this tensor and thereby learning those values. If you want PyTorch to\n", + " # learn some gradients, make sure it has this on.\n", + " W = nn.Parameter(torch.empty((input_size, num_classes), device=device, dtype=dtype, requires_grad=True))\n", + " fan_in, _ = nn.init._calculate_fan_in_and_fan_out(W) # corresponds to the number of input units\n", + " bound = 1 / np.sqrt(fan_in)\n", + " nn.init.uniform_(W, -bound, bound)\n", + " return W\n", + "\n", + "# Run the simulation\n", + "def membrane_only(input_spikes, W, tau=20*ms):\n", + " \"\"\"\n", + " Run a simulation of a membrane potential dynamic in response to input spikes.\n", + "\n", + " This function simulates the evolution of membrane potential `v` across time, given a batch of\n", + " input spike trains `input_spikes` and synaptic weight matrix `W`. The membrane potential is\n", + " updated at each time step based on the previous potential, the input spikes, and the synaptic weights,\n", + " with an exponential decay parameterized by `tau`.\n", + "\n", + " Parameters\n", + " ----------\n", + " input_spikes : Tensor\n", + " A 3D tensor representing a batch of input spike trains.\n", + " Shape: (batch_size, duration_steps, input_size)\n", + " W : Tensor\n", + " A 2D tensor representing the synaptic weight matrix.\n", + " Shape: (input_size, num_classes)\n", + " tau : float, optional\n", + " The time constant for the exponential decay of the membrane potential, in seconds.\n", + " Default is 20 ms.\n", + "\n", + " Returns\n", + " -------\n", + " v_rec : Tensor\n", + " A 3D tensor containing the recorded membrane potentials for each batch, time step, and class.\n", + " Shape: (batch_size, duration_steps, num_classes)\n", + "\n", + " Notes\n", + " -----\n", + " - `batch_size`, `num_classes`, `duration_steps`, `device`, and `dtype` should be defined in the\n", + " surrounding scope or passed as arguments.\n", + " - `input_spikes` should be a binary tensor, where `input_spikes[b, t, i]` is 1 if there is a spike\n", + " from input neuron `i` at time `t` in batch `b`, and 0 otherwise.\n", + " - `W` should contain synaptic weights such that `W[i, j]` is the weight from input neuron `i` to\n", + " output neuron `j`.\n", + "\n", + " Examples\n", + " --------\n", + " >>> input_spikes = torch.tensor([[[1, 0], [0, 1], [1, 1]]], dtype=dtype, device=device)\n", + " >>> W = torch.tensor([[0.5, -0.5], [-0.5, 0.5]], dtype=dtype, device=device)\n", + " >>> v_rec = membrane_only(input_spikes, W)\n", + " >>> print(v_rec.shape)\n", + " (1, 3, 2)\n", + " \"\"\"\n", + " # Input has shape (batch_size, duration_steps, input_size)\n", + " v = torch.zeros((batch_size, num_classes), device=device, dtype=dtype)\n", + " # v_rec will store the membrane in each time step\n", + " v_rec = [v]\n", + " # Batch matrix multiplication all time steps\n", + " # Equivalent to matrix multiply input_spikes[b, :, :] x W for all b, but faster\n", + " h = torch.einsum(\"abc,cd->abd\", (input_spikes, W)) # Note h corresponds to z in Background Slide 1\n", + " # precalculate multiplication factor\n", + " alpha = np.exp(-dt/tau)\n", + " # Update membrane and spikes one time step at a time\n", + " for t in range(duration_steps - 1):\n", + " v = alpha*v + h[:, t, :] # (batch_size, time step, number of output neurons). The batch size indicates how many input samples are processed in parallel\n", + " v_rec.append(v)\n", + " # return the recorded membrane potentials stacked into a single tensor\n", + " v_rec = torch.stack(v_rec, dim=1) # (batch_size, duration_steps, num_classes)\n", + " return v_rec" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Let's visualize the initialization of the weigth matrix\n" + ], + "metadata": { + "id": "GmcK8IqvLMNg" + }, + "id": "GmcK8IqvLMNg" + }, + { + "cell_type": "code", + "source": [ + "W=init_weight_matrix();\n", + "plt.figure\n", + "plt.hist(W.detach().numpy().ravel(), bins='auto',rwidth=0.9); # detach the tensor from the gradient calculation -> transform it to a numpy array -> unravel the 2D array into a 1D one\n", + "plt.xlabel('Weigth value')\n", + "plt.ylabel('Counts')\n", + "plt.title('Distribution of initialized weights')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 490 + }, + "id": "3Z8ZZ9tMNEnj", + "outputId": "b9d44362-9eb4-4bc7-ae9d-4ecf4637c1eb" + }, + "id": "3Z8ZZ9tMNEnj", + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Distribution of initialized weights')" + ] + }, + "metadata": {}, + "execution_count": 13 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "id": "20840c40", + "metadata": { + "id": "20840c40" + }, + "source": [ + "### Training\n", + "\n", + "We train this by dividing the input data into batches and computing gradients across batches. In this notebook, batch and data size is small so that it can be run on a laptop in a couple of minutes, but normally you'd use larger batches and more data. Let's start with the data.\n", + "\n", + "\n", + "\n", + "### Training follows the following algorithm:\n", + "\n", + "1. Shuffle the training data and divide them into batches. This is done by the function: **data_generator(discretise(ipds), spikes)**. Note that this function outputs with every execution a new set of batches after shuffling the input data.\n", + "2. Calculate the network output for every input value in the batch. Compute the loss for each point and then find the average loss across the entire batch. Here we use as loss function the Negative Log likelihood Loss function: **loss_fn = nn.NLLLoss()**. Note: This loss function computes the average loss accross the input batch by default.\n", + "3. To update the weights, calculate the gradient of the average loss function with respect to the model parameters, which is equivalent to computing the average of the individual gradients from the points in the batch. This is done by the function: **loss.backward()**\n", + "4. Update the weights according to the formular: w_new = w_old - lr * avg_i(dL_i/dw), where lr represents the learning rate. This is done by the function: **optimizer.step()**\n", + "5. Once all batches have been processed, begin a new epoch of the training process. Shuffle the data, define a new set of batches, and repeat the training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc5fdc4f", + "metadata": { + "id": "fc5fdc4f" + }, + "outputs": [], + "source": [ + "# Parameters for training. These aren't optimal, but instead designed\n", + "# to give a reasonable result in a small amount of time for the tutorial!\n", + "if my_computer_is_slow:\n", + " batch_size = 64\n", + " n_training_batches = 64\n", + "else:\n", + " batch_size = 128\n", + " n_training_batches = 128\n", + "n_testing_batches = 32\n", + "num_samples = batch_size*n_training_batches\n", + "\n", + "# NOTE 1:A batch is a subset of the training dataset used for a single update of the model parameters.\n", + "# Rather than updating model parameters after processing each individual data point (stochastic gradient descent),\n", + "# batches allow the network to update parameters after processing a group of data points.\n", + "# This approach is called mini-batch gradient descent and is more computationally efficient than stochastic gradient descent.\n", + "# The size of a batch, known as the batch size, is an important hyperparameter and can affect\n", + "# the model's training dynamics and performance.\n", + "\n", + "# NOTE2 : Small batch sizes improve generalization through noisier gradients and\n", + "# require less memory, making them ideal for limited resources, but they may\n", + "# lead to slower computation and less stable convergence due to noisier gradient\n", + "# updates. Conversely, large batch sizes enhance computational efficiency and stability\n", + "# of gradient estimates due to better GPU utilization, but they demand more memory and\n", + "# might result in poorer generalization due to the risk of converging to sharp minima\n", + "# that don't generalize well on unseen data.\n", + "\n", + "\n", + "\n", + "\n", + "# Generator function iterates over the data in batches\n", + "# We randomly permute the order of the data to improve learning\n", + "def data_generator(ipds, spikes):\n", + " \"\"\"\n", + " Generate batches of data, iterating over IPDs and spikes in a randomized order.\n", + "\n", + " This generator function yields shuffled batches of interaural phase differences (IPDs) and spikes,\n", + " facilitating mini-batch gradient descent training of a model. The order of the data is randomized\n", + " to improve learning, mitigating the risk of the model memorizing the order of the training data\n", + " (overfitting) and helping the model generalize better to unseen data.\n", + "\n", + " Parameters\n", + " ----------\n", + " ipds : Tensor\n", + " A 1D tensor of IPD values.\n", + " Shape: (n_samples, )\n", + " spikes : Tensor\n", + " A 3D tensor representing a batch of input spike trains.\n", + " Shape: (n_samples, duration_steps, input_size)\n", + "\n", + " Yields\n", + " ------\n", + " spike_batch : Tensor\n", + " A 3D tensor containing a batch of input spike trains.\n", + " Shape: (batch_size, duration_steps, input_size)\n", + " ipd_batch : Tensor\n", + " A 1D tensor containing a batch of IPD values.\n", + " Shape: (batch_size, )\n", + "\n", + " Notes\n", + " -----\n", + " - `batch_size` should be defined in the surrounding scope or passed as an argument.\n", + " - Ensure that `ipds` and the first dimension of `spikes` have the same size.\n", + " - The generator yields `spike_batch` and `ipd_batch` which are randomly shuffled batches of `spikes` and `ipds` respectively.\n", + " \"\"\"\n", + " perm = torch.randperm(spikes.shape[0])\n", + " spikes = spikes[perm, :, :]\n", + " ipds = ipds[perm]\n", + " n, _, _ = spikes.shape\n", + " n_batch = n//batch_size\n", + " for i in range(n_batch):\n", + " spike_batch = spikes[i*batch_size:(i+1)*batch_size, :, :] # spike_batch\n", + " ipd_batch = ipds[i*batch_size:(i+1)*batch_size] # ipd_batch\n", + " yield spike_batch, ipd_batch # yield means that at each function call the function returns the next result of the loop interation" + ] + }, + { + "cell_type": "markdown", + "id": "6d0c463c", + "metadata": { + "id": "6d0c463c" + }, + "source": [ + "Now we run the training. We generate the training data, initialise the weight matrix, set the training parameters, and run for a few epochs, printing the training loss as we go. We use the all-powerful Adam optimiser, softmax and negative log likelihood loss." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7fb79463", + "metadata": { + "id": "7fb79463", + "outputId": "81348d5c-5004-4d3a-ca22-e2d2a2bb99ec", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 683 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Want loss for epoch 1 to be about 2.48, multiply m by constant to get this\n", + "Epoch 1: loss=5.96144\n", + "Epoch 2: loss=3.25891\n", + "Epoch 3: loss=2.98991\n", + "Epoch 4: loss=2.69476\n", + "Epoch 5: loss=2.53694\n", + "Epoch 6: loss=2.46820\n", + "Epoch 7: loss=2.36351\n", + "Epoch 8: loss=2.23314\n", + "Epoch 9: loss=2.05013\n", + "Epoch 10: loss=2.03256\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Training parameters\n", + "nb_epochs = 10 # is quick, it won't have converged.\n", + "# Note: An epoch is one complete pass through the entire training dataset.\n", + "# During an epoch, the neural network processes every example in the dataset once.\n", + "# Completing an epoch means that every data point has been used for calculating the loss and updating the model parameters.\n", + "# Multiple epochs are usually required for the network to converge to an optimal set of parameters.\n", + "\n", + "lr = 0.01 # learning rate\n", + "\n", + "# Generate the training data\n", + "ipds, spikes, _ = random_ipd_input_signal(num_samples) # num_samples = batch_size*n_training_batches\n", + "\n", + "# Initialise a weight matrix\n", + "W = init_weight_matrix()\n", + "\n", + "# Optimiser and loss function\n", + "optimizer = torch.optim.Adam([W], lr=lr)\n", + "log_softmax_fn = nn.LogSoftmax(dim=1)\n", + "loss_fn = nn.NLLLoss() # negative log likelihood loss. Note: This loss function computes the average loss accross the input batch by default.\n", + "\n", + "print(f\"Want loss for epoch 1 to be about {-np.log(1/num_classes):.2f}, multiply m by constant to get this\")\n", + "\n", + "loss_hist = []\n", + "plt.figure\n", + "for e in range(nb_epochs):\n", + " local_loss = []\n", + " for spike_batch, ipd_batch in data_generator(discretise(ipds), spikes):\n", + " # Run network\n", + " output = membrane_only(spike_batch, W) # our function to calculate the forward path from input to output\n", + " #output = torch.abs(output)\n", + " # Compute cross entropy loss\n", + " m = torch.sum(output, 1)*0.01 # Sum the output over the time dimension. Note: We want loss for epoch 1 to be about -np.log(1/num_classes), multiply m by a constant to get this\n", + " loss = loss_fn(log_softmax_fn(m), ipd_batch)\n", + " local_loss.append(loss.item())\n", + "\n", + " # The softmax function transforms the output of a neural network's final layer into a probability\n", + " # distribution over multiple classes in such a way that increasing the score of one class\n", + " # decreases the probabilities of the other classes. It does this by exponentiating each logit\n", + " # and then normalizing these values so that they sum to 1. This is important because it ensures that\n", + " # the predicted values for each class sum up to 1.0. This probability distribution allows us to\n", + " # interpret the network's output as the likelihood of each class being the correct class.\n", + " # Training Objective: The training process aims to increase the probability of the correct class.\n", + " # As the model updates its weights to increase the probability (and hence the log probability) of the\n", + " # correct class, the softmax function inherently decreases the probabilities of the other classes due\n", + " # to the normalization step.\n", + " # Using it with the negative log likelihood loss encourages the model to increase the log probability\n", + " # of the correct class.\n", + " # Interpretability: The softmax function's output can be interpreted as class probabilities, which is\n", + " # valuable not only for making predictions but also for understanding the model's confidence in those\n", + " # predictions. This can be useful for post-processing or decision-making based on the network's output\n", + " # probabilities.\n", + "\n", + " # Update gradients\n", + " optimizer.zero_grad() # set previous gradients to zero\n", + " loss.backward() # calculate the gradient of the loss with respect to all model parameters\n", + " optimizer.step() # update the weights according to the calculated gradients : new_model_parameter=old_model_parameter−learning rate×gradient_with_respect_to_model_parameter\n", + "\n", + " loss_hist.append(np.mean(local_loss))\n", + " print(\"Epoch %i: loss=%.5f\"%(e+1, np.mean(local_loss)))\n", + "\n", + "# Plot the loss function over time\n", + "plt.plot(loss_hist)\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Loss')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### FYI: Explaination of the used loss function" + ], + "metadata": { + "id": "YUQ4R9XhvVS7" + }, + "id": "YUQ4R9XhvVS7" + }, + { + "cell_type": "markdown", + "source": [ + "In the above training we used the folloz loss function that we minimized to train hte network:\n", + "\n", + "```\n", + " loss = loss_fn(log_softmax_fn(m), ipd_batch)\n", + "```\n", + "\n", + "Let`s go step-by-step to see how this works:\n", + "\n", + "- m:\n", + "This is the model’s output for a batch of data, often referred to as logits. Logits are raw scores produced by the model, which are not yet normalized into probabilities.\n", + "- log_softmax_fn(m):\n", + "Converts the logits into normalized log-probabilities using the log-softmax function.\n", + " 1.\tSoftmax is first applied to normalize the logits into probabilities:\n", + " $$\n", + " \\text{Softmax}(x_i) = \\frac{\\exp(x_i)}{\\sum_{j} \\exp(x_j)}\n", + " $$\n", + "\n", + " The probabilities of all output neurons sum up to 1.\n", + "\n", + " 2.\tThe natural logarithm (log) is then applied to these probabilities to make them log-scaled:\n", + " $$\n", + " \\text{LogSoftmax}(x_i) = \\log(\\text{Softmax}(x_i)) = x_i - \\log\\left(\\sum_j \\exp(x_j)\\right)\n", + " $$\n", + "\n", + "- ipd_batch:\n", + "Contains the ground truth labels for the batch, which are indices representing the true class of each sample.\n", + "\n", + "- nn.NLLLoss():\n", + "The Negative Log-Likelihood Loss function computes then negative log-probability of the true class for each sample:\n", + "$$\n", + "\\text{Loss} = -\\log(\\text{Predicted Probability of True Class})\n", + "$$\n", + "This ensures the loss value decreases as the model predicts higher probabilities for the true class.\n", + "\n", + "- Overall Loss:\n", + "The total loss is the mean (or sum, depending on configuration) of the individual sample losses across the batch." + ], + "metadata": { + "id": "_rrLV1tbRgtw" + }, + "id": "_rrLV1tbRgtw" + }, + { + "cell_type": "code", + "source": [ + "# @title\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def softmax(x):\n", + " e_x = np.exp(x - np.max(x)) # subtract max(x) for numerical stability\n", + " return e_x / e_x.sum(axis=0)\n", + "\n", + "def log_softmax(x):\n", + " return np.log(softmax(x))\n", + "\n", + "# Create a range of values\n", + "x = np.linspace(-2, 2, 10)\n", + "\n", + "# Calculate softmax and log_softmax\n", + "y_softmax = softmax(x)\n", + "y_log_softmax = log_softmax(x)\n", + "\n", + "# Create plots\n", + "plt.figure(figsize=(12, 5))\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(x, y_softmax, label='Softmax', color='blue',marker='o')\n", + "plt.title('Softmax Function')\n", + "plt.xlabel('x')\n", + "plt.ylabel('Softmax(x)')\n", + "plt.grid(True)\n", + "plt.legend()\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(x, y_log_softmax, label='LogSoftmax', color='red',marker='o')\n", + "plt.title('LogSoftmax Function')\n", + "plt.xlabel('x')\n", + "plt.ylabel('LogSoftmax(x)')\n", + "plt.grid(True)\n", + "plt.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "x = [0.5, 1.5, 0.2]\n", + "print(softmax(x))\n", + "print(-np.log(softmax(x)))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 435 + }, + "id": "FVsx8p7cvHhj", + "outputId": "ff28e287-25cd-4ece-b05a-2287dc8cb2b1" + }, + "id": "FVsx8p7cvHhj", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[0.2242605 0.60960324 0.16613626]\n", + "[1.49494696 0.49494696 1.79494696]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The sum over the y_softmax is really one:" + ], + "metadata": { + "id": "E9Un66eGehnS" + }, + "id": "E9Un66eGehnS" + }, + { + "cell_type": "code", + "source": [ + "# @title\n", + "sum(y_softmax)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SvCbN3-2vO-O", + "outputId": "733b93d5-546e-42e3-d50b-731960db2ce4" + }, + "id": "SvCbN3-2vO-O", + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1.0" + ] + }, + "metadata": {}, + "execution_count": 17 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Analysis function definition\n", + "\n", + "We define an analysis function that\n", + "- creates a test data set\n", + "- runs the model in a forward path to estimated the IPDs of this test set\n", + "- computes the training and test accuracy by comparing estimated to the true IPD values\n", + "- plots histograms and confusion matrices to understand the errors it's making." + ], + "metadata": { + "id": "UnzJFNQw5Gmt" + }, + "id": "UnzJFNQw5Gmt" + }, + { + "cell_type": "code", + "source": [ + "def analyse(ipds, spikes, label, run, plot_analysis=1):\n", + " \"\"\"\n", + " Analyse the performance of a classifier on interaural phase difference (IPD) data.\n", + "\n", + " This function evaluates the accuracy and error of a classifier by comparing its\n", + " output with true IPD values. It computes the mean and standard deviation of the\n", + " classifier's accuracy and the absolute error in degrees. Additionally, it can\n", + " generate histograms and a confusion matrix to visualize the results.\n", + "\n", + " Parameters:\n", + " ipds (array): Array of true IPD values.\n", + " spikes (array): Array of spike data corresponding to the IPDs.\n", + " label (str): Label for the data, used in plot titles.\n", + " run (callable): Function that runs the classifier on a batch of spike data.\n", + " plot_analysis (bool, optional): If True, plot histograms and confusion matrix.\n", + "\n", + " Returns:\n", + " tuple: Tuple containing mean and standard deviation of classifier accuracy,\n", + " and mean and standard deviation of absolute error in degrees.\n", + " \"\"\"\n", + " # Initialize lists to store batch-wise accuracies, true IPD values, and estimated IPD values.\n", + " accs = [] # Stores accuracy for each batch\n", + " ipd_true = [] # Stores the true IPD values\n", + " ipd_estimated = [] # Stores the estimated IPD values\n", + "\n", + " # Initialize the confusion matrix for classifier evaluation\n", + " confusion = np.zeros((num_classes, num_classes))\n", + "\n", + " # Iterate over batches of data (spikes and corresponding IPDs) generated randomly\n", + " for spike_batch, ipd_batch in data_generator(ipds, spikes): #Generate batches of data, iterating over IPDs and spikes in a randomized order.\n", + " # Discretize the IPD values in the batch by mapping them to their respective classes\n", + " ipd_class_batch = discretise(ipd_batch)\n", + "\n", + " # Run the neural network classifier on the spike batch\n", + " output = run(spike_batch)\n", + "\n", + " # Aggregate the network's output over the time dimension\n", + " m = torch.sum(output, 1)\n", + "\n", + " # Use argmax to select the class with the highest score\n", + " _, ipd_class_batch_estimated = torch.max(m, 1)\n", + " # Note: We don’t use softmax(m) in the forward path but only torch.max(m) because:\n", + " # - The task only requires class estimated, not probabilities.\n", + " # - torch.max is sufficient to identify the estimated class index.\n", + " # - Softmax would add unnecessary computational cost without affecting the correctness of the predictions.\n", + "\n", + "\n", + " # Update the confusion matrix with true and estimated class values\n", + " for i, j in zip(ipd_class_batch.detach().cpu().numpy(), ipd_class_batch_estimated.detach().cpu().numpy()): # update the confusion matrix\n", + " confusion[j, i] += 1\n", + " # This code updates a confusion matrix by counting occurrences of true and predicted class pairs for a batch of data:\n", + " # confusion[j, i] += 1:\n", + " # - Increments the matrix cell at (j, i):\n", + " # - j: Predicted class.\n", + " # - i: True class.\n", + " # - Tracks how often class i is predicted as class j.\n", + "\n", + "\n", + " # Append the original IPD values to the true IPD list\n", + " ipd_true.append(ipd_batch) # creates a list of arrays\n", + "\n", + " # Convert the argmax predictions back to continuous values and append to estimated IPDs\n", + " ipd_estimated.append(continuise(ipd_class_batch_estimated.detach().cpu().numpy()))\n", + "\n", + " # Calculate batch accuracy by comparing predictions to labels\n", + " tmp = np.mean((ipd_class_batch == ipd_class_batch_estimated).detach().cpu().numpy()) # compare to labels\n", + " accs.append(tmp) # Append batch accuracy to the list\n", + "\n", + " # Flatten the lists of true and estimated IPDs into single arrays\n", + " ipd_true = np.hstack(ipd_true) # connetecates the arrays in the list horizontally to create a single flattened array\n", + " ipd_estimated = np.hstack(ipd_estimated)\n", + "\n", + " # Compute absolute errors in degrees between true and estimated IPDs\n", + " abs_errors_deg = abs(ipd_true-ipd_estimated)*180/np.pi\n", + "\n", + " # Calculate mean and standard deviation of the classifier accuracy in percentage\n", + " classifier_accuracy_mean = 100*np.mean(accs) # in percent\n", + " classifier_accuracy_std = 100*np.std(accs) # in percent\n", + "\n", + " # Calculate mean and standard deviation of the absolute error in degrees\n", + " absolute_error_mean = np.mean(abs_errors_deg) # in degree\n", + " absolute_error_std = np.std(abs_errors_deg) # in degree\n", + "\n", + " # Print results for the classifier's accuracy and absolute error\n", + " print(f\"{label} classifier accuracy: {100*np.mean(accs):.1f}%\")\n", + " print(f\"{label} absolute error: {np.mean(abs_errors_deg):.1f} deg \\n\")\n", + "\n", + " # If visualization is requested, plot the results\n", + " if plot_analysis:\n", + " plt.figure(figsize=(10, 4), dpi=100)\n", + "\n", + " # Plot histograms of true and estimated IPDs\n", + " plt.subplot(121)\n", + " plt.hist(ipd_true*180/np.pi, bins=num_classes, label='True')\n", + " plt.hist(ipd_estimated*180/np.pi, bins=num_classes, label='Estimated')\n", + " plt.xlabel(\"IPD\")\n", + " plt.yticks([])\n", + " plt.legend(loc='best')\n", + " plt.title(label)\n", + "\n", + " # Normalize the confusion matrix and plot it\n", + " plt.subplot(122)\n", + " confusion /= np.sum(confusion, axis=0)[np.newaxis, :]\n", + " ConfusionMatrix = plt.imshow(confusion, interpolation='nearest', aspect='auto', origin='lower', extent=(-90, 90, -90, 90))\n", + " plt.xlabel('True IPD')\n", + " plt.ylabel('Estimated IPD')\n", + " plt.title('Confusion matrix')\n", + " plt.tight_layout()\n", + "\n", + " # Add a color bar with the label \"Probability\"\n", + " cbar = plt.colorbar(ConfusionMatrix) # Add color bar\n", + " cbar.set_label('Probability') # Set the label for the color bar\n", + " plt.tight_layout()\n", + "\n", + " # Return the computed metrics\n", + " return classifier_accuracy_mean, classifier_accuracy_std, absolute_error_mean, absolute_error_std\n" + ], + "metadata": { + "id": "yfDApdFJ4P4J" + }, + "id": "yfDApdFJ4P4J", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Analysis of the network performance" + ], + "metadata": { + "id": "GBS35vYF50ld" + }, + "id": "GBS35vYF50ld" + }, + { + "cell_type": "markdown", + "source": [ + "By using our analysis function we run the model on a test data set that is different from the training data set and plot the analysis results." + ], + "metadata": { + "id": "mO4zONYp4igO" + }, + "id": "mO4zONYp4igO" + }, + { + "cell_type": "code", + "source": [ + "print(f\"Chance accuracy level: {100*1/num_classes:.1f}% \\n\")\n", + "run_func = lambda x: membrane_only(x, W)\n", + "analyse(ipds, spikes, 'Train', run=run_func)\n", + "ipds_test, spikes_test, _ = random_ipd_input_signal(batch_size*n_testing_batches)\n", + "analyse(ipds_test, spikes_test, 'Test', run=run_func)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 909 + }, + "id": "X0PN9wdeqGm7", + "outputId": "f6d427d3-bffa-4462-a158-0cbb7218b858" + }, + "id": "X0PN9wdeqGm7", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Chance accuracy level: 8.3% \n", + "\n", + "Train classifier accuracy: 28.7%\n", + "Train absolute error: 19.5 deg \n", + "\n", + "Test classifier accuracy: 22.7%\n", + "Test absolute error: 21.6 deg \n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(22.65625, 5.496580968543081, 21.590408188264117, 16.698834029736815)" + ] + }, + "metadata": {}, + "execution_count": 19 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Let's look at the network parameters after the last epche" + ], + "metadata": { + "id": "lb4-j5cvYqvK" + }, + "id": "lb4-j5cvYqvK" + }, + { + "cell_type": "markdown", + "source": [ + "#### Dynamics of the output neurons" + ], + "metadata": { + "id": "g4CMFtpHY54p" + }, + "id": "g4CMFtpHY54p" + }, + { + "cell_type": "code", + "source": [ + "# @title\n", + "# define the plot helper function\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from matplotlib.cm import ScalarMappable\n", + "\n", + "# We define here a helper function to control the line colors in a plot\n", + "def plot_lines_with_colorbar(x, ys, cmap_name='viridis', ax=None, colorbar_label=''):\n", + " \"\"\"\n", + " This function defines the colormap over which the line plot function cycles\n", + " and plots the corresponding colorbar next to the plot.\n", + "\n", + " Parameters:\n", + " x (array-like): X-axis values.\n", + " ys (list of arrays): List of Y-axis values for each line.\n", + " cmap_name (str): Name of the colormap to use.\n", + " ax (matplotlib axis, optional): Axis to plot on. Defaults to current axis.\n", + " colorbar_label (str, optional): Label for the colorbar. Defaults to an empty string.\n", + "\n", + " Example usage:\n", + " x = np.linspace(0, 10, 100)\n", + " ys = [np.sin(x + i) for i in np.arange(0, 2, 0.5)]\n", + "\n", + " plt.figure(figsize=(8, 6))\n", + " plot_lines_with_colorbar(x, ys, cmap_name='jet', colorbar_label='class index')\n", + " plt.show()\n", + " \"\"\"\n", + " if ax is None:\n", + " ax = plt.gca()\n", + "\n", + " # Define a new color cycle\n", + " num_lines = len(ys)\n", + " new_color_cycle = plt.get_cmap(cmap_name)(np.linspace(0, 1, num_lines))\n", + "\n", + " # Set the color cycle for the axis\n", + " ax.set_prop_cycle(color=new_color_cycle)\n", + "\n", + " # Plot multiple lines\n", + " for y in ys:\n", + " ax.plot(x, y)\n", + "\n", + " # Create a ScalarMappable object and plot the colorbar\n", + " sm = ScalarMappable(cmap=cmap_name, norm=plt.Normalize(vmin=0, vmax=num_lines))\n", + " sm.set_array([])\n", + " cbar = plt.colorbar(sm, ax=ax, ticks=np.linspace(0, num_lines, num_lines+1),\n", + " pad=0.01)\n", + " # Add the label to the colorbar\n", + " cbar.set_label(colorbar_label, rotation=270, labelpad=15)" + ], + "metadata": { + "id": "HHJoFyD7cDGP" + }, + "id": "HHJoFyD7cDGP", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print(f'nb_epochs = {nb_epochs}')\n", + "print(f'output.shape = {output.shape} => (epochs, time points, classes)')\n", + "\n", + "plt.figure\n", + "#for i in range(num_classes):\n", + "# plt.plot(output.detach().numpy()[63,:,i])\n", + "plt.subplot(1,2,1)\n", + "#ys = [torch.abs(output).detach().numpy()[63,:,i] for i in range(num_classes)]\n", + "ys = [output.detach().numpy()[63,:,i] for i in range(num_classes)]\n", + "\n", + "plot_lines_with_colorbar(np.arange(duration_steps),ys, cmap_name='bwr',colorbar_label='Class index')\n", + "plt.xlabel('Time')\n", + "plt.ylabel('V_output')\n", + "\n", + "plt.subplot(1,2,2)\n", + "# calculate output agregated over time for e.g. the sample number 63\n", + "m_selection = np.sum(output.detach().numpy()[63,:,:],0)\n", + "# determine the estimated class index\n", + "ipd_class_estimated = np.argmax(m_selection)\n", + "\n", + "# plot agregated output neuron values\n", + "#plt.bar(np.arange(12),m,color='black')\n", + "plt.plot(np.arange(12),m_selection,color='black')\n", + "plt.plot(ipd_class_estimated, m_selection[ipd_class_estimated],'*r',markersize=20,label='Estimated class')\n", + "\n", + "\n", + "plt.title(f'Estimated class = {ipd_class_estimated}')\n", + "\n", + "plt.xlabel('Output neuron index (idp classes)')\n", + "plt.ylabel('m = sum(output) per output neuron')\n", + "plt.xticks(ticks=np.arange(12))\n", + "plt.legend()\n", + "plt.tight_layout()" + ], + "metadata": { + "id": "7Wp5hVLpiOlU", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 523 + }, + "outputId": "d1dfc615-6901-40f8-ebf8-506951734528" + }, + "id": "7Wp5hVLpiOlU", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "nb_epochs = 10\n", + "output.shape = torch.Size([64, 100, 12]) => (epochs, time points, classes)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Weights distribution after training\n" + ], + "metadata": { + "id": "t5cbrZErZBsc" + }, + "id": "t5cbrZErZBsc" + }, + { + "cell_type": "code", + "source": [ + "plt.figure\n", + "plt.subplot(1,2,1)\n", + "plt.hist(init_weight_matrix().detach().numpy().ravel(),bins=100);\n", + "plt.xlabel('Weigth value')\n", + "plt.ylabel('Counts')\n", + "plt.title('initialization')\n", + "\n", + "plt.subplot(1,2,2)\n", + "plt.hist(W.detach().numpy().ravel(),bins=100);\n", + "plt.xlabel('Weigth value')\n", + "plt.ylabel('Counts')\n", + "plt.title('after training')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 490 + }, + "id": "08o6peVj2R_C", + "outputId": "7749b49e-de56-42b1-ed6d-a150ec2c5650" + }, + "id": "08o6peVj2R_C", + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'after training')" + ] + }, + "metadata": {}, + "execution_count": 22 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "results_df", + "summary": "{\n \"name\": \"results_df\",\n \"rows\": 64,\n \"fields\": [\n {\n \"column\": \"inferred ipds\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3,\n \"min\": 1,\n \"max\": 10,\n \"num_unique_values\": 9,\n \"samples\": [\n 9,\n 6,\n 8\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"true ipds\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3,\n \"min\": 0,\n \"max\": 11,\n \"num_unique_values\": 12,\n \"samples\": [\n 1,\n 9,\n 3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 23 + } + ] + }, + { + "cell_type": "markdown", + "id": "729b0bbf", + "metadata": { + "id": "729b0bbf" + }, + "source": [ + "This poor performance isn't surprising because this network is not actually doing any coincidence detection, just a weighted sum of input spikes." + ] + }, + { + "cell_type": "markdown", + "id": "3bb91016", + "metadata": { + "id": "3bb91016" + }, + "source": [ + "## Implementation 2: Spiking neural network\n", + "\n", + "Next we'll implement a version of the model with spikes to see how that changes performance. We'll just add a single hidden feed-forward layer of spiking neurons between the input and the output layers. This layer will be spiking, so we need to use the surrogate gradient descent approach.\n", + "\n", + "![Full architecture](https://github.com/neural-reckoning/cosyne-tutorial-2022/blob/main/arch-full.png?raw=1)" + ] + }, + { + "cell_type": "markdown", + "id": "03f5456e", + "metadata": { + "id": "03f5456e" + }, + "source": [ + "### Surrogate gradient descent\n", + "\n", + "First, this is the key part of surrogate gradient descent, a function where we override the computation of the gradient to replace it with a smoothed gradient. You can see that in the forward pass (method ``forward``) it returns the Heaviside function of the input (takes value 1 if the input is ``>0``) or value 0 otherwise. In the backwards pass, it returns the gradient of a sigmoid function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5fabc7b", + "metadata": { + "id": "e5fabc7b" + }, + "outputs": [], + "source": [ + "beta = 5\n", + "\n", + "class SurrGradSpike(torch.autograd.Function):\n", + " \"\"\"\n", + " This class allows for the approximation of gradients for non-differentiable spiking functions, enabling\n", + " the backpropagation of errors in networks that incorporate spiking neurons. The forward method applies\n", + " a thresholding logic, mimicking the firing of a neuron, while the backward method implements the surrogate\n", + " gradient calculation.\n", + "\n", + " Methods\n", + " -------\n", + " @staticmethod\n", + " forward(ctx, input):\n", + " Computes the forward propagation step in the neural network. This method applies a specific logic to\n", + " mimic the all-or-none spiking nature of biological neurons. It generates a binary output corresponding\n", + " to whether each neuron in the input tensor has fired or not.\n", + " Parameters:\n", + " ctx : torch.autograd.function._ContextMethodMixin\n", + " A context object for storing information necessary for the backward computation.\n", + " input : torch.Tensor\n", + " A tensor containing the input data, typically the neuronal activations in form of the membrane potential,\n", + " for which the output firing response will be computed.\n", + " Returns:\n", + " torch.Tensor: A tensor with the same shape as input, filled with binary values indicating whether\n", + " each neuron has fired (1.0) or not (0.0).\n", + "\n", + " @staticmethod\n", + " backward(ctx, grad_output):\n", + " Computes the backward propagation step in the neural network. This method calculates the surrogate\n", + " gradients of the loss function with respect to the input activations. It is designed to work with\n", + " the non-differentiable nature of spiking neurons by approximating the gradients.\n", + " Parameters:\n", + " ctx : torch.autograd.function._ContextMethodMixin\n", + " A context object that has the information stashed during the forward pass.\n", + " grad_output : torch.Tensor\n", + " A tensor containing the gradient of the loss function with respect to the outputs of the forward method.\n", + " Returns:\n", + " torch.Tensor: A tensor containing the surrogate gradients of the loss function with respect to\n", + " the input activations, which can be backpropagated through the rest of the network.\n", + " \"\"\"\n", + " @staticmethod\n", + " def forward(ctx, input):\n", + " ctx.save_for_backward(input)\n", + " out = torch.zeros_like(input)\n", + " out[input > 0] = 1.0\n", + " return out\n", + " @staticmethod\n", + " def backward(ctx, grad_output):\n", + " input, = ctx.saved_tensors\n", + " # Original SPyTorch/SuperSpike gradient\n", + " # This seems to be a typo or error? But it works well\n", + " #grad = grad_output/(100*torch.abs(input)+1.0)**2\n", + " # Sigmoid\n", + " grad = grad_output*beta*torch.sigmoid(beta*input)*(1-torch.sigmoid(beta*input))\n", + " return grad\n", + "\n", + "spike_fn = SurrGradSpike.apply # allows the defined class to be used as a function." + ] + }, + { + "cell_type": "markdown", + "id": "911318ee", + "metadata": { + "id": "911318ee" + }, + "source": [ + "### Updated model\n", + "\n", + "The code for the updated model is very similar to the membrane only layer. First, for initialisation we now need two weight matrices, $W_1$ from the input to the hidden layer, and $W_2$ from the hidden layer to the output layer. Second, we run two passes of the loop that you saw above for the membrane only model.\n", + "\n", + "The first pass computes the output spikes of the hidden layer. The second pass computes the output layer and is exactly the same as before except using the spikes from the hidden layer instead of the input layer.\n", + "\n", + "For the first pass, we modify the function in two ways.\n", + "\n", + "Firstly, we compute the spikes with the line ``s = spike_fn(v-1)``. In the forward pass this just computes the Heaviside function of $v-1$, i.e. returns 1 if $v>1$, otherwise 0, which is the spike threshold function for the LIF neuron. In the backwards pass, it returns a gradient of the smoothed version of the Heaviside function.\n", + "\n", + "The other line we change is the membrane potential update line. Now, we multiply by $1-s$ where ($s=1$ if there was a spike in the previous time step, otherwise $s=0$), so that the membrane potential is reset to 0 after a spike (but in a differentiable way rather than just setting it to 0)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b072bb5", + "metadata": { + "id": "7b072bb5" + }, + "outputs": [], + "source": [ + "num_hidden = 100\n", + "\n", + "# Weights and uniform weight initialisation\n", + "def init_weight_matrices():\n", + " # Input to hidden layer\n", + " W1 = nn.Parameter(torch.empty((input_size, num_hidden), device=device, dtype=dtype, requires_grad=True))\n", + " fan_in, _ = nn.init._calculate_fan_in_and_fan_out(W1)\n", + " bound = 1 / np.sqrt(fan_in)\n", + " nn.init.uniform_(W1, -bound, bound)\n", + " # Hidden layer to output\n", + " W2 = nn.Parameter(torch.empty((num_hidden, num_classes), device=device, dtype=dtype, requires_grad=True))\n", + " fan_in, _ = nn.init._calculate_fan_in_and_fan_out(W2)\n", + " bound = 1 / np.sqrt(fan_in)\n", + " nn.init.uniform_(W2, -bound, bound)\n", + " return W1, W2\n", + "\n", + "# Run the simulation\n", + "def snn(input_spikes, W1, W2, tau=20*ms):\n", + " # First layer: input to hidden\n", + " v = torch.zeros((batch_size, num_hidden), device=device, dtype=dtype)\n", + " s = torch.zeros((batch_size, num_hidden), device=device, dtype=dtype)\n", + " s_rec = [s]\n", + " h = torch.einsum(\"abc,cd->abd\", (input_spikes, W1))\n", + " alpha = np.exp(-dt/tau)\n", + " for t in range(duration_steps - 1):\n", + " new_v = (alpha*v + h[:, t, :])*(1-s) # multiply by 0 after a spike\n", + " s = spike_fn(v-1) # threshold of 1\n", + " v = new_v\n", + " s_rec.append(s)\n", + " s_rec = torch.stack(s_rec, dim=1)\n", + " # Second layer: hidden to output\n", + " v = torch.zeros((batch_size, num_classes), device=device, dtype=dtype)\n", + " s = torch.zeros((batch_size, num_classes), device=device, dtype=dtype)\n", + " v_rec = [v]\n", + " h = torch.einsum(\"abc,cd->abd\", (s_rec, W2))\n", + " alpha = np.exp(-dt/tau)\n", + " for t in range(duration_steps - 1):\n", + " # v = alpha * v + torch.where(h[:, t, :] > 0, h[:, t, :], torch.zeros_like(h[:, t, :])) # VB allow only positive inputs to change the membrane pot.\n", + " v = alpha*v + h[:, t, :]\n", + " v_rec.append(v)\n", + " v_rec = torch.stack(v_rec, dim=1)\n", + " # Return recorded membrane potential of output\n", + " return v_rec" + ] + }, + { + "cell_type": "markdown", + "id": "0a1662e0", + "metadata": { + "id": "0a1662e0" + }, + "source": [ + "### Training and analysing\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Training\n", + "We train it as before, except that we modify the functions to take the two weight matrices into account." + ], + "metadata": { + "id": "xG1W1MTA6L6l" + }, + "id": "xG1W1MTA6L6l" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5d558df", + "metadata": { + "id": "a5d558df", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 683 + }, + "outputId": "0121ce3b-cc74-4768-bc98-7bc2fff75ba5" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Want loss for epoch 1 to be about 2.48, multiply m by constant to get this\n", + "Epoch 1: loss=3.17483\n", + "Epoch 2: loss=1.54502\n", + "Epoch 3: loss=1.22398\n", + "Epoch 4: loss=0.99698\n", + "Epoch 5: loss=0.87871\n", + "Epoch 6: loss=0.77592\n", + "Epoch 7: loss=0.71269\n", + "Epoch 8: loss=0.67917\n", + "Epoch 9: loss=0.62464\n", + "Epoch 10: loss=0.57160\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Training parameters\n", + "nb_epochs = 10 # quick, it won't have converged\n", + "lr = 0.01 # learning rate\n", + "\n", + "# Generate the training data\n", + "ipds, spikes, _ = random_ipd_input_signal(num_samples)\n", + "\n", + "# Initialise a weight matrices\n", + "W1, W2 = init_weight_matrices()\n", + "\n", + "# Optimiser and loss function\n", + "optimizer = torch.optim.Adam([W1, W2], lr=lr)\n", + "log_softmax_fn = nn.LogSoftmax(dim=1)\n", + "loss_fn = nn.NLLLoss()\n", + "\n", + "print(f\"Want loss for epoch 1 to be about {-np.log(1/num_classes):.2f}, multiply m by constant to get this\")\n", + "\n", + "loss_hist = []\n", + "for e in range(nb_epochs):\n", + " local_loss = []\n", + " for spike_batch, ipd_batch in data_generator(discretise(ipds), spikes):\n", + " # Run network\n", + " output = snn(spike_batch, W1, W2)\n", + "\n", + " #output = torch.abs(output)\n", + "\n", + " # Compute cross entropy loss\n", + " m = torch.mean(output, 1) # Mean across time dimension\n", + " loss = loss_fn(log_softmax_fn(m), ipd_batch)\n", + " local_loss.append(loss.item())\n", + "\n", + " # The softmax function transforms the output of a neural network's final layer into a probability\n", + " # distribution over multiple classes in such a way that increasing the score of one class\n", + " # decreases the probabilities of the other classes. It does this by exponentiating each logit\n", + " # and then normalizing these values so that they sum to 1. This is important because it ensures that\n", + " # the predicted values for each class sum up to 1.0. This probability distribution allows us to\n", + " # interpret the network's output as the likelihood of each class being the correct class.\n", + " # Training Objective: The training process aims to increase the probability of the correct class.\n", + " # As the model updates its weights to increase the probability (and hence the log probability) of the\n", + " # correct class, the softmax function inherently decreases the probabilities of the other classes due\n", + " # to the normalization step.\n", + " # Using it with the negative log likelihood loss encourages the model to increase the log probability\n", + " # of the correct class.\n", + " # Interpretability: The softmax function's output can be interpreted as class probabilities, which is\n", + " # valuable not only for making predictions but also for understanding the model's confidence in those\n", + " # predictions. This can be useful for post-processing or decision-making based on the network's output\n", + " # probabilities.\n", + "\n", + " # Update gradients\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " loss_hist.append(np.mean(local_loss))\n", + " print(\"Epoch %i: loss=%.5f\"%(e+1, np.mean(local_loss)))\n", + "\n", + "# Plot the loss function over time\n", + "plt.plot(loss_hist)\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Loss')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "fde52021", + "metadata": { + "id": "fde52021" + }, + "source": [ + "You might already see that the loss functions are lower than before, so maybe performance is better? Let's see." + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Analysis" + ], + "metadata": { + "id": "Xk4jn2Ju6P7s" + }, + "id": "Xk4jn2Ju6P7s" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f8a92ad", + "metadata": { + "id": "7f8a92ad", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 909 + }, + "outputId": "67e5f069-304d-4dd1-9c23-4c12b000ae39" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Chance accuracy level: 8.3% \n", + "\n", + "Train classifier accuracy: 81.6%\n", + "Train absolute error: 4.8 deg \n", + "\n", + "Test classifier accuracy: 76.5%\n", + "Test absolute error: 5.1 deg \n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(76.513671875, 4.823615773931573, 5.090425730940498, 3.714645815731368)" + ] + }, + "metadata": {}, + "execution_count": 27 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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dMW33u6X/KigoUM5hm37aEq2gGuZG0fNO2BXVdp8KCgpIuv+scKpYUFAQSTcAoNJhyvTFFf77VFFVVbGQdAMov6pU9UyiZzY5LS3u7L+q17Coeg1z97ercvanLiXdAAAAAAAUshl22Qzz11RGJN0AAAAAAFPsMmSXuazbbH1vQdINAAAAADDFLrvMjlubv8I7kHQDgAk2m01nz571dBgoRX5+frwODAAAk2yGIZthbuTabH1vQdINAC4wDEM5OTk6duyYp0NBKfPx8VFMTIz8/Pw8HQoAABUG08tdR9INAC4oTLjDwsIUGBjIbtZewm636+DBgzp06JAaNmzI9woAAEodSTcAXILNZnMk3LVr1/Z0OChloaGhOnjwoM6dO6eqVXndFQAArrDLkI2RbpeQdAPAJRSu4Q4MDPRwJHCHwmnlNpuNpBsAABcxvdx1JN0A4CKmHnsnvlcAAMxjIzXXkXQDAAAAAEyx/+8we01lxDtSAADwUjabTY899phiYmIUEBCgxo0b6/HHH5fxh5EGwzA0ZcoU1a1bVwEBAYqPj9f333/vwagBABWB7X9rus0elREj3QBwGaInriyztvbN6OVy3UtNmU5JSVFqauplRoTy7qmnntKCBQv0yiuv6KqrrtK///1vDRw4UMHBwRo5cqQkaebMmZo7d65eeeUVxcTE6LHHHlNCQoJ27twpf39/Dz8BAAAVH0l3SaUGe7j9455tH0C5dujQIcfPb7zxhqZMmaLs7GxHWfXq1R0/G4Yhm82mKlXoErzNpk2bdMstt6hXr/N/sImOjtY//vEPbd68WdL57z49PV2TJ0/WLbfcIklaunSpwsPD9e6776pfv34eix0AUL7ZjPOH2WsqI6aXA4AXioiIcBzBwcGyWCyO8927d6tGjRr6+OOP1bZtW1mtVn322WdKSkpSnz59nO4zevRodevWzXFut9uVlpbmmK7cunVrvfXWW2X7cHBZhw4dlJmZqe+++06StG3bNn322We64YYbJEl79+5VTk6O4uPjHdcEBwcrNjZWWVlZF7xvfn6+8vLynA4AQOViL+FRGTGsAQCV1MSJE/X000+rUaNGCgkJcematLQ0vfbaa1q4cKGuuOIKbdy4Uffcc49CQ0PVtWtXN0cMsyZOnKi8vDw1a9ZMvr6+stlsevLJJ9W/f39JUk5OjiQpPDzc6brw8HDHZ8VJS0vT1KlT3Rc4AKDcs8sim8y9AcRusr63IOkGgEpq2rRpuv76612un5+fr+nTp2vt2rWKi4uTJDVq1EifffaZXnjhBZLucuif//ynli1bpuXLl+uqq67S1q1bNXr0aNWrV0+JiYklvu+kSZOUnJzsOM/Ly1NkZGRphAwAqCDsxvnD7DWVEUk3AFRS7dq1M1V/z549On36dJFEvaCgQNdcc01phoZS8vDDD2vixImOtdlXX321fvrpJ6WlpSkxMVERERGSpNzcXNWtW9dxXW5urtq0aXPB+1qtVlmtVrfGDgAo32wlGOk2W99bkHQDQCVVrVo1p3MfHx+nV0lJ0tmzZx0/nzx5UpK0cuVK1a9f36keCVj5dPr0afn4OG/f4uvrK7v9/Kq6mJgYRUREKDMz05Fk5+Xl6csvv9RDDz1U1uECAOCVSLoBAJKk0NBQ7dixw6ls69atqlq1qiSpRYsWslqt2r9/P1PJK4ibbrpJTz75pBo2bKirrrpK//nPfzR79mzdd999ks6/Wm706NF64okndMUVVzheGVavXr0im+oBAPBHjHS7jqQbACBJuu666zRr1iwtXbpUcXFxeu2117Rjxw7H1PEaNWpo3LhxGjNmjOx2uzp16qTjx4/r888/V1BQ0GWtEYZ7PPfcc3rsscc0dOhQHT58WPXq1dODDz6oKVOmOOqMHz9ep06d0gMPPKBjx46pU6dOWrVqFe/oBgBclN2wyG6Y3EjNZH1vQdINAJAkJSQk6LHHHtP48eN15swZ3XfffRowYIC2b9/uqPP4448rNDRUaWlp+vHHH1WzZk1de+21euSRRzwYOS6kRo0aSk9PV3p6+gXrWCwWTZs2TdOmTSu7wAAAFR4j3a4j6QaAy7BvRi9Ph3BJSUlJSkpKcpx369atyNrtQlOnTr3oq6AsFotGjRqlUaNGlXaYAACgArHJRzb5XLqi0zWVE0k3AAAAgErhUGfPjLT2impfpu2dM85K2ufWNowSTC83mF4OAAAAAMClMb3cdebmAwAAAAAAAJcx0g0AAAAAMMVm+MhmmFzTXfyWMl6PpBsAAAAAYIpdFtlNTpy2q3Jm3STdAAAAAABTWNPtOpJuAAAAAIApJZtezkg3AAAAAACXdH56ubmRa7P1vQW7lwMAAAAA4CYk3QAAly1ZskQ1a9b0dBimVMSYAQAo7+zykc3kYXbjNW/B9HIAuBypwWXY1nHTlyQlJemVV14pUp6QkKBVq1Zd9Nro6GiNHj1ao0ePdpT17dtXN954o+k4zFqyZIlGjx6tY8eOub0tAABgHmu6XUfSDQBermfPnsrIyHAqs1qtJbpXQECAAgICSiMsAABQgdlLMHJdWV8ZVjnH9wGgErFarYqIiHA6QkJCZBiGUlNT1bBhQ1mtVtWrV08jR46UJHXr1k0//fSTxowZI4vFIovl/MYnf56qnZqaqjZt2mjx4sVq2LChqlevrqFDh8pms2nmzJmKiIhQWFiYnnzySaeYZs+erauvvlrVqlVTZGSkhg4dqpMnT0qSNmzYoIEDB+r48eOOtlNTUyVJ+fn5GjdunOrXr69q1aopNjZWGzZscLr3kiVL1LBhQwUGBurWW2/V0aNH3fMPCwBAJWYzLCU6KiNGugGgknr77bf17LPP6vXXX9dVV12lnJwcbdu2TZK0YsUKtW7dWg888IAGDx580fv88MMP+vjjj7Vq1Sr98MMPuuOOO/Tjjz/qyiuv1KeffqpNmzbpvvvuU3x8vGJjYyVJPj4+mjt3rmJiYvTjjz9q6NChGj9+vObPn68OHTooPT1dU6ZMUXZ2tiSpevXqkqThw4dr586dev3111WvXj2988476tmzp7Zv364rrrhCX375pQYNGqS0tDT16dNHq1atUkpKihv/FQEAqJwK12mbu6ZyjnSTdAOAl/vwww8dSWuhRx55RP7+/oqIiFB8fLyqVq2qhg0bqn379pKkWrVqydfXVzVq1FBERMRF72+327V48WLVqFFDLVq0UPfu3ZWdna2PPvpIPj4+atq0qZ566imtX7/ekXT/cZ14dHS0nnjiCQ0ZMkTz58+Xn5+fgoODZbFYnNrev3+/MjIytH//ftWrV0+SNG7cOK1atUoZGRmaPn265syZo549e2r8+PGSpCuvvFKbNm265Pp1AAAAdyHpBgAv1717dy1YsMCprFatWjp16pTS09PVqFEj9ezZUzfeeKNuuukmValirmuIjo5WjRo1HOfh4eHy9fWVj4+PU9nhw4cd52vXrlVaWpp2796tvLw8nTt3TmfOnNHp06cVGBhYbDvbt2+XzWbTlVde6VSen5+v2rVrS5J27dqlW2+91enzuLg4km4AAEqZ3fCR3eRGanY2UgMAeKNq1aqpSZMmRcpr1aql7OxsrV27VmvWrNHQoUM1a9Ysffrpp6patarL9/9zXYvFUmyZ3W6XJO3bt0+9e/fWQw89pCeffFK1atXSZ599pkGDBqmgoOCCSffJkyfl6+urLVu2yNfX1+mzP4/kAwAA92J6uetIugGgEgsICNBNN92km266ScOGDVOzZs20fft2XXvttfLz85PNZiv1Nrds2SK73a5nnnnGMRr+z3/+06lOcW1fc801stlsOnz4sDp37lzsvZs3b64vv/zSqeyLL74oxegBAIAk2SXTG6PZ3RNKuUfSDQBeLj8/Xzk5OU5lVapU0YcffiibzabY2FgFBgbqtddeU0BAgKKioiSdnza+ceNG9evXT1arVXXq1CmVeJo0aaKzZ8/queee00033aTPP/9cCxcudKoTHR2tkydPKjMzU61bt1ZgYKCuvPJK9e/fXwMGDNAzzzyja665Rr/88osyMzPVqlUr9erVSyNHjlTHjh319NNP65ZbbtHq1auZWg4AgBuU7JVhlfPlWZXzqQGgElm1apXq1q3rdHTq1Ek1a9bUiy++qI4dO6pVq1Zau3atPvjgA8f66GnTpmnfvn1q3LixQkNDSy2e1q1ba/bs2XrqqafUsmVLLVu2TGlpaU51OnTooCFDhqhv374KDQ3VzJkzJUkZGRkaMGCAxo4dq6ZNm6pPnz766quv1LBhQ0nSX//6V7344ouaM2eOWrdurU8++USTJ08utdgBAMB5NsOnREdlZDGMS69mz8vLU3BwsI4fP66goKCyiKv8Sw32cPvHPds+UImcOXNGe/fuVUxMjPz9/T0dDkrZxb5f+j/XFP47ddMtqmJxfT8AAChre9L/6pF2rxj37zJt75xxVuvPve2W/qvwv/nPb4lVQHVzE6d/P3lOw9t+Wen6VaaXAwAAAABMscsiu8yu6TZX31tUzvF9AAAqiZ9//ln33HOPateurYCAAF199dX697//f8TFMAxNmTJFdevWVUBAgOLj4/X99997MGIAQEXA9HLXVc6nBgCgEvjtt9/UsWNHVa1aVR9//LF27typZ555RiEhIY46M2fO1Ny5c7Vw4UJ9+eWXqlatmhISEnTmzBkPRg4AKO8KXxlm9qiMmF4OAICXeuqppxQZGamMjAxHWUxMjONnwzCUnp6uyZMn65ZbbpEkLV26VOHh4Xr33XfVr1+/Mo8ZAFAx2A2L7GZfGWayvreonH9qAACgEnj//ffVrl073XnnnQoLC9M111yjF1980fH53r17lZOTo/j4eEdZcHCwYmNjlZWVdcH75ufnKy8vz+kAAFQu9hKMclfWV4Yx0g0ALrLb7Z4OAW7gwks8Kqwff/xRCxYsUHJysh555BF99dVXGjlypPz8/JSYmOh4f3t4eLjTdeHh4UXe7f5HaWlpmjp1qltjB1A2fFtc6ZF231mzzCPt9hjS3iPtGufOlW17hvvbsxs+sptco222vrcg6QaAS/Dz85OPj48OHjyo0NBQ+fn5yWKpnNOjvI1hGPrll19ksVhUtar3verKbrerXbt2mj59uiTpmmuu0Y4dO7Rw4UIlJiaW+L6TJk1ScnKy4zwvL0+RkZGXHS8AAN6IpBsALsHHx0cxMTE6dOiQDh486OlwUMosFosaNGggX19fT4dS6urWrasWLVo4lTVv3lxvv/22JCkiIkKSlJubq7p16zrq5Obmqk2bNhe8r9VqldVqLf2AAQAVhk0W2Uy+AsxsfW9B0g0ALvDz81PDhg117tw52Ww2T4eDUlS1alWvTLglqWPHjsrOznYq++677xQVFSXp/KZqERERyszMdCTZeXl5+vLLL/XQQw+VdbgAgAqE6eWuI+kGABcVTkH2xmnI8E5jxoxRhw4dNH36dP3973/X5s2btWjRIi1atEjS+f9Njx49Wk888YSuuOIKxcTE6LHHHlO9evXUp08fzwYPACjXbDI/cl3SYYt58+Zp1qxZysnJUevWrfXcc8+pffsLr89PT0/XggULtH//ftWpU0d33HGH0tLS5O/vX8IILg9JNwAAXuovf/mL3nnnHU2aNEnTpk1TTEyM0tPT1b9/f0ed8ePH69SpU3rggQd07NgxderUSatWrfLYLyYAgIqhrEa633jjDSUnJ2vhwoWKjY1Venq6EhISlJ2drbCwsCL1ly9frokTJ2rx4sXq0KGDvvvuOyUlJclisWj27Nmm2y8NJN0AAHix3r17q3fv3hf83GKxaNq0aZo2bVoZRgUAqOhsho9sJpNos/Ulafbs2Ro8eLAGDhwoSVq4cKFWrlypxYsXa+LEiUXqb9q0SR07dtTdd98tSYqOjtZdd92lL7/80nTbpaVyTqoHAAAAAJRrBQUF2rJli+Lj4x1lPj4+io+PV1ZWVrHXdOjQQVu2bNHmzZslnX995kcffaQbb7yxTGIuDiPdAAAAAABTDFlkN7mm2/hf/by8PKfyC70V48iRI7LZbAoPD3cqDw8P1+7du4tt4+6779aRI0fUqVMnGYahc+fOaciQIXrkkUdMxVqaGOkGAAAAAJhSOL3c7CFJkZGRCg4OdhxpaWmlFteGDRs0ffp0zZ8/X19//bVWrFihlStX6vHHHy+1NsxipBsAAAAAYIrdsMhumBvpLqx/4MABBQUFOcqLG+WWpDp16sjX11e5ublO5bm5uYqIiCj2mscee0z33nuv7r//fknS1Vdf7dgw9NFHH5WPT9mPOzPSDQAAAAAwxSafEh2SFBQU5HRcKOn28/NT27ZtlZmZ6Siz2+3KzMxUXFxcsdecPn26SGLt6+srSTIMozQe3TRGugEAAAAAplzOSLcZycnJSkxMVLt27dS+fXulp6fr1KlTjt3MBwwYoPr16zumqN90002aPXu2rrnmGsXGxmrPnj167LHHdNNNNzmS77JG0g0AAAAAKJf69u2rX375RVOmTFFOTo7atGmjVatWOTZX279/v9PI9uTJk2WxWDR58mT9/PPPCg0N1U033aQnn3zSU49A0g0AAAAAMMcuH9lNrlY2W7/Q8OHDNXz48GI/27Bhg9N5lSpVlJKSopSUlBK15Q4k3QAAAAAAU2yGRTaT08XN1vcWJN0AAAAAAFPKak23NyDpBgAAAACYYhg+shvmposbJut7C5JuAAAAAIApNllkk8np5SbrewuSbgC4kNRgD7Z93HNtAwAAXILdMD9d3O6Z12R7XOUc3wcAAAAAoAww0g0AAAAAMMVegjXdZut7C5JuAAAAAIApdllkN7lG22x9b0HSDQAAAAAwhfd0u46kGwAAAABgCtPLXUfSDQAAAHiaj69Hmm2zbLdH2r0luqNH2vU/u9kj7Xojuyzmdy+vpNPLK+efGgAAAAAAKAOMdAMAnPF+cgAAcAlGCTZSMyrpSDdJNwAAAADAFLtRgunllXQjNaaXAwBQScyYMUMWi0WjR492lJ05c0bDhg1T7dq1Vb16dd1+++3Kzc31XJAAgAqhcCM1s0dlVDmfGgCASuarr77SCy+8oFatWjmVjxkzRh988IHefPNNffrppzp48KBuu+02D0UJAKgoCke6zR6VEdPLYR7rPQGgQjl58qT69++vF198UU888YSj/Pjx43r55Ze1fPlyXXfddZKkjIwMNW/eXF988YX++te/eipkAEA5Zy/Bmm52LwcAAF5p2LBh6tWrl+Lj453Kt2zZorNnzzqVN2vWTA0bNlRWVlZZhwkAgFdipBsAAC/2+uuv6+uvv9ZXX31V5LOcnBz5+fmpZs2aTuXh4eHKycm54D3z8/OVn5/vOM/Lyyu1eAEAFQMbqbmOkW4AALzUgQMHNGrUKC1btkz+/v6ldt+0tDQFBwc7jsjIyFK7NwCgYmBNt+sY6UbFwnpyAHDZli1bdPjwYV177bWOMpvNpo0bN+r555/X6tWrVVBQoGPHjjmNdufm5ioiIuKC9500aZKSk5Md53l5eSTeAFDJMNLtOpJuwFUk/AAqmL/97W/avn27U9nAgQPVrFkzTZgwQZGRkapataoyMzN1++23S5Kys7O1f/9+xcXFXfC+VqtVVqvVrbEDAMo3km7XkXQDAOClatSooZYtWzqVVatWTbVr13aUDxo0SMnJyapVq5aCgoI0YsQIxcXFsXM5AOCiDJnfjdxwTyjlHkk3AACV2LPPPisfHx/dfvvtys/PV0JCgubPn+/psAAA8Bok3QAAVCIbNmxwOvf399e8efM0b948zwQEAKiQmF7uOpJuAAAAAIApJN2uI+kGAAAAAJhC0u06km4AAAAAgCkk3a4j6QYAAAAAmGIYFhkmk2iz9b0FSTcAAAAAwBS7LKZfGWa2vrfw8XQAAAAAAAB4K0a6AQAAAACmsKbbdSTdAC4uNdiDbR/3XNsAAAC4INZ0u46kGwAAAABgCiPdriPpBgAAAACYwki360i6AQAAAACmGCUY6SbpBgAAACo5H39/j7Q7JzvTI+2O6dLPI+0aZw94pF3AE0i6AQAAAACmGJIMw/w1lRFJNwAAAADAFLssssjkRmom63sLkm4AANzIbrdryZIlWrFihfbt2yeLxaKYmBjdcccduvfee2WxVM5fQAAAFRsbqbmOpBtAuRU9cWWx5ftm9CrjSICSMQxDN998sz766CO1bt1aV199tQzD0K5du5SUlKQVK1bo3Xff9XSYAACYZjcssvDKMJeQdAMA4CZLlizRxo0blZmZqe7duzt9tm7dOvXp00dLly7VgAEDPBQhAAAlYxglWNNdSRd1+3g6AAAAvNU//vEPPfLII0USbkm67rrrNHHiRC1btswDkQEAgLJC0g0AgJt888036tmz5wU/v+GGG7Rt27YyjAgAgNJRuKbb7FEZMb0cAAA3+fXXXxUeHn7Bz8PDw/Xbb7+VYUQAAJQONlJzHUk3AABuYrPZVKXKhbtaX19fnTt3rgwjAgCgdLCRmutIugEAcBPDMJSUlCSr1Vrs5/n5+WUcEQAApYON1FxH0g0AgJskJiZesg47lwMAKqLzSbfZ6eVuCqacI+kGAMBNMjIyPB2C0tLStGLFCu3evVsBAQHq0KGDnnrqKTVt2tRR58yZMxo7dqxef/115efnKyEhQfPnz7/oenQAQOXGmm7XsXs5AAButG/fPr344ouaN2+evv322zJv/9NPP9WwYcP0xRdfaM2aNTp79qx69OihU6dOOeqMGTNGH3zwgd588019+umnOnjwoG677bYyjxUAAG/ESDdQAURPXOmxtvf5e6xpoMJbv369evfurd9//12SVKVKFS1evFj33HNPmcWwatUqp/MlS5YoLCxMW7ZsUZcuXXT8+HG9/PLLWr58ua677jpJ50fomzdvri+++EJ//etfyyxWAEDFYfzvMHtNZcRINwAAbvLYY4/p+uuv188//6yjR49q8ODBGj9+vEdjOn78uCSpVq1akqQtW7bo7Nmzio+Pd9Rp1qyZGjZsqKysLI/ECAAo/8ryPd3z5s1TdHS0/P39FRsbq82bN1+0/rFjxzRs2DDVrVtXVqtVV155pT766KMStV0aSLoBAHCTHTt2aPr06apbt65CQkI0a9YsHT58WEePHvVIPHa7XaNHj1bHjh3VsmVLSVJOTo78/PxUs2ZNp7rh4eHKyckp9j75+fnKy8tzOgAAlYxRwsOkN954Q8nJyUpJSdHXX3+t1q1bKyEhQYcPHy62fkFBga6//nrt27dPb731lrKzs/Xiiy+qfv36Lre5fv1684FeBEk3AABukpeXpzp16jjOAwMDFRAQ4BhtLmvDhg3Tjh079Prrr1/WfdLS0hQcHOw4IiMjSylCAECFUZJR7hKMdM+ePVuDBw/WwIED1aJFCy1cuFCBgYFavHhxsfUXL16sX3/9Ve+++646duyo6Ohode3aVa1bt3a5zZ49e6px48Z64okndODAAdMx/xlJNwAAbrR69Wq9//77jsNutyszM9OprCwMHz5cH374odavX68GDRo4yiMiIlRQUKBjx4451c/NzVVERESx95o0aZKOHz/uOErjFxIAQMVS+J5us4ekIrOl8vPzi22joKBAW7ZscVoC5ePjo/j4+AsugXr//fcVFxenYcOGKTw8XC1bttT06dNls9lcfraff/5Zw4cP11tvvaVGjRopISFB//znP1VQUOD6P9AfsJEaAABuVNy7uh988EHHzxaLxdQvAmYZhqERI0bonXfe0YYNGxQTE+P0edu2bVW1alVlZmbq9ttvlyRlZ2dr//79iouLK/aeVqtVVqvVbTEDALzbn2dIpaSkKDU1tUi9I0eOyGazFXmFZXh4uHbv3l3svX/88UetW7dO/fv310cffaQ9e/Zo6NChOnv2rFJSUlyKr06dOhozZozGjBmjr7/+WhkZGRo6dKiGDh2qu+++W4MGDTI1cl7pk+6S7grt6R2d3bWb9b4ZvdxyXwBwRWn8t608/XfMbrd7OgQNGzZMy5cv13vvvacaNWo41mkHBwcrICBAwcHBGjRokJKTk1WrVi0FBQVpxIgRiouLY+dyAMAFXc57ug8cOKCgoCBHeWn+IddutyssLEyLFi2Sr6+v2rZtq59//lmzZs1yOen+o2uvvVYRERGqXbu2ZsyYocWLF2v+/PmKi4vTwoULddVVV13yHkwvBwDAiy1YsEDHjx9Xt27dVLduXcfxxhtvOOo8++yz6t27t26//XZ16dJFERERWrFihQejBgCUe4VrtM0ekoKCgpyOCyXdderUka+vr3Jzc53KL7YEqm7durryyivl6+vrKGvevLlycnJMTQ8/e/as3nrrLd14442KiorS6tWr9fzzzys3N1d79uxRVFSU7rzzTpfuVelHugEAcBdX12vffPPNbovBMC69Vay/v7/mzZunefPmuS0OAIB3+eMabTPXmOHn56e2bdsqMzNTffr0kSTH3ijDhw8v9pqOHTtq+fLlstvt8vE5P8b83XffqW7duvLz83Op3REjRugf//iHDMPQvffeq5kzZzre+iFJ1apV09NPP6169eq5dD+SbgAA3KTwF4SLcfeabgAA3KIkrwArwSvDkpOTlZiYqHbt2ql9+/ZKT0/XqVOnNHDgQEnSgAEDVL9+faWlpUmSHnroIT3//PMaNWqURowYoe+//17Tp0/XyJEjXW5z586deu6553TbbbdddBTe1VeLkXQDQAVX2ns8eHrPCm9SHtZ0AwDgDpezptuMvn376pdfftGUKVOUk5OjNm3aaNWqVY7N1fbv3+8Y0ZbOb9K2evVqjRkzRq1atVL9+vU1atQoTZgwweU2U1JS1KFDB1Wp4pwunzt3Tps2bVKXLl1UpUoVde3a1aX7kXQDAACgfPLxvXSdUnbFZ56ZeTK6rfuWmVyM7Qiv/EP5N3z48AtOJ9+wYUORsri4OH3xxRclbq979+46dOiQwsLCnMqPHz+u7t27m56hRtINAAAAADCvBNPFKwLDMGSxFB2VP3r0qKpVq2b6fiTdAAAAAABTymp6eVm67bbbJJ3fbyUpKclpPbfNZtM333yjDh06mL4vSTcAAAAAwJwy2kitLAUHB0s6P9Jdo0YNBQQEOD7z8/PTX//6Vw0ePNj0fUm6AVQ4pb1x2IWwoRgAAMCFWP53mL2m/MrIyJAkRUdHa9y4cSWaSl4ckm4AKIfK6g8LAAAAJeKFI92FUlJSSvV+JN0AALhBSEhIsZuwFOfXX391czQAAOBirr32WmVmZiokJETXXHPNRfvwr7/+2tS9SboBAHCD9PR0x89Hjx7VE088oYSEBMXFxUmSsrKytHr1aj322GMeihAAgMvgZSPdt9xyi2PjtD59+pTqvT2WdDN1snxy5XthnSsAXFpiYqLj59tvv13Tpk1zesfoyJEj9fzzz2vt2rUaM2aMJ0IEAKDkDMv5w+w15dQfp5SX9vRyn1K9GwAAKGL16tXq2bNnkfKePXtq7dq1HogIAIDLYxglOyojppcDAOBmtWvX1nvvvaexY8c6lb/33nuqXbu2h6ICAOAyeNn0cnfuxULSDQCAm02dOlX333+/NmzYoNjYWEnSl19+qVWrVunFF1/0cHQAAJSAl00v/+NeLKWNpBsAADdLSkpS8+bNNXfuXK1YsUKS1Lx5c3322WeOJBwAgIrEYpw/zF5TXv1xL5bSRtINAEAZiI2N1bJlyzwdBgAAKEZeXp6CgoIcP19MYT1XkXQDAFAGfvjhB2VkZOjHH39Uenq6wsLC9PHHH6thw4a66qqrPB0eAADmeOGa7kOHDiksLEw1a9Ysdn23YRiyWCyy2Wym7k3SDQCAm3366ae64YYb1LFjR23cuFFPPPGEwsLCtG3bNr388st66623PB0iAADmeNma7nXr1qlWrVqSpPXr15fqvUm6AQBws4kTJ+qJJ55QcnKyatSo4Si/7rrr9Pzzz3swMgAASsjLRrq7du1a7M+loUIn3fv87/Z0CAAAXNL27du1fPnyIuVhYWE6cuSIByICAOAyeVnS/We//fabXn75Ze3atUuS1KJFCw0cONAxGm6GT2kHBwAAnNWsWVOHDh0qUv6f//xH9evX90BEAABcJqOERwWwceNGRUdHa+7cufrtt9/022+/ae7cuYqJidHGjRtN369Cj3QDlQWzOoCKrV+/fpowYYLefPNNWSwW2e12ff755xo3bpwGDBjg6fAAAMAfDBs2TH379tWCBQvk6+srSbLZbBo6dKiGDRum7du3m7ofI90AALjZ9OnT1axZM0VGRurkyZNq0aKFunTpog4dOmjy5MmeDg8AAPMKN1Ize1QAe/bs0dixYx0JtyT5+voqOTlZe/bsMX0/km4AANzMz89PL774on788Ud9+OGHeu2117R79269+uqrTh26J82bN0/R0dHy9/dXbGysNm/e7OmQAADlmMUo2VERXHvttY613H+0a9cutW7d2vT9mF4OAICbTZs2TePGjVNkZKQiIyMd5b///rtmzZqlKVOmeDA66Y033lBycrIWLlyo2NhYpaenKyEhQdnZ2QoLC/NobACAcsrLNlL75ptvHD+PHDlSo0aN0p49e/TXv/5VkvTFF19o3rx5mjFjhul7k3QDAOBmU6dO1ZAhQxQYGOhUfvr0aU2dOtXjSffs2bM1ePBgDRw4UJK0cOFCrVy5UosXL9bEiRM9GhsAAGWhTZs2slgsMoz//8vA+PHji9S7++671bdvX1P3JukGAMDNDMOQxVJ0Hdu2bdtK9OqR0lRQUKAtW7Zo0qRJjjIfHx/Fx8crKyvLg5EBAMozi8xPFy/PK7r37t3rtnuTdAMA4CYhISGyWCyyWCy68sornRJvm82mkydPasiQIR6MUDpy5IhsNpvCw8OdysPDw7V79+5ir8nPz1d+fr7jPC8vz60xAgDgblFRUW67N0k3AABukp6eLsMwdN9992nq1KkKDg52fObn56fo6GjFxcV5MMKSSUtL09SpUz0dBsqQb23PzMiY9/X7Zd7m/feNKvM2JanKkS0eaRcosZLsRl5Bdi8vtHPnTu3fv18FBQVO5TfffLOp+5B0AwDgJomJiZKkmJgYdejQQVWrVvVwREXVqVNHvr6+ys3NdSrPzc1VREREsddMmjRJycnJjvO8vDynDeIAAJWAl22k9kc//vijbr31Vm3fvt1pnXfhjDWbzWbqfrwyDAAAN+vatasj4T5z5ozy8vKcDk/y8/NT27ZtlZmZ6Siz2+3KzMy84Ci81WpVUFCQ0wEAqGSMEh4VwKhRoxQTE6PDhw8rMDBQ3377rTZu3Kh27dppw4YNpu/HSDcAAG52+vRpjR8/Xv/85z919OjRIp+b/Yt5aUtOTlZiYqLatWun9u3bKz09XadOnXLsZg4AwJ+V5L3bFeU93VlZWVq3bp3q1KkjHx8f+fj4qFOnTkpLS9PIkSP1n//8x9T9GOkGAMDNHn74Ya1bt04LFiyQ1WrVSy+9pKlTp6pevXpaunSpp8NT37599fTTT2vKlClq06aNtm7dqlWrVhXZXA0AAAcvHum22WyqUaOGpPPLsA4ePCjp/GZr2dnZpu/HSDcAAG72wQcfaOnSperWrZsGDhyozp07q0mTJoqKitKyZcvUv39/T4eo4cOHa/jw4Z4OAwBQUXjxmu6WLVtq27ZtiomJUWxsrGbOnCk/Pz8tWrRIjRo1Mn0/RroBAHCzX3/91dFJBwUF6ddff5UkderUSRs3bvRkaAAA4E8mT54su90uSZo2bZr27t2rzp0766OPPtLcuXNN34+RbgAA3KxRo0bau3evGjZsqGbNmumf//yn2rdvrw8++EA1a9b0dHgAAJjmzWu6ExISHD83adJEu3fv1q+//qqQkBDHDuZmkHQDAOBmAwcO1LZt29S1a1dNnDhRN910k55//nmdPXtWs2fP9nR4AACYVwne0y1JBw4ckKTLejUmSTcAAG42ZswYx8/x8fHavXu3tmzZoiZNmqhVq1YejAwAgBLy4jXd586d09SpUzV37lydPHlSklS9enWNGDFCKSkpjteAuoqkGwCAMhYVFaWoqChPhwEAQIl58/TyESNGaMWKFZo5c6bi4uIknX+NWGpqqo4ePaoFCxaYuh9JN4Bya5//3Z4OASg1X331ldavX6/Dhw87NmcpxBRzAECF48Uj3cuXL9frr7+uG264wVHWqlUrRUZG6q677iLpBgCgvJk+fbomT56spk2bKjw83GkTlpJsyAIAANzHarUqOjq6SHlMTIz8/PxM34+kGwAAN5szZ44WL16spKQkT4cCAEDpKMH08ooy0j18+HA9/vjjysjIkNVqlSTl5+frySef1PDhw03fj6QbAAA38/HxUceOHT0dBgAApcfLppffdtttTudr165VgwYN1Lp1a0nStm3bVFBQoL/97W+m703SDQCAm40ZM0bz5s1Tenq6p0MBAKB0eFnSHRwc7HR+++23O53zyjAAAMqxcePGqVevXmrcuLFatGhR5FUjK1as8FBkAACUjLftXp6RkeG2e5N0AwDgZiNHjtT69evVvXt31a5dm83TAACoAH755RdlZ2dLkpo2barQ0NAS3YekGwAAN3vllVf09ttvq1evXp4OBQAAXMKpU6c0YsQILV261PGaT19fXw0YMEDPPfecAgMDTd3Pxx1BAgCA/1erVi01btzY02EAAFB6jBIeFUBycrI+/fRTffDBBzp27JiOHTum9957T59++qnGjh1r+n4k3QAAuFlqaqpSUlJ0+vRpT4cCAECpKFzTbfaoCN5++229/PLLuuGGGxQUFKSgoCDdeOONevHFF/XWW2+Zvh/TywEAcLO5c+fqhx9+UHh4uKKjo4tspPb11197KDIAAC5DBUmizTp9+rTCw8OLlIeFhZXoD+gk3QAAuFmfPn08HQIAAKXLy14Z9kdxcXFKSUnR0qVL5e/vL0n6/fffNXXqVMXFxZm+H0k3AABulpKS4ukQAAAoVd72yrA/Sk9PV8+ePdWgQQO1bt1akrRt2zb5+/tr9erVpu9H0g0AAAAAMMeLR7qvvvpqff/991q2bJl2794tSbrrrrvUv39/BQQEmL4fSTcAAG5Qq1Ytfffdd6pTp45CQkIu+m7uX3/9tQwjQ0VWpVG0R9p9MvMNj7Q77Nqby7zNKke3lHmbAC5u3rx5mjVrlnJyctS6dWs999xzat++/SWve/3113XXXXfplltu0bvvvutSW2fPnlWzZs304YcfavDgwZcZ+Xkk3QAAuMGzzz6rGjVqOH6+WNINAEBFU1bTy9944w0lJydr4cKFio2NVXp6uhISEpSdna2wsLALXrdv3z6NGzdOnTt3NtVe1apVdebMGfOBXgRJNwAAbpCYmOj4OSkpyXOBAADgDmU0vXz27NkaPHiwBg4cKElauHChVq5cqcWLF2vixInFXmOz2dS/f39NnTpV//rXv3Ts2DFTbQ4bNkxPPfWUXnrpJVWpcvkpM0k3AABu5uvrq0OHDhX5i/zRo0cVFhYmm83mocgAACihy0i68/LynIqtVqusVmuR6gUFBdqyZYsmTZrkKPPx8VF8fLyysrIu2My0adMUFhamQYMG6V//+pfJIKWvvvpKmZmZ+uSTT3T11VerWrVqTp+vWLHC1P18TEcAAABMMYzifyvJz8+Xn5+fW9rct2+fBg0apJiYGAUEBKhx48ZKSUlRQUGBU71vvvlGnTt3lr+/vyIjIzVz5ky3xAMA8C6F08vNHpIUGRmp4OBgx5GWllZsG0eOHJHNZivyzuzw8HDl5OQUe81nn32ml19+WS+++GKJn61mzZq6/fbblZCQoHr16jnFGhwcbPp+jHQDAOAmc+fOlSRZLBa99NJLql69uuMzm82mjRs3qlmzZm5pe/fu3bLb7XrhhRfUpEkT7dixQ4MHD9apU6f09NNPSzo/0tCjRw/Fx8dr4cKF2r59u+677z7VrFlTDzzwgFviAgB4icsY6T5w4ICCgoIcxcWNcpfEiRMndO+99+rFF19UnTp1TF9vt9s1a9YsfffddyooKNB1112n1NTUEu1Y/kck3QAAuMmzzz4r6fxI98KFC+Xr6+v4zM/PT9HR0Vq4cKFb2u7Zs6d69uzpOG/UqJGys7O1YMECR9K9bNkyFRQUaPHixfLz89NVV12lrVu3avbs2STdAAC3CQoKckq6L6ROnTry9fVVbm6uU3lubq4iIiKK1P/hhx+0b98+3XTTTY4yu90uSapSpYqys7PVuHHjC7b35JNPKjU1VfHx8QoICNDcuXP1yy+/aPHixa4+WrFIugEAcJO9e/dKkrp3764VK1YoJCTEo/EcP35ctWrVcpxnZWWpS5cuTlPcExIS9NRTT+m33367YLz5+fnKz893nP95bR4AoBIog43U/Pz81LZtW2VmZqpPnz6SzifRmZmZGj58eJH6zZo10/bt253KJk+erBMnTmjOnDmKjIy8aHtLly7V/Pnz9eCDD0qS1q5dq169eumll16Sj0/JV2aTdAMA4Gbr1693OrfZbNq+fbuioqLKLBHfs2ePnnvuOccotyTl5OQoJibGqV7hurmcnJwLxpaWlqapU6e6L1gAQLlXVq8MS05OVmJiotq1a6f27dsrPT1dp06dcuxmPmDAANWvX19paWny9/dXy5Ytna6vWbOmJBUpL87+/ft14403Os7j4+NlsVh08OBBNWjQwHzw/8NGagAAuNno0aP18ssvSzqfcHfp0kXXXnutIiMjtWHDBlP3mjhxoiwWy0WP3bt3O13z888/q2fPnrrzzjs1ePDgy36eSZMm6fjx447jwIEDl31PAEAFY5TwMKlv3756+umnNWXKFLVp00Zbt27VqlWrHH8k3r9/vw4dOnT5zyPp3Llz8vf3dyqrWrWqzp49e1n3ZaQbAAA3e/PNN3XPPfdIkj744APt27dPu3fv1quvvqpHH31Un3/+ucv3Gjt27CXf+92oUSPHzwcPHlT37t3VoUMHLVq0yKleREREsevkCj+7kAu92gUAUHmU1Ui3JA0fPrzY6eSSLvnH6yVLlrjcjmEYSkpKcurjzpw5oyFDhji9NszsK8NIugEAcLOjR486ktiPPvpId955p6688krdd999mjNnjql7hYaGKjQ01KW6P//8s7p37662bdsqIyOjyHq0uLg4Pfroozp79qyqVq0qSVqzZo2aNm3q8fXnAIByrgzWdJe1xMTEImWFfzS/HCTdAAC4WXh4uHbu3Km6detq1apVWrBggSTp9OnTTjual6aff/5Z3bp1U1RUlJ5++mn98ssvjs8K/wBw9913a+rUqRo0aJAmTJigHTt2aM6cOY5d1wEAqEwyMjLccl+SbgAA3GzgwIH6+9//rrp168pisSg+Pl6S9OWXX7rtPd1r1qzRnj17tGfPniKbvxjG+aGG4OBgffLJJxo2bJjatm2rOnXqaMqUKbwuDABwaV440u0uJN0AALhZamqqWrZsqQMHDujOO+90rBXz9fXVxIkT3dJmUlLSJdd+S1KrVq30r3/9yy0xAAC8l+V/h9lrKiOSbgAAysAdd9xRpKy4tWMAAFQIjHS7jFeGAQDgJjfeeKOOHz/uOJ8xY4aOHTvmOD969KhatGjhgcgAALg8hbuXmz0qI5JuAADcZPXq1crPz3ecT58+Xb/++qvj/Ny5c8rOzvZEaAAAXJ4yek+3NyDpBgDATQo3LLvQOQAA8H6s6QYAAAAAmMffkl1C0g0AgJtYLBZZLJYiZQAAVHQlWaNdWdd0k3QDAOAmhmEoKSnJ8YqwM2fOaMiQIapWrZokOa33BgCgQmH3cpeRdAMA4CZ/fiXYPffcU6TOgAEDyiocAABKDSPdriPpBgDATTIyMjwdAgAA7sFIt8tIugEAAAAApjDS7TqSbgAAAJMOD+3gkXafH/e8R9qddFV3j7RrP/3rpSsBQDlH0g0AAAAAMIfp5S4j6QYAAAAAmEPS7TKSbgAAAACAKazpdh1JNwAAAADAHEa6XUbSDQAAAAAwxWIYshjmsmiz9b2Fj6cDAAAAAADAWzHSDQAAAAAwh+nlLiPpBgAAAACYwkZqriPpBgAAAACYw0i3y0i6AaAc2ud/t6dDAAAAuCBGul1H0g0AAAAAMIeRbpexezkAAAAAAG5C0g0AgJfLz89XmzZtZLFYtHXrVqfPvvnmG3Xu3Fn+/v6KjIzUzJkzPRMkAKBCKZxebvaojEi6AQDwcuPHj1e9evWKlOfl5alHjx6KiorSli1bNGvWLKWmpmrRokUeiBIAUKEYJTwqIdZ0AwDgxT7++GN98sknevvtt/Xxxx87fbZs2TIVFBRo8eLF8vPz01VXXaWtW7dq9uzZeuCBBzwUMQCgoqisI9dmMdINAICXys3N1eDBg/Xqq68qMDCwyOdZWVnq0qWL/Pz8HGUJCQnKzs7Wb7/9dsH75ufnKy8vz+kAAFQyhlGyoxIi6QYAwAsZhqGkpCQNGTJE7dq1K7ZOTk6OwsPDncoKz3Nyci5477S0NAUHBzuOyMjI0gscAFAhsKbbdSTdAABUIBMnTpTFYrnosXv3bj333HM6ceKEJk2aVOoxTJo0ScePH3ccBw4cKPU2AADlHGu6XcaabgAAKpCxY8cqKSnponUaNWqkdevWKSsrS1ar1emzdu3aqX///nrllVcUERGh3Nxcp88LzyMiIi54f6vVWuS+AACgeCTdAABUIKGhoQoNDb1kvblz5+qJJ55wnB88eFAJCQl64403FBsbK0mKi4vTo48+qrNnz6pq1aqSpDVr1qhp06YKCQlxzwMAALyCxX7+MHtNZUTSDQCAF2rYsKHTefXq1SVJjRs3VoMGDSRJd999t6ZOnapBgwZpwoQJ2rFjh+bMmaNnn322zOMFAFQwJZkuzvRyAABQmQQHB+uTTz7RsGHD1LZtW9WpU0dTpkzhdWEAgEsqycZolXUjNZJuAAAqgejoaBnFvKqlVatW+te//uWBiAAAFVpJXgFWSV8ZRtINAAAAADCFkW7X8cowAAAAAADchJFuAAAAk+qu/8Uj7U6bf61H2pVOe6hdAOUWG6m5jKQbAAAAAGAK08tdR9INAAAAADCHjdRcRtINAAAAADCFkW7XkXQDAAAAAMxhTbfL2L0cAAAAAAA3YaQbAAAAAGAK08tdR9INAAAAADDHbpw/zF5TCZF0AwAAAADMYU23y0i6AQAAAACmWFSC6eVuiaT8YyM1AAAAAIA5he/pNnuUwLx58xQdHS1/f3/FxsZq8+bNF6z74osvqnPnzgoJCVFISIji4+MvWr8skHQDAAAAAMqlN954Q8nJyUpJSdHXX3+t1q1bKyEhQYcPHy62/oYNG3TXXXdp/fr1ysrKUmRkpHr06KGff/65jCP/fyTdAAAAAABTCncvN3uYNXv2bA0ePFgDBw5UixYttHDhQgUGBmrx4sXF1l+2bJmGDh2qNm3aqFmzZnrppZdkt9uVmZl5mU9cciTdAAAAAABzjBIekvLy8pyO/Pz8YpsoKCjQli1bFB8f7yjz8fFRfHy8srKyXArz9OnTOnv2rGrVqlWSpywVJN0AAAAAAFMshlGiQ5IiIyMVHBzsONLS0opt48iRI7LZbAoPD3cqDw8PV05OjktxTpgwQfXq1XNK3Msau5cDAAAAAMyx/+8we42kAwcOKCgoyFFstVpLLaw/mjFjhl5//XVt2LBB/v7+bmnDFSTdAAAAAABT/jhybeYaSQoKCnJKui+kTp068vX1VW5urlN5bm6uIiIiLnrt008/rRkzZmjt2rVq1aqVqThLG9PLAQAAAADmXMaablf5+fmpbdu2TpugFW6KFhcXd8HrZs6cqccff1yrVq1Su3btzDXqBox0AwAAAADKpeTkZCUmJqpdu3Zq37690tPTderUKQ0cOFCSNGDAANWvX9+xLvypp57SlClTtHz5ckVHRzvWflevXl3Vq1f3yDOQdAMAAAAAzDGM84fZa0zq27evfvnlF02ZMkU5OTlq06aNVq1a5dhcbf/+/fLx+f8J3AsWLFBBQYHuuOMOp/ukpKQoNTXVdPulgenlAAB4sZUrVyo2NlYBAQEKCQlRnz59nD7fv3+/evXqpcDAQIWFhenhhx/WuXPnPBMsAKDCKKv3dEvS8OHD9dNPPyk/P19ffvmlYmNjHZ9t2LBBS5YscZzv27dPhmEUOTyVcEuMdAMA4LXefvttDR48WNOnT9d1112nc+fOaceOHY7PbTabevXqpYiICG3atEmHDh3SgAEDVLVqVU2fPt2DkQMAyr0yGun2BiTdAAB4oXPnzmnUqFGaNWuWBg0a5Chv0aKF4+dPPvlEO3fu1Nq1axUeHq42bdro8ccf14QJE5Samio/Pz9PhA4AqAAs9vOH2WsqI6aXAwDghb7++mv9/PPP8vHx0TXXXKO6devqhhtucBrpzsrK0tVXX+1YFydJCQkJysvL07fffuuJsAEAFUXhSLfZoxIi6QYAwAv9+OOPkqTU1FRNnjxZH374oUJCQtStWzf9+uuvkqScnBynhFuS47xwt9fi5OfnKy8vz+kAAADFY3o5AAAVyMSJE/XUU09dtM6uXbtkt5+fw/foo4/q9ttvlyRlZGSoQYMGevPNN/Xggw+WOIa0tDRNnTq1SLlvWKh8fcp2SvpH//mkTNsrFPN+e4+0e+UQjzQLAEWV4L3bput7CZJuAAAqkLFjxyopKemidRo1aqRDhw5Jcl7DbbVa1ahRI+3fv1+SFBERoc2bNztdm5ub6/jsQiZNmqTk5GTHeV5eniIjI009BwCgYrMYhiwmp4ubre8tSLoBAKhAQkNDFRoaesl6bdu2ldVqVXZ2tjp16iRJOnv2rPbt26eoqChJUlxcnJ588kkdPnxYYWFhkqQ1a9YoKCjIKVn/M6vVKqvVWgpPAwCosNi93GUk3QAAeKGgoCANGTJEKSkpioyMVFRUlGbNmiVJuvPOOyVJPXr0UIsWLXTvvfdq5syZysnJ0eTJkzVs2DCSagDAxRmSzO5GXjlzbpJuAAC81axZs1SlShXde++9+v333xUbG6t169YpJCREkuTr66sPP/xQDz30kOLi4lStWjUlJiZq2rRpHo4cAFDeMb3cdSTdAAB4qapVq+rpp5/W008/fcE6UVFR+uijj8owKgAAKheSbgAAAACAOYZKsKbbLZGUeyTdAAAAAABz2EjNZSTdAAAAAABz7JIsJbimEiLpBgAAAACYwkZqriPpBgAAAACYw/Ryl5F0AwAAAADMIel2mY+nAwAAAAAAwFsx0g0AAAAAMIeRbpeRdAMAAAAAzGH3cpeRdAMAAAAATGH3cteRdAMAAAAAzGF6uctIugEAAAAA5tgNyWIyibZXzqSb3csBAAAAAHATRroBAAAAAOYwvdxlJN0AAAAAAJNKkHSLpBsAAAAAgEtjpNtlJN0AAAAAAHPshkyPXFfSjdRIugEAAAAA5hj284fZayohdi8HAAAAAMBNGOkGAAAAAJjDmm6XkXQDAIBSMWv9+6peo2wn0SXU71ym7RW60tjskXYBoNxgTbfLSLoBAAAAAOYw0u0ykm4AAAAAgDmGSpB0uyWSco+ku4La53+3p0MAAJRz3333nR5++GF9/vnnKigoUKtWrfT444+re/fujjr79+/XQw89pPXr16t69epKTExUWlqaqlThVwQAwEUw0u0ydi8HAMBL9e7dW+fOndO6deu0ZcsWtW7dWr1791ZOTo4kyWazqVevXiooKNCmTZv0yiuvaMmSJZoyZYqHIwcAwHuQdAMA4IWOHDmi77//XhMnTlSrVq10xRVXaMaMGTp9+rR27NghSfrkk0+0c+dOvfbaa2rTpo1uuOEGPf7445o3b54KCgo8/AQAgHLNbi/ZUQmRdAMA4IVq166tpk2baunSpTp16pTOnTunF154QWFhYWrbtq0kKSsrS1dffbXCw8Md1yUkJCgvL0/ffvutp0IHAFQEhdPLzR6VEAu2AADwQhaLRWvXrlWfPn1Uo0YN+fj4KCwsTKtWrVJISIgkKScnxynhluQ4L5yCXpz8/Hzl5+c7zvPy8tzwBACAco013S5jpBsAgApk4sSJslgsFz12794twzA0bNgwhYWF6V//+pc2b96sPn366KabbtKhQ4cuK4a0tDQFBwc7jsjIyFJ6OgBAhWE3SnZUQox0AwBQgYwdO1ZJSUkXrdOoUSOtW7dOH374oX777TcFBQVJkubPn681a9bolVde0cSJExUREaHNmzc7XZubmytJioiIuOD9J02apOTkZMd5Xl4eiTcAVDKGYZdhmFujbba+tyDpBgCgAgkNDVVoaOgl650+fVqS5OPjPKnNx8dH9v9tZBMXF6cnn3xShw8fVlhYmCRpzZo1CgoKUosWLS54b6vVKqvVWtJHAAB4A6MEI9dMLwcAAN4iLi5OISEhSkxM1LZt2xzv7N67d6969eolSerRo4datGihe++9V9u2bdPq1as1efJkDRs2jKQaAIBSQtINAIAXqlOnjlatWqWTJ0/quuuuU7t27fTZZ5/pvffeU+vWrSVJvr6++vDDD+Xr66u4uDjdc889GjBggKZNm+bh6AEA5R67l7uM6eUAAHipdu3aafXq1RetExUVpY8++qiMIgIAeA27XbKYXKPNmm4AAAAAAFxgGJJY0+0Kkm4AAAAAgCmG3S7D5Eg3u5cDAAAAAOAKRrpdxkZqAAAAAAC4CSPdAAAAAABz7IZkYaTbFSTdAAAAAABzDEOS2d3LK2fSzfRyAAAAAIApht0o0VES8+bNU3R0tPz9/RUbG6vNmzdftP6bb76pZs2ayd/fX1dffbXHX41J0g0AAAAAMMewl+ww6Y033lBycrJSUlL09ddfq3Xr1kpISNDhw4eLrb9p0ybdddddGjRokP7zn/+oT58+6tOnj3bs2HG5T1xiJN0AAAAAAFPKaqR79uzZGjx4sAYOHKgWLVpo4cKFCgwM1OLFi4utP2fOHPXs2VMPP/ywmjdvrscff1zXXnutnn/++ct95BIj6QYAAAAAlDsFBQXasmWL4uPjHWU+Pj6Kj49XVlZWsddkZWU51ZekhISEC9YvCy5tpGb8b8F7Xl5eqTVszz992ffIM7tbHgCgXCuVvqEU+6rCexmVdOMXVxX++5w8aX7a4OU6Z5wt8zYlVdrNgABUDOd0/r+N7uy/zhn5pqeLF8b1577aarXKarUWqX/kyBHZbDaFh4c7lYeHh2v37t3FtpGTk1Ns/ZycHFOxliaXku4TJ05IkiIjI90ajFnBng4AAFDK/n7ZdwhOv/wo/uzEiRMKDqbXuZDC3xO6xB7xQOvveqBNAKgY3NF/+fn5KSIiQp/llGxzsurVqxfJK1NSUpSamloK0ZVPLiXd9erV04EDB1SjRg1ZLBZ3x1Rm8vLyFBkZqQMHDigoKMjT4bgFz1jxefvzSd7/jN7+fJL3PqNhGDpx4oTq1avn6VDKNU/9nuAN/7vzhmeQeI7yhucoPzz1DO7sv/z9/bV3714VFBSU6HrDMIr0FcWNcktSnTp15Ovrq9zcXKfy3NxcRUREFHtNRESEqfplwaWk28fHRw0aNHB3LB4TFBRUYf+P7CqeseLz9ueTvP8Zvf35JO98Rka4L83Tvyd4w//uvOEZJJ6jvOE5yg9PPIM7+y9/f3/5+/u77f6F/Pz81LZtW2VmZqpPnz6SJLvdrszMTA0fPrzYa+Li4pSZmanRo0c7ytasWaO4uDi3x3shLiXdAAAAAACUteTkZCUmJqpdu3Zq37690tPTderUKQ0cOFCSNGDAANWvX19paWmSpFGjRqlr16565pln1KtXL73++uv697//rUWLFnnsGUi6AQAAAADlUt++ffXLL79oypQpysnJUZs2bbRq1SrHZmn79++Xj8//v5SrQ4cOWr58uSZPnqxHHnlEV1xxhd599121bNnSU49QuZNuq9WqlJSUC64h8AY8Y8Xn7c8nef8zevvzSZXjGVH+eMP/7rzhGSSeo7zhOcoPb3iG8mD48OEXnE6+YcOGImV33nmn7rzzTjdH5TqLwXtQAAAAAABwC59LVwEAAAAAACVB0g0AAAAAgJuQdAMAAAAA4CaVMunesGGDLBZLscdXX30lSdq3b1+xn3/xxRcejt510dHRReKfMWOGU51vvvlGnTt3lr+/vyIjIzVz5kwPRWvOvn37NGjQIMXExCggIECNGzdWSkqKCgoKnOpU9O9QkubNm6fo6Gj5+/srNjZWmzdv9nRIJZKWlqa//OUvqlGjhsLCwtSnTx9lZ2c71enWrVuR72vIkCEeiti81NTUIvE3a9bM8fmZM2c0bNgw1a5dW9WrV9ftt9+u3NxcD0ZsTnH/TbFYLBo2bJikiv/9oeLwln7cG/ppb+uPK1Kf6y39qrf0nfSRuJhKuXt5hw4ddOjQIaeyxx57TJmZmWrXrp1T+dq1a3XVVVc5zmvXrl0mMZaWadOmafDgwY7zGjVqOH7Oy8tTjx49FB8fr4ULF2r79u267777VLNmTT3wwAOeCNdlu3fvlt1u1wsvvKAmTZpox44dGjx4sE6dOqWnn37aqW5F/g7feOMNJScna+HChYqNjVV6eroSEhKUnZ2tsLAwT4dnyqeffqphw4bpL3/5i86dO6dHHnlEPXr00M6dO1WtWjVHvcGDB2vatGmO88DAQE+EW2JXXXWV1q5d6zivUuX//zM7ZswYrVy5Um+++aaCg4M1fPhw3Xbbbfr88889EappX331lWw2m+N8x44duv766512B63o3x8qBm/qxyt6P+1N/XFF63O9qV/1hr6TPhIXZcAoKCgwQkNDjWnTpjnK9u7da0gy/vOf/3gusMsUFRVlPPvssxf8fP78+UZISIiRn5/vKJswYYLRtGnTMoiu9M2cOdOIiYlxnHvDd9i+fXtj2LBhjnObzWbUq1fPSEtL82BUpePw4cOGJOPTTz91lHXt2tUYNWqU54K6TCkpKUbr1q2L/ezYsWNG1apVjTfffNNRtmvXLkOSkZWVVUYRlq5Ro0YZjRs3Nux2u2EYFf/7Q8VVUftxb+2nK2p/XNH73Irar3pr30kfiT+qlNPL/+z999/X0aNHNXDgwCKf3XzzzQoLC1OnTp30/vvveyC6yzNjxgzVrl1b11xzjWbNmqVz5845PsvKylKXLl3k5+fnKCv8i+5vv/3miXAvy/Hjx1WrVq0i5RX1OywoKNCWLVsUHx/vKPPx8VF8fLyysrI8GFnpOH78uCQV+c6WLVumOnXqqGXLlpo0aZJOnz7tifBK7Pvvv1e9evXUqFEj9e/fX/v375ckbdmyRWfPnnX6Pps1a6aGDRtWyO+zoKBAr732mu677z5ZLBZHeUX//lAxVeR+3Bv76YrYH3tDn1uR+1Vv6zvpI/FnlXJ6+Z+9/PLLSkhIUIMGDRxl1atX1zPPPKOOHTvKx8dHb7/9tvr06aN3331XN998swejdd3IkSN17bXXqlatWtq0aZMmTZqkQ4cOafbs2ZKknJwcxcTEOF0THh7u+CwkJKTMYy6pPXv26LnnnnOaylbRv8MjR47IZrM5vpNC4eHh2r17t4eiKh12u12jR49Wx44d1bJlS0f53XffraioKNWrV0/ffPONJkyYoOzsbK1YscKD0bouNjZWS5YsUdOmTXXo0CFNnTpVnTt31o4dO5STkyM/Pz/VrFnT6Zrw8HDl5OR4JuDL8O677+rYsWNKSkpylFX07w8VV0Xtx72xn66o/XFF73Mrcr/qjX0nfSSK8PRQe2maMGGCIemix65du5yuOXDggOHj42O89dZbl7z/vffea3Tq1Mld4bukJM9Y6OWXXzaqVKlinDlzxjAMw7j++uuNBx54wKnOt99+a0gydu7c6fZnKU5Jnu+///2v0bhxY2PQoEGXvH95+A5d9fPPPxuSjE2bNjmVP/zww0b79u09FFXpGDJkiBEVFWUcOHDgovUyMzMNScaePXvKKLLS9dtvvxlBQUHGSy+9ZCxbtszw8/MrUucvf/mLMX78eA9Ed3l69Ohh9O7d+6J1Kvr3h7LnDf24t/TTla0/ruh9rjf1q97Qd9JH4s+8aqR77NixTn9RKk6jRo2czjMyMlS7dm2X/tIaGxurNWvWXE6Il60kz1goNjZW586d0759+9S0aVNFREQU2f2x8DwiIqJU4jXL7PMdPHhQ3bt3V4cOHbRo0aJL3r88fIeuqlOnjnx9fYv9jjz1/ZSG4cOH68MPP9TGjRudRqWKExsbK+n8yEnjxo3LIrxSVbNmTV155ZXas2ePrr/+ehUUFOjYsWNOf7GviN/nTz/9pLVr117yr/MV/ftD2fOGftxb+unK1h9X5D7X2/rVit530keiOF6VdIeGhio0NNTl+oZhKCMjQwMGDFDVqlUvWX/r1q2qW7fu5YR42cw+4x9t3bpVPj4+jh044+Li9Oijj+rs2bOO51+zZo2aNm3qsSlrZp7v559/Vvfu3dW2bVtlZGTIx+fSWxSUh+/QVX5+fmrbtq0yMzPVp08fSeenj2VmZmr48OGeDa4EDMPQiBEj9M4772jDhg1FpkwWZ+vWrZJUYb6zPzt58qR++OEH3XvvvWrbtq2qVq2qzMxM3X777ZKk7Oxs7d+/X3FxcR6O1JyMjAyFhYWpV69eF61X0b8/lD1v6Me9pZ+ubP1xRexzvbVfreh9J30kiuXZgXbPWrt27QWneS1ZssRYvny5sWvXLmPXrl3Gk08+afj4+BiLFy/2QKTmbdq0yXj22WeNrVu3Gj/88IPx2muvGaGhocaAAQMcdY4dO2aEh4cb9957r7Fjxw7j9ddfNwIDA40XXnjBg5G75r///a/RpEkT429/+5vx3//+1zh06JDjKFTRv0PDMIzXX3/dsFqtxpIlS4ydO3caDzzwgFGzZk0jJyfH06GZ9tBDDxnBwcHGhg0bnL6v06dPG4ZhGHv27DGmTZtm/Pvf/zb27t1rvPfee0ajRo2MLl26eDhy140dO9bYsGGDsXfvXuPzzz834uPjjTp16hiHDx82DOP89L+GDRsa69atM/79738bcXFxRlxcnIejNsdmsxkNGzY0JkyY4FTuDd8fKp6K3I97Sz/tTf1xRetzvaVf9aa+kz4SF1Kpk+677rrL6NChQ7GfLVmyxGjevLkRGBhoBAUFGe3bt3d6XUF5t2XLFiM2NtYIDg42/P39jebNmxvTp093rBMrtG3bNqNTp06G1Wo16tevb8yYMcNDEZuTkZFxwTVmhSr6d1joueeeMxo2bGj4+fkZ7du3N7744gtPh1QiF/q+MjIyDMMwjP379xtdunQxatWqZVitVqNJkybGww8/bBw/ftyzgZvQt29fo27duoafn59Rv359o2/fvk5rtX7//Xdj6NChRkhIiBEYGGjceuutTr+YVgSrV682JBnZ2dlO5d7w/aHiqcj9uLf0097WH1ekPtdb+lVv6jvpI3EhFsMwjLIZUwcAAAAAoHLhPd0AAAAAALgJSTcAAAAAAG5C0g0AAAAAgJuQdAMAAAAA4CYk3QAAAAAAuAlJNwAAAAAAbkLSDQAAAACAm5B0AwAAAADgJiTdAAAAAAC4CUk34EFJSUnq06eP42eLxSKLxSI/Pz81adJE06ZN07lz5yRJGzZscHzu4+Oj4OBgXXPNNRo/frwOHTrkwacAAMB9Cvu+Cx2pqallFku3bt00evRop/PCOPz9/dWiRQvNnz/f8fmSJUscn/v6+iokJESxsbGaNm2ajh8/XmZxA/Askm6gHOnZs6cOHTqk77//XmPHjlVqaqpmzZrlVCc7O1sHDx7UV199pQkTJmjt2rVq2bKltm/f7qGoAQBwn0OHDjmO9PR0BQUFOZWNGzfOUdcwDMcfq8vK4MGDdejQIe3cuVN///vfNWzYMP3jH/9wfF4Y73//+19t2rRJDzzwgJYuXao2bdro4MGDZRorAM8g6QbKEavVqoiICEVFRemhhx5SfHy83n//fac6YWFhioiI0JVXXql+/frp888/V2hoqB566CEPRQ0AgPtEREQ4juDgYFksFsf57t27VaNGDX388cdq27atrFarPvvsM6eZZIVGjx6tbt26Oc7tdrvS0tIUExOjgIAAtW7dWm+99Zbp+AIDAxUREaFGjRopNTVVV1xxhVPfXRhv3bp11bx5cw0aNEibNm3SyZMnNX78+JL+swCoQEi6gXIsICBABQUFl6wzZMgQff755zp8+HAZRQYAQPkxceJEzZgxQ7t27VKrVq1cuiYtLU1Lly7VwoUL9e2332rMmDG655579Omnn15WLK703WFhYerfv7/ef/992Wy2y2oPQPlXxdMBACjKMAxlZmZq9erVGjFixCXrN2vWTJK0b98+hYWFuTs8AADKlWnTpun66693uX5+fr6mT5+utWvXKi4uTpLUqFEjffbZZ3rhhRfUtWtX0zHYbDb94x//0DfffKMHHnjgkvWbNWumEydO6OjRo/TdgJcj6QbKkQ8//FDVq1fX2bNnZbfbdffdd7u0QYxhGJLOT2EDAKCyadeunan6e/bs0enTp4sk6gUFBbrmmmtM3Wv+/Pl66aWXVFBQIF9fX40ZM8alJV/03UDlQdINlCPdu3fXggUL5Ofnp3r16qlKFdf+L7pr1y5JUnR0tBujAwCgfKpWrZrTuY+PjyOpLXT27FnHzydPnpQkrVy5UvXr13eqZ7VaTbXdv39/PfroowoICFDdunXl4+Pa6s1du3YpKChItWvXNtUegIqHpBsoR6pVq6YmTZqYuub333/XokWL1KVLF4WGhropMgAAKo7Q0FDt2LHDqWzr1q2qWrWqJKlFixayWq3av39/iaaS/1FwcLDpvvvw4cNavny5+vTp43KSDqDiIukGKpjDhw/rzJkzOnHihLZs2aKZM2fqyJEjWrFihadDAwCgXLjuuus0a9YsLV26VHFxcXrttde0Y8cOx9TxGjVqaNy4cRozZozsdrs6deqk48eP6/PPP1dQUJASExNLLRbDMJSTkyPDMHTs2DFlZWVp+vTpCg4O1owZM0qtHQDlF0k3UME0bdpUFotF1atXV6NGjdSjRw8lJycrIiLC06EBAFAuJCQk6LHHHtP48eN15swZ3XfffRowYIC2b9/uqPP4448rNDRUaWlp+vHHH1WzZk1de+21euSRR0o1lry8PNWtW1cWi0VBQUFq2rSpEhMTNWrUKAUFBZVqWwDKJ4vx5wUvAAAAAACgVLCIBAAAAAAANyHpBgAAAADATUi6AQAAAABwE5JuAAAAAADchKQbAAAAAAA3IekGAAAAAMBNSLoBAAAAAHATkm4AAAAAANyEpBsAAAAAADch6QYAAAAAwE1IugEAAAAAcBOSbgAAAAAA3OT/ANoImP+UkuUdAAAAAElFTkSuQmCC\n" 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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Analyse\n", + "print(f\"Chance accuracy level: {100*1/num_classes:.1f}% \\n\")\n", + "run_func = lambda x: snn(x, W1, W2)\n", + "analyse(ipds, spikes, 'Train', run=run_func)\n", + "ipds_test, spikes_test, _ = random_ipd_input_signal(batch_size*n_testing_batches)\n", + "analyse(ipds_test, spikes_test, 'Test', run=run_func)" + ] + }, + { + "cell_type": "markdown", + "id": "26e19d66", + "metadata": { + "id": "26e19d66" + }, + "source": [ + "Yes! Performance is much better and now the confusion matrices look more like what you'd expect too. Let's take a look at the weight matrices." + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Now play with it\n", + "Change systematically some of the parameters and record the performance. You can do this for example by running the following code in a loop:" + ], + "metadata": { + "id": "34BK5WAviV6-" + }, + "id": "34BK5WAviV6-" + }, + { + "cell_type": "markdown", + "source": [ + "#### Example project: Envelope power\n", + "\n", + "\n" + ], + "metadata": { + "id": "XomkkqeZtCvF" + }, + "id": "XomkkqeZtCvF" + }, + { + "cell_type": "markdown", + "source": [ + "This code performs an analysis of the performance of a spiking neural network (SNN) classifier across a range of envelope power values.\n", + "\n", + "Please note that the current code only plots the result of a single training session.\n", + "\n", + "\n", + "##### Suggestion for improvement:\n", + " \n", + "* Run the example code from the EnvelopePower project multiple times.\n", + "\n", + "* You will notice that the results vary significantly between consecutive training sessions.\n", + "\n", + "* To draw robust conclusions, calculate the average across all training sessions.\n", + "\n", + "* Then, plot a graph with the average and the corresponding standard deviation.\n", + "\n", + "* Apply this same approach to your other projects to obtain reliable results." + ], + "metadata": { + "id": "gvviJOm7QF7T" + }, + "id": "gvviJOm7QF7T" + }, + { + "cell_type": "code", + "source": [ + "from tqdm import tqdm # Import the tqdm library for displaying a progress bar\n", + "\n", + "# Set training parameters\n", + "nb_epochs = 10 # Number of epochs (quick training for demonstration)\n", + "lr = 0.01 # Learning rate\n", + "\n", + "# Flag for whether to plot analysis results (histograms and confusion matrises)\n", + "plot_analysis = 0 # 0:not plotting ; 1:plotting\n", + "\n", + "# Define a range of envelope powers to test\n", + "Envelop_powers = [0, 1, 2, 3, 4, 5, 10, 30, 40, 50, 100]\n", + "\n", + "# Initialize lists to store results for training and testing accuracy and absolute error.\n", + "# Mean and std are calulated over the different batches\n", + "Train_accuracy_mean = [] # Mean training accuracy\n", + "Train_accuracy_std = [] # Standard deviation of training accuracy\n", + "Train_abs_error_mean = [] # Mean training absolute error\n", + "Train_abs_error_std = [] # Standard deviation of training absolute error\n", + "\n", + "Test_accuracy_mean = [] # Mean testing accuracy\n", + "Test_accuracy_std = [] # Standard deviation of testing accuracy\n", + "Test_abs_error_mean = [] # Mean testing absolute error\n", + "Test_abs_error_std = [] # Standard deviation of testing absolute error\n", + "\n", + "results_Train = [] # Stores results from training data\n", + "results_Test = [] # Stores results from test data\n", + "\n", + "# Loop through each envelope power, showing progress with tqdm\n", + "for i, envelope_power in enumerate(tqdm(Envelop_powers, desc=\"Processing Envelope Powers\")):\n", + "\n", + " # Generate training data: interaural phase differences (IPDs) and spike data\n", + " ipds, spikes, _ = random_ipd_input_signal(num_samples)\n", + " plt.imshow(spikes[0, :, :].T, aspect='auto', interpolation='nearest', cmap=plt.cm.gray_r)\n", + "\n", + " # Initialize weight matrices for the neural network classifier\n", + " W1, W2 = init_weight_matrices()\n", + "\n", + " # Define the optimizer and loss functions\n", + " optimizer = torch.optim.Adam([W1, W2], lr=lr)\n", + " log_softmax_fn = nn.LogSoftmax(dim=1)\n", + " loss_fn = nn.NLLLoss()\n", + "\n", + " # Print the expected initial loss\n", + " print(f\"Want loss for epoch 1 to be about {-np.log(1/num_classes):.2f}, multiply m by constant to get this\")\n", + "\n", + " loss_hist = [] # Track loss over epochs\n", + " for e in range(nb_epochs): # Loop through each epoch\n", + " local_loss = [] # Track batch losses for the current epoch\n", + " for spike_batch, ipd_batch in data_generator(discretise(ipds), spikes): # Generate data batches\n", + " # Run the classifier on the batch\n", + " output = snn(spike_batch, W1, W2)\n", + "\n", + " # Compute cross-entropy loss\n", + " m = torch.sum(output, 1) * 0.01 # Aggregate output over the time dimension\n", + " loss = loss_fn(log_softmax_fn(m), ipd_batch)\n", + " local_loss.append(loss.item())\n", + "\n", + " # Update weights\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " # Append the mean loss for the epoch\n", + " loss_hist.append(np.mean(local_loss))\n", + " print(\"Epoch %i: loss=%.5f\" % (e + 1, np.mean(local_loss)))\n", + "\n", + " # Optionally plot the loss curve over epochs\n", + " if plot_analysis:\n", + " # Plot the loss function over time\n", + " plt.plot(loss_hist)\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Loss')\n", + " plt.tight_layout()\n", + "\n", + "\n", + " # Analyse training data\n", + " print(f\"Chance accuracy level: {100*1/num_classes:.1f}%\")\n", + "\n", + " run_func = lambda x: snn(x, W1, W2) # Define the classifier function\n", + " results_Train = analyse(ipds, spikes, 'Train', run=run_func, plot_analysis=plot_analysis)\n", + "\n", + " # Generate and analyse test data\n", + " ipds_test, spikes_test, _ = random_ipd_input_signal(batch_size*n_testing_batches)\n", + " results_Test = analyse(ipds_test, spikes_test, 'Test', run=run_func, plot_analysis=0)\n", + "\n", + " # Append training results\n", + " Train_accuracy_mean.append(results_Train[0])\n", + " Train_accuracy_std.append(results_Train[1])\n", + " Train_abs_error_mean.append(results_Train[2])\n", + " Train_abs_error_std.append(results_Train[3])\n", + "\n", + " # Append testing results\n", + " Test_accuracy_mean.append(results_Test[0])\n", + " Test_accuracy_std.append(results_Test[1])\n", + " Test_abs_error_mean.append(results_Test[2])\n", + " Test_abs_error_std.append(results_Test[3])\n", + "\n", + "# Plot training and testing accuracy with error bars\n", + "plt.figure(figsize=(8, 6))\n", + "plt.errorbar(Envelop_powers,Train_accuracy_mean, yerr=Train_accuracy_std, label='Training',fmt='o', ecolor='blue', capsize=5)\n", + "plt.errorbar(Envelop_powers,Test_accuracy_mean, yerr=Test_accuracy_std, label='Test', fmt='o', ecolor='red', capsize=5)\n", + "plt.ylim([0,100])\n", + "plt.xlim([-1,50])\n", + "plt.xlabel('Envelop Power')\n", + "plt.ylabel('Accurancy (mean+/-std (%))')\n", + "plt.legend()\n", + "\n", + "# Plot training and testing absolute error with error bars\n", + "plt.figure(figsize=(8, 6))\n", + "plt.errorbar(Envelop_powers,Train_abs_error_mean, yerr=Train_abs_error_std, label='Training',fmt='o', ecolor='blue', capsize=5)\n", + "plt.errorbar(Envelop_powers,Test_abs_error_mean, yerr=Test_abs_error_std, label='Test', fmt='o', ecolor='red', capsize=5)\n", + "plt.ylim([0,100])\n", + "plt.xlim([-1,50])\n", + "plt.xlabel('Envelop Power')\n", + "plt.ylabel('Abs_error (mean+/-std (deg))')\n", + "plt.legend()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "R98YIDmQiTUg", + "outputId": "6d4cfcfa-4a80-4a0b-9843-8e27360370c9" + }, + "id": "R98YIDmQiTUg", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\rProcessing Envelope Powers: 0%| | 0/11 [00:00" + ] + }, + "metadata": {}, + "execution_count": 29 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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\n" 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.savefig(\"Proj_EnvelopePower.png\") # Save the plot as a PNG file\n", + "\n", + "!cp Proj_EnvelopePower.png \"/content/drive/My Drive/Mini-Project_ColabNotebooks/SBCP_2024/Results_Figures\"\n", + "\n", + "!ls \"/content/drive/My Drive/Mini-Project_ColabNotebooks/SBCP_2024/Results_Figures\"\n" + ], + "metadata": { + "id": "Su22h0nrio3Z" + }, + "id": "Su22h0nrio3Z", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Example project 2: Weight matrix analyis" + ], + "metadata": { + "id": "aM2fl6UF6aTi" + }, + "id": "aM2fl6UF6aTi" + }, + { + "cell_type": "code", + "source": [ + "# TRAINING\n", + "\n", + "# Training parameters\n", + "nb_epochs = 10 # is quick, it won't have converged.\n", + "# Note: An epoch is one complete pass through the entire training dataset.\n", + "# During an epoch, the neural network processes every example in the dataset once.\n", + "# Completing an epoch means that every data point has been used for calculating the loss and updating the model parameters.\n", + "# Multiple epochs are usually required for the network to converge to an optimal set of parameters.\n", + "lr = 0.01 # learning rate\n", + "\n", + "# Generate the training data\n", + "ipds, spikes, _ = random_ipd_input_signal(num_samples) # num_samples = batch_size * num_training\n", + "\n", + "# Initialise a weight matrices\n", + "W1, W2 = init_weight_matrices()\n", + "\n", + "# Optimiser and loss function\n", + "optimizer = torch.optim.Adam([W1, W2], lr=lr)\n", + "log_softmax_fn = nn.LogSoftmax(dim=1)\n", + "loss_fn = nn.NLLLoss()\n", + "\n", + "print(f\"Want loss for epoch 1 to be about {-np.log(1/num_classes):.2f}, multiply m by constant to get this\")\n", + "\n", + "loss_hist = []\n", + "for e in range(nb_epochs):\n", + " local_loss = []\n", + " for spike_batch, ipd_batch in data_generator(discretise(ipds), spikes):\n", + " # Run network\n", + " output = snn(spike_batch, W1, W2)\n", + "\n", + " # Compute cross entropy loss\n", + " m = torch.sum(output, 1)*0.01 # Agregation fuction: Sum across time dimension. Note: We want loss for epoch 1 to be about -np.log(1/num_classes), multiply m by a constant to get this\n", + " loss = loss_fn(log_softmax_fn(m), ipd_batch)\n", + " local_loss.append(loss.item())\n", + "\n", + " # The softmax function transforms the output of a neural network's final layer into a probability\n", + " # distribution over multiple classes in such a way that increasing the score of one class\n", + " # decreases the probabilities of the other classes. It does this by exponentiating each logit\n", + " # and then normalizing these values so that they sum to 1. This is important because it ensures that\n", + " # the predicted values for each class sum up to 1.0. This probability distribution allows us to\n", + " # interpret the network's output as the likelihood of each class being the correct class.\n", + " # Training Objective: The training process aims to increase the probability of the correct class.\n", + " # As the model updates its weights to increase the probability (and hence the log probability) of the\n", + " # correct class, the softmax function inherently decreases the probabilities of the other classes due\n", + " # to the normalization step.\n", + " # Using it with the negative log likelihood loss encourages the model to increase the log probability\n", + " # of the correct class.\n", + " # Interpretability: The softmax function's output can be interpreted as class probabilities, which is\n", + " # valuable not only for making predictions but also for understanding the model's confidence in those\n", + " # predictions. This can be useful for post-processing or decision-making based on the network's output\n", + " # probabilities.\n", + "\n", + " # Update gradients\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " loss_hist.append(np.mean(local_loss))\n", + " print(\"Epoch %i: loss=%.5f\"%(e+1, np.mean(local_loss)))\n", + "\n", + "# Plot the loss function over time\n", + "plt.plot(loss_hist)\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Loss')\n", + "plt.tight_layout()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 683 + }, + "id": "f4v0OcbUeQJl", + "outputId": "9171d19d-9d0d-48ae-ef73-b18a16bd459f" + }, + "id": "f4v0OcbUeQJl", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Want loss for epoch 1 to be about 2.48, multiply m by constant to get this\n", + "Epoch 1: loss=3.01142\n", + "Epoch 2: loss=1.55636\n", + "Epoch 3: loss=1.22104\n", + "Epoch 4: loss=1.03283\n", + "Epoch 5: loss=0.92237\n", + "Epoch 6: loss=0.82222\n", + "Epoch 7: loss=0.73452\n", + "Epoch 8: loss=0.67997\n", + "Epoch 9: loss=0.63810\n", + "Epoch 10: loss=0.59215\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "416c6383", + "metadata": { + "id": "416c6383", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 320 + }, + "outputId": "0a756baa-c60d-46b9-eed7-51c2f29f2fbe" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "plt.figure(figsize=(10, 4), dpi=100)\n", + "plt.subplot(121)\n", + "plt.imshow(W1.detach().cpu().numpy(), interpolation='nearest', aspect='auto', origin='lower')\n", + "plt.ylabel('Input neuron index')\n", + "plt.xlabel('Hidden layer neuron index')\n", + "plt.colorbar(label=\"Weight\")\n", + "plt.subplot(122)\n", + "plt.imshow(W2.detach().cpu().numpy(), interpolation='nearest', aspect='auto', origin='lower')\n", + "plt.ylabel('Hidden layer neuron index')\n", + "plt.xlabel('Output neuron index')\n", + "plt.colorbar(label=\"Weight\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "source": [ + "W2_ = W2.detach().cpu().numpy()\n", + "W2_.shape\n", + "print(f\"W2_.shape: {W2_.shape}\")\n", + "print(f\"W2_01\")\n", + "print(f\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QJe1WmPq9qz1", + "outputId": "f5e03c57-b455-44da-93e4-e91220ef4778" + }, + "id": "QJe1WmPq9qz1", + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(100, 12)" + ] + }, + "metadata": {}, + "execution_count": 34 + } + ] + }, + { + "cell_type": "markdown", + "id": "d5d19904", + "metadata": { + "id": "d5d19904" + }, + "source": [ + "Hmm, hard to interpret.\n", + "\n", + "**Exercise.** Any ideas?\n", + "\n", + "Here's what Dan Goodmann got so far..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cd305405", + "metadata": { + "id": "cd305405", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 530 + }, + "outputId": "20c04b61-6445-47e3-c09e-c47be96fff1f" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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\n" + }, + "metadata": {} + } + ], + "source": [ + "w1 = W1.detach().cpu().numpy()\n", + "w2 = W2.detach().cpu().numpy()\n", + "\n", + "# for each column of w1, compute the weighted mean and re-order according to that\n", + "A = np.arange(w1.shape[0])[:, None]\n", + "weighted_mean = np.mean((A*w1), axis=0)\n", + "weighted_mean[np.max(np.abs(w1), axis=0)<.5] = np.inf\n", + "I = np.argsort(weighted_mean)\n", + "#w1 = w1[:, I]\n", + "#w2 = w2[I, :]\n", + "\n", + "# Plot the re-ordered weight matrices\n", + "plt.figure(figsize=(10, 3), dpi=100)\n", + "plt.subplot(131)\n", + "plt.imshow(w1, interpolation='nearest', aspect='auto', origin='lower')\n", + "plt.ylabel('Input neuron index')\n", + "plt.xlabel('Hidden layer neuron index')\n", + "plt.title('$W_1$')\n", + "plt.colorbar()\n", + "plt.subplot(132)\n", + "plt.imshow(w2, interpolation='nearest', aspect='auto', origin='lower')\n", + "plt.ylabel('Hidden layer neuron index')\n", + "plt.xlabel('Output neuron index')\n", + "plt.title('$W_2$')\n", + "plt.colorbar()\n", + "plt.subplot(133)\n", + "plt.imshow(w1@w2, interpolation='nearest', aspect='auto', origin='lower')\n", + "plt.ylabel('Input neuron index')\n", + "plt.xlabel('Output neuron index')\n", + "plt.title('$W_1W_2$')\n", + "plt.colorbar()\n", + "plt.tight_layout()\n", + "\n", + "# Plot some sample weights for hidden neurons\n", + "I_nz, = (np.max(np.abs(w1), axis=0)>.5).nonzero()\n", + "plt.figure(figsize=(10, 5), dpi=80)\n", + "phi = np.linspace(-np.pi/2, np.pi/2, w1.shape[0]//2)\n", + "for i, j in list(enumerate(I_nz))[:15]:\n", + " plt.subplot(3, 5, i+1)\n", + " plt.plot(phi*180/np.pi, w1[:w1.shape[0]//2, j], label=\"Left ear\")\n", + " plt.plot(phi*180/np.pi, w1[w1.shape[0]//2:, j], label=\"Right ear\")\n", + "plt.suptitle(\"Individual $W_1$ weights\")\n", + "plt.legend(loc='best')\n", + "plt.xlabel('Phase delay (deg)')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Make a collection of functions and their docstrings that are defined in this notebook." + ], + "metadata": { + "id": "-soPMuPLpUMm" + }, + "id": "-soPMuPLpUMm" + }, + { + "cell_type": "code", + "source": [ + "import inspect\n", + "\n", + "def example_function1():\n", + " \"\"\"This is a docstring for example_function1.\"\"\"\n", + " pass\n", + "\n", + "def example_function2():\n", + " \"\"\"This is a docstring for example_function2.\"\"\"\n", + " pass\n", + "\n", + "# Extracting names and docstrings of user-defined functions\n", + "functions_info = {name: obj.__doc__ for name, obj in globals().items() if inspect.isfunction(obj)}\n", + "\n", + "# Displaying names and docstrings\n", + "for name, doc in functions_info.items():\n", + " print(f\"Function Name: {name}\\nDescription: {doc}\\n{'-'*40}\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "TxXcm2l__YDF", + "outputId": "24ddfe8d-5a00-4b6c-cf90-c39609968e98" + }, + "id": "TxXcm2l__YDF", + "execution_count": null, + "outputs": [ + { + "metadata": { + "tags": null + }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Function Name: input_signal\n", + "Description: \n", + " Generate a Poisson spike train based on an input Interaural Phase Difference (IPD) array\n", + " and the delays imposed by the individual auditory nerve fibers.\n", + "\n", + " Parameters\n", + " ----------\n", + " ipd : array-like\n", + " An array of true Interaural Phase Differences (IPD). Shape: (num_samples, )\n", + "\n", + " Returns\n", + " -------\n", + " spikes : ndarray\n", + " A binary array indicating spike occurrences, shaped (num_samples, duration_steps, 2*anf_per_ear).\n", + " `spikes[i, j, k]` is 1 if a spike occurred at the jth time step for the ith IPD in the kth auditory nerve fiber,\n", + " and 0 otherwise.\n", + "\n", + " Notes\n", + " -----\n", + " - The function first calculates an array of phases (`phi`) to define the sinudoidal auditory stimulus and adds a random\n", + " phase offset because we want that the system learns to infer the angular location of the sound source indepent of its distance\n", + " to the source.\n", + " - An array of theta values is initialized that will hold the transformed phi values according to the phase delay imposed by the\n", + " individual auditory nerve fibers and the ipd between the two ears.\n", + " - Different phase delays, ranging from 0 to pi/2, are calculated and added with the ipd value to generate theta.\n", + " - Poisson spikes are generated based on the theta values and a sinusoidal modulation of the firing rate.\n", + " - The spikes are returned as a binary array, indicating the occurrence of spikes across auditory nerve fibers and time.\n", + " \n", + "----------------------------------------\n", + "Function Name: random_ipd_input_signal\n", + "Description: \n", + " Generate random Interaural Phase Differences (IPDs) and then corresponding spike arrays using\n", + " the function input_signal(idp).\n", + "\n", + " The function generates `num_samples` IPDs, uniformly distributed in the range (-pi/2, pi/2).\n", + " It then generates corresponding spike arrays using the `input_signal` function.\n", + " Optionally, IPDs and spike arrays can be converted to PyTorch tensors.\n", + "\n", + " Parameters\n", + " ----------\n", + " num_samples : int\n", + " The number of IPD samples to generate.\n", + " tensor : bool, optional\n", + " If True, converts the IPDs and spike arrays to PyTorch tensors before returning them.\n", + " If False, they are returned as NumPy arrays. Default is True.\n", + "\n", + " Returns\n", + " -------\n", + " ipd : ndarray or Tensor\n", + " An array of randomly generated IPDs. Shape: (num_samples, ).\n", + " Returned as a PyTorch tensor if `tensor` is True, otherwise as a NumPy array.\n", + " spikes : ndarray or Tensor\n", + " A binary array indicating spike occurrences along time, generated by `input_signal` based on `ipd`.\n", + " Returned as a PyTorch tensor if `tensor` is True, otherwise as a NumPy array.\n", + " Shaped: (num_samples, duration_steps, 2*anf_per_ear)\n", + "\n", + " Notes\n", + " -----\n", + " - Ensure that the `input_signal` function is defined in your environment as it is called within this function.\n", + " - If `tensor` is True, ensure that PyTorch is installed and configured in your environment.\n", + "\n", + " Examples\n", + " --------\n", + " >>> ipd, spikes = random_ipd_input_signal(50, tensor=False)\n", + " >>> print(ipd.shape, spikes.shape)\n", + " (50,) (50, duration_steps, 2*anf_per_ear)\n", + " \n", + "----------------------------------------\n", + "Function Name: discretise\n", + "Description: \n", + " Discretize Interaural Phase Differences (IPDs) to generate class labels.\n", + "\n", + " The function maps IPDs, which are continuous values in the range (-pi/2, pi/2),\n", + " to discrete classes in the range [0, num_classes-1]. The resulting discrete values\n", + " are suitable for classification tasks.\n", + "\n", + " Parameters\n", + " ----------\n", + " ipds : Tensor\n", + " A tensor containing continuous IPD values. The values should be in the range (-pi/2, pi/2).\n", + "\n", + " Returns\n", + " -------\n", + " Tensor\n", + " A tensor containing the classification of IPD values, in the range [0, num_classes-1].\n", + "\n", + " Notes\n", + " -----\n", + " - Assumes the input `ipds` is a PyTorch tensor.\n", + " - `num_classes` should be defined in the surrounding scope.\n", + " - The output tensor will have the same shape as the input `ipds`.\n", + "\n", + " Examples\n", + " --------\n", + " >>> ipds = torch.tensor([-np.pi/2, 0, np.pi/2])\n", + " >>> ipd_indices = discretise(ipds)\n", + " \n", + "----------------------------------------\n", + "Function Name: continuise\n", + "Description: \n", + " This function maps IPD indices, which are discrete values in the range [0, num_classes-1],\n", + " back to continuous IPD values. The resulting continuous values are suitable for\n", + " representing the midpoints of the original IPD ranges in the continuous domain.\n", + "\n", + " Parameters\n", + " ----------\n", + " ipd_indices : array-like\n", + " An array or tensor of IPD indices, which are discrete values obtained from\n", + " discretizing continuous IPDs into `num_classes` bins by the function discretise(ipds).\n", + "\n", + " Returns\n", + " -------\n", + " array-like\n", + " An array or tensor of continuous IPD midpoints, corresponding to the provided\n", + " `ipd_indices`. The midpoints are computed based on the assumed discretization\n", + " strategy, and are in the range (-pi/2, pi/2).\n", + "\n", + " Notes\n", + " -----\n", + " - `num_classes` should be defined in the surrounding scope and should be the same\n", + " value that was used for discretization.\n", + " - The input `ipd_indices` and the output will have the same shape.\n", + " - The output type (e.g., NumPy array, PyTorch tensor) will match the input type.\n", + " \n", + "----------------------------------------\n", + "Function Name: init_weight_matrix\n", + "Description: \n", + " Initialize a weight matrix for a neural network layer using uniform distribution.\n", + "\n", + " The function initializes a weight matrix, `W`, with dimensions `(input_size, num_classes)`.\n", + " The matrix is initialized using a uniform distribution over `[-bound, bound]`, where\n", + " `bound` is computed as the inverse of the square root of the fan-in (number of input units).\n", + "\n", + " This initialization method helps in achieving faster convergence during training by\n", + " setting initial weights in a range that's inversely proportional to the square root\n", + " of the number of input units.\n", + "\n", + " Returns\n", + " -------\n", + " W = nn.Parameter\n", + " A tensor representing the initialized weight matrix. The tensor has the attribute\n", + " `requires_grad=True`, indicating that gradients will be computed with respect to this tensor\n", + " during the backward pass.\n", + "\n", + " Notes\n", + " -----\n", + " - `input_size` and `num_classes` should be defined in the surrounding scope.\n", + " - The tensor is moved to the device specified by the `device` variable and has the data type\n", + " specified by the `dtype` variable. Both `device` and `dtype` should be defined in the surrounding scope.\n", + " - The `requires_grad=True` argument in `nn.Parameter` ensures that gradients are computed for\n", + " this tensor, enabling learning of its values during optimization.\n", + "\n", + " Examples\n", + " --------\n", + " >>> W = init_weight_matrix()\n", + " >>> print(W.shape)\n", + " (input_size, num_classes)\n", + " \n", + "----------------------------------------\n", + "Function Name: membrane_only\n", + "Description: \n", + " Run a simulation of a membrane potential dynamic in response to input spikes.\n", + "\n", + " This function simulates the evolution of membrane potential `v` across time, given a batch of\n", + " input spike trains `input_spikes` and synaptic weight matrix `W`. The membrane potential is\n", + " updated at each time step based on the previous potential, the input spikes, and the synaptic weights,\n", + " with an exponential decay parameterized by `tau`.\n", + "\n", + " Parameters\n", + " ----------\n", + " input_spikes : Tensor\n", + " A 3D tensor representing a batch of input spike trains.\n", + " Shape: (batch_size, duration_steps, input_size)\n", + " W : Tensor\n", + " A 2D tensor representing the synaptic weight matrix.\n", + " Shape: (input_size, num_classes)\n", + " tau : float, optional\n", + " The time constant for the exponential decay of the membrane potential, in seconds.\n", + " Default is 20 ms.\n", + "\n", + " Returns\n", + " -------\n", + " v_rec : Tensor\n", + " A 3D tensor containing the recorded membrane potentials for each batch, time step, and class.\n", + " Shape: (batch_size, duration_steps, num_classes)\n", + "\n", + " Notes\n", + " -----\n", + " - `batch_size`, `num_classes`, `duration_steps`, `device`, and `dtype` should be defined in the\n", + " surrounding scope or passed as arguments.\n", + " - `input_spikes` should be a binary tensor, where `input_spikes[b, t, i]` is 1 if there is a spike\n", + " from input neuron `i` at time `t` in batch `b`, and 0 otherwise.\n", + " - `W` should contain synaptic weights such that `W[i, j]` is the weight from input neuron `i` to\n", + " output neuron `j`.\n", + "\n", + " Examples\n", + " --------\n", + " >>> input_spikes = torch.tensor([[[1, 0], [0, 1], [1, 1]]], dtype=dtype, device=device)\n", + " >>> W = torch.tensor([[0.5, -0.5], [-0.5, 0.5]], dtype=dtype, device=device)\n", + " >>> v_rec = membrane_only(input_spikes, W)\n", + " >>> print(v_rec.shape)\n", + " (1, 3, 2)\n", + " \n", + "----------------------------------------\n", + "Function Name: data_generator\n", + "Description: \n", + " Generate batches of data, iterating over IPDs and spikes in a randomized order.\n", + "\n", + " This generator function yields shuffled batches of interaural phase differences (IPDs) and spikes,\n", + " facilitating mini-batch gradient descent training of a model. The order of the data is randomized\n", + " to improve learning, mitigating the risk of the model memorizing the order of the training data\n", + " (overfitting) and helping the model generalize better to unseen data.\n", + "\n", + " Parameters\n", + " ----------\n", + " ipds : Tensor\n", + " A 1D tensor of IPD values.\n", + " Shape: (n_samples, )\n", + " spikes : Tensor\n", + " A 3D tensor representing a batch of input spike trains.\n", + " Shape: (n_samples, duration_steps, input_size)\n", + "\n", + " Yields\n", + " ------\n", + " spike_batch : Tensor\n", + " A 3D tensor containing a batch of input spike trains.\n", + " Shape: (batch_size, duration_steps, input_size)\n", + " ipd_batch : Tensor\n", + " A 1D tensor containing a batch of IPD values.\n", + " Shape: (batch_size, )\n", + "\n", + " Notes\n", + " -----\n", + " - `batch_size` should be defined in the surrounding scope or passed as an argument.\n", + " - Ensure that `ipds` and the first dimension of `spikes` have the same size.\n", + " - The generator yields `spike_batch` and `ipd_batch` which are randomly shuffled batches of `spikes` and `ipds` respectively.\n", + " \n", + "----------------------------------------\n", + "Function Name: analyse\n", + "Description: \n", + " Analyse the performance of a classifier on interaural phase difference (IPD) data.\n", + "\n", + " This function evaluates the accuracy and error of a classifier by comparing its\n", + " output with true IPD values. It computes the mean and standard deviation of the\n", + " classifier's accuracy and the absolute error in degrees. Additionally, it can\n", + " generate histograms and a confusion matrix to visualize the results.\n", + "\n", + " Parameters:\n", + " ipds (array): Array of true IPD values.\n", + " spikes (array): Array of spike data corresponding to the IPDs.\n", + " label (str): Label for the data, used in plot titles.\n", + " run (callable): Function that runs the classifier on a batch of spike data.\n", + " plot_analysis (bool): If True, plot histograms and confusion matrix.\n", + "\n", + " Returns:\n", + " tuple: Tuple containing mean and standard deviation of classifier accuracy,\n", + " and mean and standard deviation of absolute error in degrees.\n", + " \n", + "----------------------------------------\n", + "Function Name: run_func\n", + "Description: None\n", + "----------------------------------------\n", + "Function Name: plot_lines_with_colorbar\n", + "Description: \n", + " This function defines the colormap over which the line plot function cycles\n", + " and plots the corresponding colorbar next to the plot\n", + " # Example usage\n", + " x = np.linspace(0, 10, 100)\n", + " ys = [np.sin(x + i) for i in np.arange(0, 2, 0.5)]\n", + "\n", + " plt.figure(figsize=(8, 6))\n", + " plot_lines_with_colorbar(x, ys, cmap_name='jet')\n", + " plt.show()\n", + " \n", + "----------------------------------------\n", + "Function Name: softmax\n", + "Description: None\n", + "----------------------------------------\n", + "Function Name: log_softmax\n", + "Description: None\n", + "----------------------------------------\n", + "Function Name: init_weight_matrices\n", + "Description: None\n", + "----------------------------------------\n", + "Function Name: snn\n", + "Description: None\n", + "----------------------------------------\n", + "Function Name: example_function1\n", + "Description: This is a docstring for example_function1.\n", + "----------------------------------------\n", + "Function Name: example_function2\n", + "Description: This is a docstring for example_function2.\n", + "----------------------------------------\n" + ] + } + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + }, + "colab": { + "provenance": [], + "toc_visible": true + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file