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GSOC 2015
The Virtual Brain (TVB) is one of the few open source neuroinformatics platforms used to simulate whole brain dynamics. Models are not limited to the human brain but researchers can also work wirh the macaque's and/or the mouse's connectome. Models based on biologically realistic macroscopic connectivity will hopefully help us to understand the global dynamics observed in the healthy and diseased brain. Whether you are interested in beautiful visualizations or differential equations, you can join us and help us improve!
Several open issues addressed by the following proposals involve
- verifying numerical methods
- improving simulator performance
- enhancing data IO and visualization
Description: add some description here
Skills required: this, that and one of those
Expected results: Working and well documented code
Mentors: Alice & Bob
Description: Currently, TVB does not provide the tools to obtain a complete dataset from structural and diffusion MRI ready to use in simulations. Preparation of such data is not trivial. An semi-automatic pipeline, SCRIPTS, addressing the specific needs of TVB is freely available. The aim of this project is the integration of the pipeline into TVB to simplify the task of data reconstruction.
An API in python using nypipe will be provided. In particular, a GUI will be built (PyQt or equivalent in HTML5/WebGL) enabling a non-expert to perform the following steps:
- upload data
- visualize existing data
- launch pipeline
- view progress & visual verifiers
- visualize final results
Skills required: Shell scripting, Python, HTML5, JS, WebGL, D3.js. Some knowledge about MRI processing techniques is a plus.
Expected results: An adapter for TVB web interface allowing to run the pipeline and visualize the results.
Mentors: Timothée Proix (@timpx), Paula Sanz-Leon (@pausz), Marmaduke Woodman (@maedoc), Lia Domide (@liadomide)
Description: Assessing the numerical accuracy of a simulation is necessary in order to verify the results. This proposal involves using systems with known solutions (such as a linear ordinary differential equation) with TVB's numerical methods to determine how accurate the methods are. These results may be tested against methods implemented by other softwares such as XPPAUT.
Skills required: Python; Experience with differential equations, MATLAB would be helpful.
Expected results: Test suite for assessing integration accuracy, and documentation of accuracy tests in user guide.
Mentors: Paula Sanz-Leon (@pausz), Marmaduke Woodman (@maedoc), Mihai Andrei (@mihandrei)
Description Data visualization plays a crucial role in TVB's neuroinformatics platform; effective interactive visualization can improve users' experience by helping them to quickly explore large datasets. Doing it properly in TVB is challenging because the web browser is still a developing platform with respect to graphics. Several tasks related to this project are available. Interested students are urged to select one or more from the list based on time, interest & experience:
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a) Rewrite visualizers that are currently implemented using MatplotLib and MPLH5 with visualization libraries oriented toward web browsers, such as D3.js or Bokeh.
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b) Refactor one of the time-series visualizer currently available.
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c) Improve interactive editing of large matrices (O(1000^2)). Rendering performance as well as per-element interaction is important.
Skills required: HTML/JS/CSS & Python; Experience in web development, JQuery, SVG, WebGL, as well as server side frameworks such as CherryPy, is helpful.
Mentors: Lia Domide (@liadomide), Mihai Andrei (@mihandrei), Paula Sanz-Leon (@pausz)