From 0ab5241cf22d4c337908da8f6e4edfd9ace74abf Mon Sep 17 00:00:00 2001
From: pauladkisson <paul.wesley.adkisson@gmail.com>
Date: Wed, 18 Dec 2024 16:27:28 -0800
Subject: [PATCH] added example notebooks w/o streaming

---
 001259/README.md                    |   8 +
 001259/environment.yml              |  11 +
 001259/ephys_example_notebook.ipynb | 781 ++++++++++++++++++++++++++++
 001259/opto_example_notebook.ipynb  | 558 ++++++++++++++++++++
 001259/stream_nwbfile.py            |  36 ++
 5 files changed, 1394 insertions(+)
 create mode 100644 001259/README.md
 create mode 100644 001259/environment.yml
 create mode 100644 001259/ephys_example_notebook.ipynb
 create mode 100644 001259/opto_example_notebook.ipynb
 create mode 100644 001259/stream_nwbfile.py

diff --git a/001259/README.md b/001259/README.md
new file mode 100644
index 0000000..9b11b72
--- /dev/null
+++ b/001259/README.md
@@ -0,0 +1,8 @@
+# Example Sessions for Dandiset 001259
+
+This submission provides 2 notebooks showcasing example sessions for the Dandiset 001259.
+
+Each notebook provides an example of how to access the critical data and metadata from the 2 types of experiments in the dataset:
+
+- `ephys_example_notebook.ipynb` showcases one example session from the 001259 dataset containing operant behavior and concurrent OpenEphys recordings in primary auditory cortex (A1).
+- `optogenetics_example_notebook.ipynb` showcases one example session from the 001259 dataset containing operant behavior and concurrent optogenetic stimulation.
\ No newline at end of file
diff --git a/001259/environment.yml b/001259/environment.yml
new file mode 100644
index 0000000..9674997
--- /dev/null
+++ b/001259/environment.yml
@@ -0,0 +1,11 @@
+# run: conda env create --file environment.yml
+name: schneider_notebook_env
+channels:
+    - conda-forge
+dependencies:
+    - python==3.12
+    - ipywidgets
+    - pip
+    - pip:
+      - matplotlib
+      - lerner-lab-to-nwb @ git+https://github.com/catalystneuro/lerner-lab-to-nwb.git@main
\ No newline at end of file
diff --git a/001259/ephys_example_notebook.ipynb b/001259/ephys_example_notebook.ipynb
new file mode 100644
index 0000000..f7ad8e1
--- /dev/null
+++ b/001259/ephys_example_notebook.ipynb
@@ -0,0 +1,781 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pynwb import NWBHDF5IO\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "                color: #00788E;\n",
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+       "            .container-fields details > summary {\n",
+       "                cursor: pointer;\n",
+       "                display: list-item;\n",
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+       "            .container-fields details > summary:hover {\n",
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+       "        <script>\n",
+       "            function copyToClipboard(text) {\n",
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+       "                    console.error('Could not copy text: ', err);\n",
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+       "            }\n",
+       "\n",
+       "            document.addEventListener('DOMContentLoaded', function() {\n",
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+       "        </script>\n",
+       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>root (NWBFile)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".session_description\">session_description: </span><span class=\"field-value\">Mice performed the auditory guided task while electricophysiological neural activity was recorded in the primary auditory cortex (A1).</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".identifier\">identifier: </span><span class=\"field-value\">1fb4f618-1681-43b1-b541-cedcce1466cc</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".session_start_time\"><b>session_start_time</b></summary><span class=\"field-key\">2023-10-29 16:56:01-04:00</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".timestamps_reference_time\"><b>timestamps_reference_time</b></summary><span class=\"field-key\">2023-10-29 16:56:01-04:00</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".file_create_date\"><b>file_create_date</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".file_create_date[0]\"><b>0</b></summary><span class=\"field-key\">2024-12-18 09:06:51.025092-08:00</span></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".experimenter\"><b>experimenter</b></summary><span class=\"field-key\">('Zempolich, Grant W.', 'Schneider, David M.')</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".acquisition\"><b>acquisition</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".acquisition['ElectricalSeries']\"><b>ElectricalSeries</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].starting_time\">starting_time: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].rate\">rate: </span><span class=\"field-value\">30000.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].description\">description: </span><span class=\"field-value\">Recording of AC neural responses in mice performing this behavioral task utilized dense 128-channel recording probes (Masmanidis Lab). These recording probes span a depth ~1mm, allowing for sampling of all layers of cortex. Electrophysiology data was recorded using OpenEphys Acquisition Board v2.4 and associated OpenEphys GUI software.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].conversion\">conversion: </span><span class=\"field-value\">1.9499999999999999e-07</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].unit\">unit: </span><span class=\"field-value\">volts</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['ElectricalSeries'].data\"><b>data</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].starting_time_unit\">starting_time_unit: </span><span class=\"field-value\">seconds</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['ElectricalSeries'].electrodes\"><b>electrodes</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].electrodes.description\">description: </span><span class=\"field-value\">electrode_table_region</span></div><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".acquisition['ElectricalSeries'].electrodes.table\"><b>table</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].electrodes.table\">description: </span><span class=\"field-value\">metadata about extracellular electrodes</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".acquisition['ElectricalSeries'].electrodes.table\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>location</th>\n",
+       "      <th>group</th>\n",
+       "      <th>group_name</th>\n",
+       "      <th>channel_name</th>\n",
+       "      <th>rel_y</th>\n",
+       "      <th>rel_x</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Primary Auditory Cortex (A1)</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at 0x5118772528\\nFields:\\n  description: ElectrodeGroup for all channels in the recording probe.\\n  device: MasmanidisSiliconMicroprobe128AxN pynwb.device.Device at 0x5118623520\\nFields:\\n  description: Masmanidis Lab dense 128-channel recording probes (https://masmanidislab.neurobio.ucla.edu/technology.html).\\n  manufacturer: Masmanidis Lab\\n\\n  location: Primary Auditory Cortex (A1)\\n</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>CH16</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>200.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table></details></details></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1']\"><b>video_camera_1</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].description\">description: </span><span class=\"field-value\">Two IR video cameras (AAK CA20 600TVL 2.8MM) were used to monitor the experiments from different angles of interest, allowing for offline analysis of body movements, pupillometry, and other behavioral data as necessary. Camera 1 is a side angle view of the mouse.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].unit\">unit: </span><span class=\"field-value\">Frames</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].interval\">interval: </span><span class=\"field-value\">1</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].external_file\"><b>external_file</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].starting_frame\"><b>starting_frame</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">[0]</div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].format\">format: </span><span class=\"field-value\">external</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2']\"><b>video_camera_2</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].description\">description: </span><span class=\"field-value\">Two IR video cameras (AAK CA20 600TVL 2.8MM) were used to monitor the experiments from different angles of interest, allowing for offline analysis of body movements, pupillometry, and other behavioral data as necessary. Camera 2 is a zoomed-in view of the pupil of the mouse.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].unit\">unit: </span><span class=\"field-value\">Frames</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].interval\">interval: </span><span class=\"field-value\">1</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].external_file\"><b>external_file</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].starting_frame\"><b>starting_frame</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">[0]</div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].format\">format: </span><span class=\"field-value\">external</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".keywords\"><b>keywords</b></summary></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".processing\"><b>processing</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".processing['behavior']\"><b>behavior</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].description\">description: </span><span class=\"field-value\">C57BL/6 mice were first be water restricted, habituated to head fixation in the behavioral set up for two days and classically conditioned to associate a 16 kHz tone with a small water reward given 150 ms after the tone plays (~12 seconds inter-tone-interval). Mice were then be trained for 15 to 20 sessions on an auditory guided task described as follows. Inspired by human performance on stringed instruments, whereby a target note is achieved via modulation of forelimb and hand movements, we have engineered a novel behavioral paradigm that requires mice to skillfully adjust the size of lever presses in response to a dynamic virtual acoustic environment. Mice were trained to press a small 3-D printed lever forward with their forelimb toward a 2 mm wide target zone. Mice heard a 16 Hz tone when the lever enters the zone and a 10 kHz tone if the press exceeds the bounds of the zone. Presses that peak within the zone produce only the entry tone and are rewarded when the lever returns to the starting position. Presses that undershoot (producing no tones) or overshoot (producing both an entry and an exit tone) are unrewarded. Every 30 trials, which we refer to as a block, the target zone was relocated without warning and the mice must use acoustic feedback to adjust their lever presses to peak at the new location.</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces\"><b>data_interfaces</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series']\"><b>behavioral_time_series</b></summary><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series\"><b>time_series</b></summary><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder']\"><b>encoder</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].description\">description: </span><span class=\"field-value\">Sampled values for entire duration of experiment for lever pressing behavior read from a rotary encoder (US Digital). Digital signals for licking and lever movement were collected by a data acquisition card (National Instruments) connected to a computer and logged by custom Matlab software (Mathworks, PsychToolBox) and sampled at 2kHz.</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].unit\">unit: </span><span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].interval\">interval: </span><span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick']\"><b>lick</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].description\">description: </span><span class=\"field-value\">Samples values for entire duration of experiment for voltage signal readout from a custom infrared/capacitive lickometer sensor (Schneider Lab).  Digital signals for licking and lever movement were collected by a data acquisition card (National Instruments) connected to a computer and logged by custom Matlab software (Mathworks, PsychToolBox) and sampled at 2kHz.</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].unit\">unit: </span><span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].interval\">interval: </span><span class=\"field-value\">1</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['targetOUT']\"><b>targetOUT</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].description\">description: </span><span class=\"field-value\">Time at which the target zone is overshot during a press.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneIN']\"><b>toneIN</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].description\">description: </span><span class=\"field-value\">Time at which target zone is entered and target entry tone is played.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneOUT']\"><b>toneOUT</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].description\">description: </span><span class=\"field-value\">Time at which target exit tone is played (this is delayed 50ms relative to targetOUT so that entry and exit tones don't bleed into each other.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valve']\"><b>valve</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['valve'].description\">description: </span><span class=\"field-value\">Times at which solenoid valve opens to deliver water after a correct trial.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valve'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['valve'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valued_events_table']\"><b>valued_events_table</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['valued_events_table']\">description: </span><span class=\"field-value\">Metadata about valued events.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valued_events_table']\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "      <th>event_times</th>\n",
+       "      <th>label</th>\n",
+       "      <th>event_description</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, ...]</td>\n",
+       "      <td>[2017.8980716115984, 2018.9518499695782, 2020.003974437991, 2021.0560366067878, 2022.1107962585747, 2023.1668696021022, 2024.2185851730928, 2025.2744759177485, 2026.3273306815604, 2027.3805422431042, 2028.433056109059, 2029.4864062697368, 2030.5382417399826, 2031.5908030056212, 2032.6432292721304, 2033.6949235432678, 2034.7478345067166, 2035.8035136527246, 2036.858632502247, 2037.911757564347, 2038.9679882068874, 2040.0206993715876, 2041.073034538676, 2042.125070507642, 2043.177316575283, 2044.231457230973, 2045.2869840779456, 2046.3397792421156, 2047.3954766880022, 2048.4485546503893, 2049.5003617208013, 2050.562680124963, 2051.614361396171, 2052.6670118612574, 2053.720565420659, 2054.7738006820596, 2055.8256480522045, 2056.877617321607, 2057.929639990643, 2058.98234325542, 2060.034222725374, 2061.0869674898563, 2062.1391423579566, 2063.191559724513, 2064.2458221794604, 2065.2981518465704, 2066.3499962167407, 2067.4024316831747, 2068.454694650721, 2069.5074332152535, 2070.559872881664, 2071.6122939482248, 2072.6642931174047, 2073.717558778641, 2074.769599947556, 2075.8216661163287, 2076.8742220820186, 2077.9281686389386, 2078.979919009713, 2080.032674574151, 2081.085022241139, 2082.140088191, 2083.195803236793, 2084.252941573597, 2085.3072098284943, 2086.363447670986, 2087.4153593407204, 2088.4670414119287, 2089.5229857562476, 2090.5753192233337, 2091.63422514901, 2092.6878982076373, 2093.7414865668234, 2094.7941654317433, 2095.846430199263, 2096.8987763662785, 2097.951348931874, 2099.0051972893925, 2100.0587607487496, 2101.1111402155466, 2102.1648806737676, 2103.216960242471, 2104.269369109074, 2105.3220012742813, 2106.373825644571, 2107.426654208552, 2108.4840629436444, 2109.5373144049495, 2110.592115056499, 2111.646091113225, 2112.6982058817125, 2113.750644748122, 2114.8060345959625, 2115.858254063772, 2116.9107193300124, 2117.96300249744, 2119.015053166309, 2120.0683294274427, 2121.1231206790385, 2122.175454446154, ...]</td>\n",
+       "      <td>tuningTones</td>\n",
+       "      <td>Times at which tuning tones are played to an animal after a behavioral experiment during ephys recording sessions. Integer values correspond to the frequency of the tuning tone played i.e. 2 for 2kHz, 4 for 4kHz, etc.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table></details></details></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging']\"><b>intrinsic_signal_optical_imaging</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['intrinsic_signal_optical_imaging'].description\">description: </span><span class=\"field-value\">For precise targeting of auditory cortex, intrinsic optical imaging (IOS) was performed using a 2-photon microscope (Neurolabware). The skull was first bilaterally thinned over a region of interest (ROI) and made translucent. On experiment day, 680nm red light (ThorLabs) is used to image the ROI. Data was collected via MATLAB running custom suites for online and offline analyses.</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces\"><b>data_interfaces</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images']\"><b>images</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].description\">description: </span><span class=\"field-value\">Intrinsic signal optical images.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images\"><b>images</b></summary><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images['overlaid_image']\"><b>overlaid_image</b></summary></details><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images['target_image']\"><b>target_image</b></summary></details></details></details></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".epoch_tags\"><b>epoch_tags</b></summary><span class=\"field-key\">set()</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".electrodes\"><b>electrodes</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".electrodes\">description: </span><span class=\"field-value\">metadata about extracellular electrodes</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".electrodes\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>location</th>\n",
+       "      <th>group</th>\n",
+       "      <th>group_name</th>\n",
+       "      <th>channel_name</th>\n",
+       "      <th>rel_y</th>\n",
+       "      <th>rel_x</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Primary Auditory Cortex (A1)</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at 0x5118772528\\nFields:\\n  description: ElectrodeGroup for all channels in the recording probe.\\n  device: MasmanidisSiliconMicroprobe128AxN pynwb.device.Device at 0x5118623520\\nFields:\\n  description: Masmanidis Lab dense 128-channel recording probes (https://masmanidislab.neurobio.ucla.edu/technology.html).\\n  manufacturer: Masmanidis Lab\\n\\n  location: Primary Auditory Cortex (A1)\\n</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>CH16</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>200.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".electrode_groups\"><b>electrode_groups</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".electrode_groups['ElectrodeGroup']\"><b>ElectrodeGroup</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".electrode_groups['ElectrodeGroup'].description\">description: </span><span class=\"field-value\">ElectrodeGroup for all channels in the recording probe.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".electrode_groups['ElectrodeGroup'].location\">location: </span><span class=\"field-value\">Primary Auditory Cortex (A1)</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".electrode_groups['ElectrodeGroup'].device\"><b>device</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".electrode_groups['ElectrodeGroup'].device.description\">description: </span><span class=\"field-value\">Masmanidis Lab dense 128-channel recording probes (https://masmanidislab.neurobio.ucla.edu/technology.html).</span></div><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".electrode_groups['ElectrodeGroup'].device.manufacturer\">manufacturer: </span><span class=\"field-value\">Masmanidis Lab</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".devices\"><b>devices</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['MasmanidisSiliconMicroprobe128AxN']\"><b>MasmanidisSiliconMicroprobe128AxN</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['MasmanidisSiliconMicroprobe128AxN'].description\">description: </span><span class=\"field-value\">Masmanidis Lab dense 128-channel recording probes (https://masmanidislab.neurobio.ucla.edu/technology.html).</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['MasmanidisSiliconMicroprobe128AxN'].manufacturer\">manufacturer: </span><span class=\"field-value\">Masmanidis Lab</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser']\"><b>intrinsic_signal_optical_imaging_laser</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser'].description\">description: </span><span class=\"field-value\">ThorLabs 700nm fiber coupled LED (M700F3) driven by their basic LED driver (LEDD1B).</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser'].manufacturer\">manufacturer: </span><span class=\"field-value\">ThorLabs</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['lickometer']\"><b>lickometer</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['lickometer'].description\">description: </span><span class=\"field-value\">The lickometer comprised a custom-mounted (3D printed using Formlabs Form2) IR-beam emitter and receiver. IR signal was titrated and pre-processed using a custom printed circuit board (designed by Melissa Caras and Dan Sanes) to generate a binary TTL signal with IR sensitivity controlled by a potentiometer.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['lickometer'].manufacturer\">manufacturer: </span><span class=\"field-value\">Schneider Lab</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['rotary_encoder']\"><b>rotary_encoder</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['rotary_encoder'].description\">description: </span><span class=\"field-value\">H5 BALL BEARING OPTICAL SHAFT ENCODER</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['rotary_encoder'].manufacturer\">manufacturer: </span><span class=\"field-value\">US Digital</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['two_photon_microscope']\"><b>two_photon_microscope</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['two_photon_microscope'].description\">description: </span><span class=\"field-value\">Standard Microscope by Neurolabware.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['two_photon_microscope'].manufacturer\">manufacturer: </span><span class=\"field-value\">Neurolabware</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".intervals\"><b>intervals</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".intervals['epochs']\"><b>epochs</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".intervals['epochs']\">description: </span><span class=\"field-value\">experimental epochs</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".intervals['epochs']\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>tags</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1191.866913</td>\n",
+       "      <td>1993.435631</td>\n",
+       "      <td>[Active Behavior]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2017.898072</td>\n",
+       "      <td>2164.295733</td>\n",
+       "      <td>[Passive Listening]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".intervals['trials']\"><b>trials</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".intervals['trials']\">description: </span><span class=\"field-value\">experimental trials</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".intervals['trials']\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>rewarded</th>\n",
+       "      <th>time_reward_s</th>\n",
+       "      <th>opto_trial</th>\n",
+       "      <th>opto_time</th>\n",
+       "      <th>opto_time_end</th>\n",
+       "      <th>ITI_respect</th>\n",
+       "      <th>ThresholdVector</th>\n",
+       "      <th>endZone_ThresholdVector</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1191.866913</td>\n",
+       "      <td>1192.054760</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1192.917609</td>\n",
+       "      <td>1193.742364</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1194.216813</td>\n",
+       "      <td>1194.700726</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1196.710184</td>\n",
+       "      <td>1197.109255</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>... and 483 more rows.</p></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".subject\"><b>subject</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.age\">age: </span><span class=\"field-value\">P12W/</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.age__reference\">age__reference: </span><span class=\"field-value\">birth</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.description\">description: </span><span class=\"field-value\">12-week-old C57BL/6 or VGATChR2-EYFP mice were used for all behavioral, electrophysiology, and optogenetic experiments. In the VGAT-ChR2-EYFP mouse line, channelrhodopsin (ChR2) was coupled to the vesicular GABA transporter, inducing expression in GABAergic inhibitory neurons ubiquitously across cortex and allowing for real time optogenetic inhibition of brain regions of interest.</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.genotype\">genotype: </span><span class=\"field-value\">C57BL/6 or VGATChR2-EYFP</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.sex\">sex: </span><span class=\"field-value\">U</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.species\">species: </span><span class=\"field-value\">Mus musculus</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.subject_id\">subject_id: </span><span class=\"field-value\">m53</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.strain\">strain: </span><span class=\"field-value\">C57BL/6</span></div></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".epochs\"><b>epochs</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".epochs\">description: </span><span class=\"field-value\">experimental epochs</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".epochs\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>tags</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1191.866913</td>\n",
+       "      <td>1993.435631</td>\n",
+       "      <td>[Active Behavior]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2017.898072</td>\n",
+       "      <td>2164.295733</td>\n",
+       "      <td>[Passive Listening]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".trials\"><b>trials</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".trials\">description: </span><span class=\"field-value\">experimental trials</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".trials\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>rewarded</th>\n",
+       "      <th>time_reward_s</th>\n",
+       "      <th>opto_trial</th>\n",
+       "      <th>opto_time</th>\n",
+       "      <th>opto_time_end</th>\n",
+       "      <th>ITI_respect</th>\n",
+       "      <th>ThresholdVector</th>\n",
+       "      <th>endZone_ThresholdVector</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1191.866913</td>\n",
+       "      <td>1192.054760</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1192.917609</td>\n",
+       "      <td>1193.742364</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1194.216813</td>\n",
+       "      <td>1194.700726</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1196.710184</td>\n",
+       "      <td>1197.109255</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>... and 483 more rows.</p></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".units\"><b>units</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".units\">description: </span><span class=\"field-value\">Neural spikes were sorted offline using Kilosort 2.5 and Phy2 software and manually curated to ensure precise spike time acquisition.</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".units\">waveform_unit: </span><span class=\"field-value\">volts</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".units\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>spike_times</th>\n",
+       "      <th>unit_name</th>\n",
+       "      <th>sh</th>\n",
+       "      <th>n_spikes</th>\n",
+       "      <th>amp</th>\n",
+       "      <th>KSLabel</th>\n",
+       "      <th>original_cluster_id</th>\n",
+       "      <th>ContamPct</th>\n",
+       "      <th>fr</th>\n",
+       "      <th>ch</th>\n",
+       "      <th>quality</th>\n",
+       "      <th>Amplitude</th>\n",
+       "      <th>depth</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[3.5251333333333332, 3.5982666666666665, 3.615466666666667, 3.6355, 3.667133333333333, 3.673233333333333, 3.6804, 3.6820666666666666, 3.719533333333333, 3.7214, 3.7286333333333332, 3.7361, 3.7454666666666667, 3.7516666666666665, 3.7582333333333335, 3.7706333333333335, 3.7723333333333335, 3.7761666666666667, 3.7828, 3.789266666666667, 3.7908, 3.7944333333333335, 3.7975, 3.801433333333333, 3.8028333333333335, 3.8101, 3.8163, 3.8306666666666667, 3.8476, 3.8533333333333335, 3.8613333333333335, 3.8677333333333332, 3.8763, 3.8783333333333334, 3.8937333333333335, 3.8977333333333335, 3.9047666666666667, 3.9154666666666667, 3.9199, 3.9278, 3.9289666666666667, 3.9656, 3.9732666666666665, 3.9785, 3.9968333333333335, 4.253466666666666, 7.058, 7.0735, 7.076233333333334, 7.093833333333333, 7.0957333333333334, 7.117, 7.1205, 7.1216333333333335, 7.122833333333333, 7.1245666666666665, 7.1294, 7.131433333333334, 7.1349, 7.138366666666666, 7.144266666666667, 7.145533333333334, 7.151666666666666, 7.1612, 7.1795333333333335, 7.1817, 7.194966666666667, 7.198966666666666, 7.227566666666666, 7.2441, 7.253466666666666, 7.256, 7.270533333333334, 7.2833, 7.3402666666666665, 7.348166666666667, 7.385433333333333, 7.3868, 7.429133333333334, 7.468233333333333, 7.493733333333333, 7.500633333333333, 7.533966666666666, 7.572033333333334, 12.2529, 12.287133333333333, 12.332033333333333, 12.345333333333333, 12.362333333333334, 12.431366666666667, 12.447833333333334, 12.468133333333334, 12.476366666666667, 12.4821, 12.4863, 12.489433333333332, 12.491666666666667, 12.497033333333333, 12.509133333333333, 12.5132, ...]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>25481</td>\n",
+       "      <td>115.930557</td>\n",
+       "      <td>mua</td>\n",
+       "      <td>0</td>\n",
+       "      <td>80.7</td>\n",
+       "      <td>9.435566</td>\n",
+       "      <td>127</td>\n",
+       "      <td>noise</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[3.5561333333333334, 3.5773, 3.5833666666666666, 3.5907, 3.6018333333333334, 3.607, 3.6252333333333335, 3.6276, 3.630333333333333, 3.636433333333333, 3.672, 3.6748, 3.689233333333333, 3.6984, 3.7062, 3.712666666666667, 3.7167666666666666, 3.722366666666667, 3.7267333333333332, 3.7298, 3.7329333333333334, 3.7546666666666666, 3.759533333333333, 3.761833333333333, 3.7788666666666666, 3.7809333333333335, 3.784, 3.8009, 3.804866666666667, 3.807, 3.8131, 3.8219333333333334, 3.8369, 3.8399, 3.8489333333333335, 3.872733333333333, 3.875, 3.8805666666666667, 3.9079333333333333, 3.9105666666666665, 3.9217333333333335, 3.923566666666667, 3.9467333333333334, 3.9625666666666666, 3.9875333333333334, 4.0126333333333335, 4.046733333333333, 7.04, 7.048466666666667, 7.0652333333333335, 7.066766666666667, 7.071133333333333, 7.075466666666666, 7.080466666666666, 7.083566666666667, 7.084633333333334, 7.0874, 7.0924, 7.099066666666666, 7.102333333333333, 7.105033333333333, 7.108833333333333, 7.1101, 7.111666666666666, 7.1146, 7.1165666666666665, 7.118266666666667, 7.124033333333333, 7.129833333333333, 7.1328, 7.147766666666667, 7.1531666666666665, 7.157033333333334, 7.1607666666666665, 7.163333333333333, 7.165933333333333, 7.1679, 7.170233333333333, 7.172533333333333, 7.173666666666667, 7.174633333333333, 7.177266666666666, 7.189033333333334, 7.202366666666666, 7.203533333333334, 7.2062, 7.208933333333333, 7.2103, 7.215233333333333, 7.221533333333333, 7.2261, 7.233466666666667, 7.2365, 7.240233333333333, 7.259133333333334, 7.263366666666666, 7.2657, 7.2673, 7.289833333333333, 7.308666666666666, ...]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>35443</td>\n",
+       "      <td>126.206665</td>\n",
+       "      <td>mua</td>\n",
+       "      <td>1</td>\n",
+       "      <td>62.4</td>\n",
+       "      <td>13.124476</td>\n",
+       "      <td>127</td>\n",
+       "      <td>noise</td>\n",
+       "      <td>13.2</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[0.12136666666666666, 0.8584333333333334, 1.2345333333333333, 3.9112, 5.9219333333333335, 9.9444, 12.450633333333334, 14.325033333333334, 14.3353, 14.5326, 18.6813, 23.658733333333334, 28.391166666666667, 37.837133333333334, 40.08743333333334, 41.8005, 41.903533333333336, 49.185966666666666, 56.691766666666666, 60.7654, 65.25846666666666, 66.39336666666667, 66.88976666666667, 70.34863333333334, 71.04976666666667, 73.94253333333333, 74.4071, 74.6132, 81.01176666666667, 82.65003333333334, 84.92176666666667, 85.42363333333333, 93.15026666666667, 97.1867, 97.19306666666667, 97.2139, 97.38656666666667, 97.42413333333333, 97.6594, 97.6653, 97.6856, 97.86983333333333, 98.4364, 98.4886, 98.82413333333334, 99.05023333333334, 99.52346666666666, 99.52543333333334, 100.2741, 100.2827, 101.2859, 102.92483333333334, 103.9318, 104.3477, 105.0469, 105.2011, 106.22393333333333, 106.25263333333334, 106.41993333333333, 108.4289, 109.03933333333333, 109.5766, 109.80056666666667, 109.87266666666666, 110.75076666666666, 110.92793333333333, 112.9565, 112.98053333333333, 113.53836666666666, 114.05953333333333, 114.5931, 115.62396666666666, 116.19866666666667, 116.86976666666666, 116.8932, 116.94683333333333, 118.0651, 118.55736666666667, 118.57543333333334, 118.8569, 118.9409, 119.22193333333334, 119.87903333333334, 119.9101, 119.91233333333334, 120.007, 120.09633333333333, 120.14376666666666, 120.9085, 120.93486666666666, 121.11313333333334, 121.12623333333333, 121.22286666666666, 121.22786666666667, 122.9742, 123.09116666666667, 123.1092, 123.1423, 123.53393333333334, 125.6168, ...]</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>12217</td>\n",
+       "      <td>116.071793</td>\n",
+       "      <td>mua</td>\n",
+       "      <td>2</td>\n",
+       "      <td>49.8</td>\n",
+       "      <td>4.523932</td>\n",
+       "      <td>63</td>\n",
+       "      <td>noise</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[3.7224333333333335, 9.597633333333333, 12.7993, 12.8781, 16.0953, 27.6601, 27.8507, 28.9355, 29.759833333333333, 33.2042, 40.22743333333333, 41.89406666666667, 42.1827, 42.194966666666666, 42.94793333333333, 54.178066666666666, 56.0236, 59.0662, 60.64053333333333, 60.64763333333333, 60.76893333333334, 60.851, 61.1273, 63.092866666666666, 70.4433, 71.1334, 81.05643333333333, 81.0611, 81.08413333333333, 81.24146666666667, 81.48196666666666, 88.2934, 91.73243333333333, 96.785, 97.18263333333333, 97.19186666666667, 97.27646666666666, 97.50363333333334, 98.0959, 98.18673333333334, 98.71266666666666, 99.0543, 99.05646666666667, 99.5565, 99.56853333333333, 99.57726666666667, 102.64806666666667, 102.65586666666667, 102.89906666666667, 103.50246666666666, 103.6804, 104.14823333333334, 104.19613333333334, 104.62423333333334, 105.57153333333333, 105.73293333333334, 105.88566666666667, 105.99263333333333, 106.2862, 106.4737, 106.8489, 107.3072, 107.99803333333334, 108.38136666666666, 108.45516666666667, 109.1129, 109.27656666666667, 109.31046666666667, 109.3976, 109.81196666666666, 110.6496, 110.84356666666666, 110.96526666666666, 111.6309, 111.7842, 112.01633333333334, 112.08223333333333, 112.9762, 113.05623333333334, 113.51366666666667, 113.78466666666667, 114.62746666666666, 114.64606666666667, 114.65686666666667, 115.90943333333334, 116.88066666666667, 117.24156666666667, 118.57846666666667, 120.12076666666667, 120.17023333333333, 120.411, 121.76593333333334, 122.1256, 122.51696666666666, 123.53856666666667, 123.59466666666667, 125.3487, 126.73206666666667, 126.78536666666666, 127.2204, ...]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>11622</td>\n",
+       "      <td>116.476280</td>\n",
+       "      <td>mua</td>\n",
+       "      <td>3</td>\n",
+       "      <td>50.2</td>\n",
+       "      <td>4.303605</td>\n",
+       "      <td>63</td>\n",
+       "      <td>noise</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>... and 231 more rows.</p></details></details><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".experiment_description\">experiment_description: </span><span class=\"field-value\">Identifying mistakes is important for improving performance during acoustic behaviors like speech and musicianship. Although hearing is instrumental for monitoring and adapting these behaviors, the neural circuits that integrate motor, acoustic, and goal-related signals to detect errors and guide ongoing sensorimotor adaptation in mammals remain unidentified. Here, we develop a novel closed-loop, sound-guided behavior that requires mice to use real-time acoustic feedback to guide skilled ongoing forelimb movements. Large scale electrophysiology recordings reveal that the mouse auditory cortex integrates information about sound and movement, as well as encodes error- and learning-related signals during this sound-generating behavior. Distinct groups of auditory cortex neurons signal different error types, and the activity of these neurons predicts both within-trial and across-trial behavioral adaptations. Brief, behavior-triggered optogenetic suppression of auditory cortex during error signaling hinders behavioral corrections on both rapid and long time scales, indicating that cortical error signals are necessary for skilled acoustic behaviors. Together, these experiments identify a cortical role for detecting errors and learning from mistakes and suggest that the auditory cortex plays a critical role in skilled, sound-generating behavior in mammals.</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".session_id\">session_id: </span><span class=\"field-value\">231029</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".lab\">lab: </span><span class=\"field-value\">Schneider</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".institution\">institution: </span><span class=\"field-value\">New York University</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".source_script\">source_script: </span><span class=\"field-value\">Created using NeuroConv v0.6.5</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".source_script_file_name\">source_script_file_name: </span><span class=\"field-value\">/opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/neuroconv/basedatainterface.py</span></div></div>"
+      ],
+      "text/plain": [
+       "root pynwb.file.NWBFile at 0x4945540784\n",
+       "Fields:\n",
+       "  acquisition: {\n",
+       "    ElectricalSeries <class 'pynwb.ecephys.ElectricalSeries'>,\n",
+       "    video_camera_1 <class 'pynwb.image.ImageSeries'>,\n",
+       "    video_camera_2 <class 'pynwb.image.ImageSeries'>\n",
+       "  }\n",
+       "  devices: {\n",
+       "    MasmanidisSiliconMicroprobe128AxN <class 'pynwb.device.Device'>,\n",
+       "    intrinsic_signal_optical_imaging_laser <class 'pynwb.device.Device'>,\n",
+       "    lickometer <class 'pynwb.device.Device'>,\n",
+       "    rotary_encoder <class 'pynwb.device.Device'>,\n",
+       "    two_photon_microscope <class 'pynwb.device.Device'>\n",
+       "  }\n",
+       "  electrode_groups: {\n",
+       "    ElectrodeGroup <class 'pynwb.ecephys.ElectrodeGroup'>\n",
+       "  }\n",
+       "  electrodes: electrodes <class 'hdmf.common.table.DynamicTable'>\n",
+       "  epochs: epochs <class 'pynwb.epoch.TimeIntervals'>\n",
+       "  experiment_description: Identifying mistakes is important for improving performance during acoustic behaviors like speech and musicianship. Although hearing is instrumental for monitoring and adapting these behaviors, the neural circuits that integrate motor, acoustic, and goal-related signals to detect errors and guide ongoing sensorimotor adaptation in mammals remain unidentified. Here, we develop a novel closed-loop, sound-guided behavior that requires mice to use real-time acoustic feedback to guide skilled ongoing forelimb movements. Large scale electrophysiology recordings reveal that the mouse auditory cortex integrates information about sound and movement, as well as encodes error- and learning-related signals during this sound-generating behavior. Distinct groups of auditory cortex neurons signal different error types, and the activity of these neurons predicts both within-trial and across-trial behavioral adaptations. Brief, behavior-triggered optogenetic suppression of auditory cortex during error signaling hinders behavioral corrections on both rapid and long time scales, indicating that cortical error signals are necessary for skilled acoustic behaviors. Together, these experiments identify a cortical role for detecting errors and learning from mistakes and suggest that the auditory cortex plays a critical role in skilled, sound-generating behavior in mammals.\n",
+       "  experimenter: ['Zempolich, Grant W.' 'Schneider, David M.']\n",
+       "  file_create_date: [datetime.datetime(2024, 12, 18, 9, 6, 51, 25092, tzinfo=tzoffset(None, -28800))]\n",
+       "  identifier: 1fb4f618-1681-43b1-b541-cedcce1466cc\n",
+       "  institution: New York University\n",
+       "  intervals: {\n",
+       "    epochs <class 'pynwb.epoch.TimeIntervals'>,\n",
+       "    trials <class 'pynwb.epoch.TimeIntervals'>\n",
+       "  }\n",
+       "  keywords: <StrDataset for HDF5 dataset \"keywords\": shape (3,), type \"|O\">\n",
+       "  lab: Schneider\n",
+       "  processing: {\n",
+       "    behavior <class 'pynwb.base.ProcessingModule'>,\n",
+       "    intrinsic_signal_optical_imaging <class 'pynwb.base.ProcessingModule'>\n",
+       "  }\n",
+       "  session_description: Mice performed the auditory guided task while electricophysiological neural activity was recorded in the primary auditory cortex (A1).\n",
+       "  session_id: 231029\n",
+       "  session_start_time: 2023-10-29 16:56:01-04:00\n",
+       "  source_script: Created using NeuroConv v0.6.5\n",
+       "  source_script_file_name: /opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/neuroconv/basedatainterface.py\n",
+       "  subject: subject pynwb.file.Subject at 0x4946217632\n",
+       "Fields:\n",
+       "  age: P12W/\n",
+       "  age__reference: birth\n",
+       "  description: 12-week-old C57BL/6 or VGATChR2-EYFP mice were used for all behavioral, electrophysiology, and optogenetic experiments. In the VGAT-ChR2-EYFP mouse line, channelrhodopsin (ChR2) was coupled to the vesicular GABA transporter, inducing expression in GABAergic inhibitory neurons ubiquitously across cortex and allowing for real time optogenetic inhibition of brain regions of interest.\n",
+       "  genotype: C57BL/6 or VGATChR2-EYFP\n",
+       "  sex: U\n",
+       "  species: Mus musculus\n",
+       "  strain: C57BL/6\n",
+       "  subject_id: m53\n",
+       "\n",
+       "  timestamps_reference_time: 2023-10-29 16:56:01-04:00\n",
+       "  trials: trials <class 'pynwb.epoch.TimeIntervals'>\n",
+       "  units: units <class 'pynwb.misc.Units'>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nwbfile_path = '/Volumes/T7/CatalystNeuro/Schneider/conversion_nwb/sub-m53_ses-231029.nwb'\n",
+    "io = NWBHDF5IO(nwbfile_path, 'r')\n",
+    "nwbfile = io.read()\n",
+    "display(nwbfile)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Retrieve Ephys and Behavioral Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ephys\n",
+    "electrical_series = np.asarray(nwbfile.acquisition[\"ElectricalSeries\"].data[:, 0])\n",
+    "fs = nwbfile.acquisition[\"ElectricalSeries\"].rate\n",
+    "raw_to_uV = nwbfile.acquisition[\"ElectricalSeries\"].conversion * 1e6\n",
+    "electrical_series_in_uV = electrical_series * raw_to_uV\n",
+    "electrical_series_timestamps = np.arange(0, len(electrical_series)) / fs\n",
+    "\n",
+    "# Sorted Units\n",
+    "units = nwbfile.units.to_dataframe()\n",
+    "good_units = units[units.quality==\"good\"]\n",
+    "\n",
+    "# Behavioral Events\n",
+    "tone_in = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"toneIN\"].timestamps)\n",
+    "tone_out = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"toneOUT\"].timestamps)\n",
+    "target_out = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"targetOUT\"].timestamps)\n",
+    "valve = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"valve\"].timestamps)\n",
+    "\n",
+    "# Behavioral Time Series\n",
+    "encoder_data = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"behavioral_time_series\"][\"encoder\"].data)\n",
+    "encoder_timestamps = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"behavioral_time_series\"][\"encoder\"].timestamps)\n",
+    "lick_data = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"behavioral_time_series\"][\"lick\"].data)\n",
+    "lick_timestamps = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"behavioral_time_series\"][\"lick\"].timestamps)\n",
+    "\n",
+    "# Behavioral Trials\n",
+    "trials = nwbfile.trials.to_dataframe()\n",
+    "example_rewarded_trial = trials[trials[\"rewarded\"] == True].iloc[0]\n",
+    "example_unrewarded_trial = trials[trials[\"rewarded\"] == False].iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot an Example Rewarded Trial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/matplotlib/cbook.py:1762: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  return math.isfinite(val)\n",
+      "/opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/matplotlib/cbook.py:1398: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  return np.asarray(x, float)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "example_trial = example_rewarded_trial\n",
+    "\n",
+    "# Define plot parameters\n",
+    "lineoffsets = 0\n",
+    "linelengths = 750\n",
+    "ylim = [-500, 500]\n",
+    "grace_time = 0.100\n",
+    "\n",
+    "# Define time windows for plotting\n",
+    "plot_start_time = example_trial[\"start_time\"] - grace_time\n",
+    "plot_end_time = example_trial[\"stop_time\"] + grace_time\n",
+    "electrical_slice = slice(int(plot_start_time * fs), int(plot_end_time * fs))\n",
+    "tone_in_mask = (tone_in >= plot_start_time) & (tone_in <= plot_end_time)\n",
+    "tone_out_mask = (tone_out >= plot_start_time) & (tone_out <= plot_end_time)\n",
+    "target_out_mask = (target_out >= plot_start_time) & (target_out <= plot_end_time)\n",
+    "valve_mask = (valve >= plot_start_time) & (valve <= plot_end_time)\n",
+    "encoder_mask = (encoder_timestamps >= plot_start_time) & (encoder_timestamps <= plot_end_time)\n",
+    "lick_mask = (lick_timestamps >= plot_start_time) & (lick_timestamps <= plot_end_time)\n",
+    "\n",
+    "# Normalize times to trial start time\n",
+    "trial_start_time = example_trial[\"start_time\"]\n",
+    "normalized_tone_in = tone_in[tone_in_mask] - trial_start_time\n",
+    "normalized_tone_out = tone_out[tone_out_mask] - trial_start_time\n",
+    "normalized_target_out = target_out[target_out_mask] - trial_start_time\n",
+    "normalized_valve = valve[valve_mask] - trial_start_time\n",
+    "normalized_electrical_series_timestamps = electrical_series_timestamps[electrical_slice] - trial_start_time\n",
+    "normalized_encoder_timestamps = encoder_timestamps[encoder_mask] - trial_start_time\n",
+    "normalized_lick_timestamps = lick_timestamps[lick_mask] - trial_start_time\n",
+    "\n",
+    "unit_masks, normalized_spike_times = [], []\n",
+    "for _, unit in good_units.iterrows():\n",
+    "    unit_mask = np.logical_and(unit.spike_times >= plot_start_time, unit.spike_times <= plot_end_time)\n",
+    "    spike_times = unit[\"spike_times\"][unit_mask] - trial_start_time\n",
+    "    unit_masks.append(unit_mask)\n",
+    "    normalized_spike_times.append(spike_times)\n",
+    "\n",
+    "fig, axs = plt.subplots(4, 1, figsize=(20, 10), sharex=True)\n",
+    "axs[0].set_title(\"Example Rewarded Trial\")\n",
+    "axs[0].plot(normalized_electrical_series_timestamps, electrical_series_in_uV[electrical_slice], color=\"k\", label=\"Electrical Series\")\n",
+    "axs[0].eventplot(normalized_tone_in, color=\"red\", label=\"Tone In\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_tone_out, color=\"blue\", label=\"Tone Out\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_target_out, color=\"green\", label=\"Target Out\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_valve, color=\"purple\", label=\"Valve\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].axvline(0, color=\"black\", linestyle=\"--\", label=\"Start Time\")\n",
+    "axs[0].axvline(example_trial[\"stop_time\"] - example_trial[\"start_time\"], color=\"black\", linestyle=\"--\", label=\"Stop Time\")\n",
+    "axs[0].set_ylim(ylim)\n",
+    "axs[0].set_ylabel(\"Channel 1 Raw Voltage (uV)\")\n",
+    "axs[0].legend()\n",
+    "\n",
+    "axs[1].eventplot(normalized_spike_times, colors=\"k\")\n",
+    "axs[1].set_yticks([])\n",
+    "axs[1].set_ylabel(\"'Good' Unit Spikes\")\n",
+    "\n",
+    "axs[2].plot(normalized_encoder_timestamps, encoder_data[encoder_mask], color=\"k\", label=\"Encoder\")\n",
+    "axs[2].axhline(example_trial[\"ThresholdVector\"], color=\"black\", linestyle=\"--\", label=\"Threshold Vector\")\n",
+    "axs[2].axhline(example_trial[\"endZone_ThresholdVector\"], color=\"red\", linestyle=\"--\", label=\"End Zone Threshold Vector\")\n",
+    "axs[2].set_ylabel(\"Rotary Encoder (a.u.)\")\n",
+    "axs[2].legend()\n",
+    "\n",
+    "axs[3].plot(normalized_lick_timestamps, lick_data[lick_mask], color=\"k\", label=\"Lick\")\n",
+    "axs[3].set_ylabel(\"Lick Sensor (a.u.)\")\n",
+    "_ = axs[3].set_xlabel(\"Time Since Trial Start (s)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot an Example Unrewarded Trial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "example_trial = example_unrewarded_trial\n",
+    "\n",
+    "# Define plot parameters\n",
+    "lineoffsets = 0\n",
+    "linelengths = 400\n",
+    "ylim = [-250, 250]\n",
+    "grace_time = 0.100\n",
+    "\n",
+    "# Define time windows for plotting\n",
+    "plot_start_time = example_trial[\"start_time\"] - grace_time\n",
+    "plot_end_time = example_trial[\"stop_time\"] + grace_time\n",
+    "electrical_slice = slice(int(plot_start_time * fs), int(plot_end_time * fs))\n",
+    "tone_in_mask = (tone_in >= plot_start_time) & (tone_in <= plot_end_time)\n",
+    "tone_out_mask = (tone_out >= plot_start_time) & (tone_out <= plot_end_time)\n",
+    "target_out_mask = (target_out >= plot_start_time) & (target_out <= plot_end_time)\n",
+    "valve_mask = (valve >= plot_start_time) & (valve <= plot_end_time)\n",
+    "encoder_mask = (encoder_timestamps >= plot_start_time) & (encoder_timestamps <= plot_end_time)\n",
+    "lick_mask = (lick_timestamps >= plot_start_time) & (lick_timestamps <= plot_end_time)\n",
+    "\n",
+    "# Normalize times to trial start time\n",
+    "trial_start_time = example_trial[\"start_time\"]\n",
+    "normalized_tone_in = tone_in[tone_in_mask] - trial_start_time\n",
+    "normalized_tone_out = tone_out[tone_out_mask] - trial_start_time\n",
+    "normalized_target_out = target_out[target_out_mask] - trial_start_time\n",
+    "normalized_valve = valve[valve_mask] - trial_start_time\n",
+    "normalized_electrical_series_timestamps = electrical_series_timestamps[electrical_slice] - trial_start_time\n",
+    "normalized_encoder_timestamps = encoder_timestamps[encoder_mask] - trial_start_time\n",
+    "normalized_lick_timestamps = lick_timestamps[lick_mask] - trial_start_time\n",
+    "\n",
+    "unit_masks, normalized_spike_times = [], []\n",
+    "for _, unit in good_units.iterrows():\n",
+    "    unit_mask = np.logical_and(unit.spike_times >= plot_start_time, unit.spike_times <= plot_end_time)\n",
+    "    spike_times = unit[\"spike_times\"][unit_mask] - trial_start_time\n",
+    "    unit_masks.append(unit_mask)\n",
+    "    normalized_spike_times.append(spike_times)\n",
+    "\n",
+    "fig, axs = plt.subplots(4, 1, figsize=(20, 10), sharex=True)\n",
+    "axs[0].set_title(\"Example Unrewarded Trial\")\n",
+    "axs[0].plot(normalized_electrical_series_timestamps, electrical_series_in_uV[electrical_slice], color=\"k\", label=\"Electrical Series\")\n",
+    "axs[0].eventplot(normalized_tone_in, color=\"red\", label=\"Tone In\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_tone_out, color=\"blue\", label=\"Tone Out\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_target_out, color=\"green\", label=\"Target Out\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_valve, color=\"purple\", label=\"Valve\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].axvline(0, color=\"black\", linestyle=\"--\", label=\"Start Time\")\n",
+    "axs[0].axvline(example_trial[\"stop_time\"] - example_trial[\"start_time\"], color=\"black\", linestyle=\"--\", label=\"Stop Time\")\n",
+    "axs[0].set_ylim(ylim)\n",
+    "axs[0].set_ylabel(\"Channel 1 Raw Voltage (uV)\")\n",
+    "axs[0].legend()\n",
+    "\n",
+    "axs[1].eventplot(normalized_spike_times, colors=\"k\")\n",
+    "axs[1].set_yticks([])\n",
+    "axs[1].set_ylabel(\"'Good' Unit Spikes\")\n",
+    "\n",
+    "axs[2].plot(normalized_encoder_timestamps, encoder_data[encoder_mask], color=\"k\", label=\"Encoder\")\n",
+    "axs[2].axhline(example_trial[\"ThresholdVector\"], color=\"black\", linestyle=\"--\", label=\"Threshold Vector\")\n",
+    "axs[2].axhline(example_trial[\"endZone_ThresholdVector\"], color=\"red\", linestyle=\"--\", label=\"End Zone Threshold Vector\")\n",
+    "axs[2].set_ylabel(\"Rotary Encoder (a.u.)\")\n",
+    "axs[2].legend()\n",
+    "\n",
+    "axs[3].plot(normalized_lick_timestamps, lick_data[lick_mask], color=\"k\", label=\"Lick\")\n",
+    "axs[3].set_ylabel(\"Lick Sensor (a.u.)\")\n",
+    "_ = axs[3].set_xlabel(\"Time Since Trial Start (s)\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "schneider_notebook_env",
+   "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": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/001259/opto_example_notebook.ipynb b/001259/opto_example_notebook.ipynb
new file mode 100644
index 0000000..c5b2c43
--- /dev/null
+++ b/001259/opto_example_notebook.ipynb
@@ -0,0 +1,558 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pynwb import NWBHDF5IO\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <style>\n",
+       "            .container-fields {\n",
+       "                font-family: \"Open Sans\", Arial, sans-serif;\n",
+       "            }\n",
+       "            .container-fields .field-value {\n",
+       "                color: #00788E;\n",
+       "            }\n",
+       "            .container-fields details > summary {\n",
+       "                cursor: pointer;\n",
+       "                display: list-item;\n",
+       "            }\n",
+       "            .container-fields details > summary:hover {\n",
+       "                color: #0A6EAA;\n",
+       "            }\n",
+       "        </style>\n",
+       "        \n",
+       "        <script>\n",
+       "            function copyToClipboard(text) {\n",
+       "                navigator.clipboard.writeText(text).then(function() {\n",
+       "                    console.log('Copied to clipboard: ' + text);\n",
+       "                }, function(err) {\n",
+       "                    console.error('Could not copy text: ', err);\n",
+       "                });\n",
+       "            }\n",
+       "\n",
+       "            document.addEventListener('DOMContentLoaded', function() {\n",
+       "                let fieldKeys = document.querySelectorAll('.container-fields .field-key');\n",
+       "                fieldKeys.forEach(function(fieldKey) {\n",
+       "                    fieldKey.addEventListener('click', function() {\n",
+       "                        let accessCode = fieldKey.getAttribute('title').replace('Access code: ', '');\n",
+       "                        copyToClipboard(accessCode);\n",
+       "                    });\n",
+       "                });\n",
+       "            });\n",
+       "        </script>\n",
+       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>root (NWBFile)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".session_description\">session_description: </span><span class=\"field-value\">Mice performed the auditory guided task while optogenetic stimulation was delivered to the primary auditory cortex (A1).</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".identifier\">identifier: </span><span class=\"field-value\">b6a37a08-f2ab-45af-b49b-225aa17c8012</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".session_start_time\"><b>session_start_time</b></summary><span class=\"field-key\">2023-10-13 00:00:00-04:00</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".timestamps_reference_time\"><b>timestamps_reference_time</b></summary><span class=\"field-key\">2023-10-13 00:00:00-04:00</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".file_create_date\"><b>file_create_date</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".file_create_date[0]\"><b>0</b></summary><span class=\"field-key\">2024-12-18 09:07:21.778849-08:00</span></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".experimenter\"><b>experimenter</b></summary><span class=\"field-key\">('Zempolich, Grant W.', 'Schneider, David M.')</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".acquisition\"><b>acquisition</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1']\"><b>video_camera_1</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].description\">description: </span><span class=\"field-value\">Two IR video cameras (AAK CA20 600TVL 2.8MM) were used to monitor the experiments from different angles of interest, allowing for offline analysis of body movements, pupillometry, and other behavioral data as necessary. Camera 1 is a side angle view of the mouse.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].unit\">unit: </span><span class=\"field-value\">Frames</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].interval\">interval: </span><span class=\"field-value\">1</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].external_file\"><b>external_file</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].starting_frame\"><b>starting_frame</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">[0]</div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].format\">format: </span><span class=\"field-value\">external</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2']\"><b>video_camera_2</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].description\">description: </span><span class=\"field-value\">Two IR video cameras (AAK CA20 600TVL 2.8MM) were used to monitor the experiments from different angles of interest, allowing for offline analysis of body movements, pupillometry, and other behavioral data as necessary. Camera 2 is a zoomed-in view of the pupil of the mouse.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].unit\">unit: </span><span class=\"field-value\">Frames</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].interval\">interval: </span><span class=\"field-value\">1</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].external_file\"><b>external_file</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].starting_frame\"><b>starting_frame</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">[0]</div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].format\">format: </span><span class=\"field-value\">external</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".stimulus\"><b>stimulus</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".stimulus['optogenetic_series']\"><b>optogenetic_series</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].description\">description: </span><span class=\"field-value\">In optogenetic perturbation trials (~33% of trials), during each lever press, continuous wave stimulation of 473nm light (15-20mW) was delivered bilaterally over primary auditory cortex A1 (or secondary motor cortex, M2, as necessary using similar protocol - see Aim 2) to activate the terminals of ChR2 expressing neurons.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].unit\">unit: </span><span class=\"field-value\">watts</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".stimulus['optogenetic_series'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".stimulus['optogenetic_series'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].interval\">interval: </span><span class=\"field-value\">1</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".stimulus['optogenetic_series'].site\"><b>site</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".stimulus['optogenetic_series'].site.device\"><b>device</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].site.device.description\">description: </span><span class=\"field-value\">Real time optogenetic stimulation of brain regions of interest was accomplished via TTL control of an all solidstate 473nm blue laser (MBL-III-473/1~100mW, Opto Engine LLC). Bifurcated fiber cables (ThorLabs, Ø200 µm Core Multimode Fiber) were used for light delivery.</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].site.device.manufacturer\">manufacturer: </span><span class=\"field-value\">Opto Engine LLC</span></div></details><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].site.description\">description: </span><span class=\"field-value\">To identify cortical neurons that project from the auditory cortex to motor regions (Aim 2), stereotaxic injections of AAV-ChR2 were made into the primary auditory cortex (-2.8 AP, 4.2 ML relative to bregma; guided by intrinsic optical imaging) during head-fixation and animals were trained while expression occurs (~2 weeks). In addition, fiber optics were implanted to target cell bodies in the secondary motor cortex (1.0-1.5 AP, 0.5-0.7 ML).</span></div><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].site.excitation_lambda\">excitation_lambda: </span><span class=\"field-value\">473.0</span></div><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].site.location\">location: </span><span class=\"field-value\">Primary Auditory Cortex (-2.8 AP, 4.2 ML relative to bregma; guided by intrinsic optical imaging)</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".keywords\"><b>keywords</b></summary></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".processing\"><b>processing</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".processing['behavior']\"><b>behavior</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].description\">description: </span><span class=\"field-value\">C57BL/6 mice were first be water restricted, habituated to head fixation in the behavioral set up for two days and classically conditioned to associate a 16 kHz tone with a small water reward given 150 ms after the tone plays (~12 seconds inter-tone-interval). Mice were then be trained for 15 to 20 sessions on an auditory guided task described as follows. Inspired by human performance on stringed instruments, whereby a target note is achieved via modulation of forelimb and hand movements, we have engineered a novel behavioral paradigm that requires mice to skillfully adjust the size of lever presses in response to a dynamic virtual acoustic environment. Mice were trained to press a small 3-D printed lever forward with their forelimb toward a 2 mm wide target zone. Mice heard a 16 Hz tone when the lever enters the zone and a 10 kHz tone if the press exceeds the bounds of the zone. Presses that peak within the zone produce only the entry tone and are rewarded when the lever returns to the starting position. Presses that undershoot (producing no tones) or overshoot (producing both an entry and an exit tone) are unrewarded. Every 30 trials, which we refer to as a block, the target zone was relocated without warning and the mice must use acoustic feedback to adjust their lever presses to peak at the new location.</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces\"><b>data_interfaces</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series']\"><b>behavioral_time_series</b></summary><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series\"><b>time_series</b></summary><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder']\"><b>encoder</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].description\">description: </span><span class=\"field-value\">Sampled values for entire duration of experiment for lever pressing behavior read from a rotary encoder (US Digital). Digital signals for licking and lever movement were collected by a data acquisition card (National Instruments) connected to a computer and logged by custom Matlab software (Mathworks, PsychToolBox) and sampled at 2kHz.</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].unit\">unit: </span><span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].interval\">interval: </span><span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick']\"><b>lick</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].description\">description: </span><span class=\"field-value\">Samples values for entire duration of experiment for voltage signal readout from a custom infrared/capacitive lickometer sensor (Schneider Lab).  Digital signals for licking and lever movement were collected by a data acquisition card (National Instruments) connected to a computer and logged by custom Matlab software (Mathworks, PsychToolBox) and sampled at 2kHz.</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].unit\">unit: </span><span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].interval\">interval: </span><span class=\"field-value\">1</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['targetOUT']\"><b>targetOUT</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].description\">description: </span><span class=\"field-value\">Time at which the target zone is overshot during a press.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneIN']\"><b>toneIN</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].description\">description: </span><span class=\"field-value\">Time at which target zone is entered and target entry tone is played.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneOUT']\"><b>toneOUT</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].description\">description: </span><span class=\"field-value\">Time at which target exit tone is played (this is delayed 50ms relative to targetOUT so that entry and exit tones don't bleed into each other.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valve']\"><b>valve</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['valve'].description\">description: </span><span class=\"field-value\">Times at which solenoid valve opens to deliver water after a correct trial.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valve'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['valve'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging']\"><b>intrinsic_signal_optical_imaging</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['intrinsic_signal_optical_imaging'].description\">description: </span><span class=\"field-value\">For precise targeting of auditory cortex, intrinsic optical imaging (IOS) was performed using a 2-photon microscope (Neurolabware). The skull was first bilaterally thinned over a region of interest (ROI) and made translucent. On experiment day, 680nm red light (ThorLabs) is used to image the ROI. Data was collected via MATLAB running custom suites for online and offline analyses.</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces\"><b>data_interfaces</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images']\"><b>images</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].description\">description: </span><span class=\"field-value\">Intrinsic signal optical images.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images\"><b>images</b></summary><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images['overlaid_image']\"><b>overlaid_image</b></summary></details><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images['target_image']\"><b>target_image</b></summary></details></details></details></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".epoch_tags\"><b>epoch_tags</b></summary><span class=\"field-key\">set()</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".devices\"><b>devices</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser']\"><b>intrinsic_signal_optical_imaging_laser</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser'].description\">description: </span><span class=\"field-value\">ThorLabs 700nm fiber coupled LED (M700F3) driven by their basic LED driver (LEDD1B).</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser'].manufacturer\">manufacturer: </span><span class=\"field-value\">ThorLabs</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['lickometer']\"><b>lickometer</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['lickometer'].description\">description: </span><span class=\"field-value\">The lickometer comprised a custom-mounted (3D printed using Formlabs Form2) IR-beam emitter and receiver. IR signal was titrated and pre-processed using a custom printed circuit board (designed by Melissa Caras and Dan Sanes) to generate a binary TTL signal with IR sensitivity controlled by a potentiometer.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['lickometer'].manufacturer\">manufacturer: </span><span class=\"field-value\">Schneider Lab</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['optogenetic_stimulation_laser']\"><b>optogenetic_stimulation_laser</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['optogenetic_stimulation_laser'].description\">description: </span><span class=\"field-value\">Real time optogenetic stimulation of brain regions of interest was accomplished via TTL control of an all solidstate 473nm blue laser (MBL-III-473/1~100mW, Opto Engine LLC). Bifurcated fiber cables (ThorLabs, Ø200 µm Core Multimode Fiber) were used for light delivery.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['optogenetic_stimulation_laser'].manufacturer\">manufacturer: </span><span class=\"field-value\">Opto Engine LLC</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['rotary_encoder']\"><b>rotary_encoder</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['rotary_encoder'].description\">description: </span><span class=\"field-value\">H5 BALL BEARING OPTICAL SHAFT ENCODER</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['rotary_encoder'].manufacturer\">manufacturer: </span><span class=\"field-value\">US Digital</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['two_photon_microscope']\"><b>two_photon_microscope</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['two_photon_microscope'].description\">description: </span><span class=\"field-value\">Standard Microscope by Neurolabware.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['two_photon_microscope'].manufacturer\">manufacturer: </span><span class=\"field-value\">Neurolabware</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".ogen_sites\"><b>ogen_sites</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".ogen_sites['optogenetic_stimulus_site']\"><b>optogenetic_stimulus_site</b></summary><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].device\"><b>device</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].device.description\">description: </span><span class=\"field-value\">Real time optogenetic stimulation of brain regions of interest was accomplished via TTL control of an all solidstate 473nm blue laser (MBL-III-473/1~100mW, Opto Engine LLC). Bifurcated fiber cables (ThorLabs, Ø200 µm Core Multimode Fiber) were used for light delivery.</span></div><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].device.manufacturer\">manufacturer: </span><span class=\"field-value\">Opto Engine LLC</span></div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].description\">description: </span><span class=\"field-value\">To identify cortical neurons that project from the auditory cortex to motor regions (Aim 2), stereotaxic injections of AAV-ChR2 were made into the primary auditory cortex (-2.8 AP, 4.2 ML relative to bregma; guided by intrinsic optical imaging) during head-fixation and animals were trained while expression occurs (~2 weeks). In addition, fiber optics were implanted to target cell bodies in the secondary motor cortex (1.0-1.5 AP, 0.5-0.7 ML).</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].excitation_lambda\">excitation_lambda: </span><span class=\"field-value\">473.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].location\">location: </span><span class=\"field-value\">Primary Auditory Cortex (-2.8 AP, 4.2 ML relative to bregma; guided by intrinsic optical imaging)</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".intervals\"><b>intervals</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".intervals['epochs']\"><b>epochs</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".intervals['epochs']\">description: </span><span class=\"field-value\">experimental epochs</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".intervals['epochs']\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>tags</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>126.256702</td>\n",
+       "      <td>786.229029</td>\n",
+       "      <td>[Active Behavior]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".intervals['trials']\"><b>trials</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".intervals['trials']\">description: </span><span class=\"field-value\">experimental trials</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".intervals['trials']\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>rewarded</th>\n",
+       "      <th>time_reward_s</th>\n",
+       "      <th>opto_trial</th>\n",
+       "      <th>opto_time</th>\n",
+       "      <th>opto_time_end</th>\n",
+       "      <th>ITI_respect</th>\n",
+       "      <th>ThresholdVector</th>\n",
+       "      <th>endZone_ThresholdVector</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>126.256702</td>\n",
+       "      <td>126.368786</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>126.976659</td>\n",
+       "      <td>127.150010</td>\n",
+       "      <td>True</td>\n",
+       "      <td>142823.197278</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>128.341690</td>\n",
+       "      <td>128.464307</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>128.784895</td>\n",
+       "      <td>129.004811</td>\n",
+       "      <td>True</td>\n",
+       "      <td>142825.050398</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>... and 433 more rows.</p></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".subject\"><b>subject</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.age\">age: </span><span class=\"field-value\">P12W/</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.age__reference\">age__reference: </span><span class=\"field-value\">birth</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.description\">description: </span><span class=\"field-value\">12-week-old C57BL/6 or VGATChR2-EYFP mice were used for all behavioral, electrophysiology, and optogenetic experiments. In the VGAT-ChR2-EYFP mouse line, channelrhodopsin (ChR2) was coupled to the vesicular GABA transporter, inducing expression in GABAergic inhibitory neurons ubiquitously across cortex and allowing for real time optogenetic inhibition of brain regions of interest.</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.genotype\">genotype: </span><span class=\"field-value\">C57BL/6 or VGATChR2-EYFP</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.sex\">sex: </span><span class=\"field-value\">U</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.species\">species: </span><span class=\"field-value\">Mus musculus</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.subject_id\">subject_id: </span><span class=\"field-value\">m53</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.strain\">strain: </span><span class=\"field-value\">C57BL/6</span></div></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".epochs\"><b>epochs</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".epochs\">description: </span><span class=\"field-value\">experimental epochs</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".epochs\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>tags</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>126.256702</td>\n",
+       "      <td>786.229029</td>\n",
+       "      <td>[Active Behavior]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".trials\"><b>trials</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".trials\">description: </span><span class=\"field-value\">experimental trials</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".trials\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>rewarded</th>\n",
+       "      <th>time_reward_s</th>\n",
+       "      <th>opto_trial</th>\n",
+       "      <th>opto_time</th>\n",
+       "      <th>opto_time_end</th>\n",
+       "      <th>ITI_respect</th>\n",
+       "      <th>ThresholdVector</th>\n",
+       "      <th>endZone_ThresholdVector</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>126.256702</td>\n",
+       "      <td>126.368786</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>126.976659</td>\n",
+       "      <td>127.150010</td>\n",
+       "      <td>True</td>\n",
+       "      <td>142823.197278</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>128.341690</td>\n",
+       "      <td>128.464307</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>128.784895</td>\n",
+       "      <td>129.004811</td>\n",
+       "      <td>True</td>\n",
+       "      <td>142825.050398</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>... and 433 more rows.</p></details></details><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".experiment_description\">experiment_description: </span><span class=\"field-value\">Identifying mistakes is important for improving performance during acoustic behaviors like speech and musicianship. Although hearing is instrumental for monitoring and adapting these behaviors, the neural circuits that integrate motor, acoustic, and goal-related signals to detect errors and guide ongoing sensorimotor adaptation in mammals remain unidentified. Here, we develop a novel closed-loop, sound-guided behavior that requires mice to use real-time acoustic feedback to guide skilled ongoing forelimb movements. Large scale electrophysiology recordings reveal that the mouse auditory cortex integrates information about sound and movement, as well as encodes error- and learning-related signals during this sound-generating behavior. Distinct groups of auditory cortex neurons signal different error types, and the activity of these neurons predicts both within-trial and across-trial behavioral adaptations. Brief, behavior-triggered optogenetic suppression of auditory cortex during error signaling hinders behavioral corrections on both rapid and long time scales, indicating that cortical error signals are necessary for skilled acoustic behaviors. Together, these experiments identify a cortical role for detecting errors and learning from mistakes and suggest that the auditory cortex plays a critical role in skilled, sound-generating behavior in mammals.</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".session_id\">session_id: </span><span class=\"field-value\">231013</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".lab\">lab: </span><span class=\"field-value\">Schneider</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".institution\">institution: </span><span class=\"field-value\">New York University</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".source_script\">source_script: </span><span class=\"field-value\">Created using NeuroConv v0.6.5</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".source_script_file_name\">source_script_file_name: </span><span class=\"field-value\">/opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/neuroconv/basedatainterface.py</span></div></div>"
+      ],
+      "text/plain": [
+       "root pynwb.file.NWBFile at 0x4858229584\n",
+       "Fields:\n",
+       "  acquisition: {\n",
+       "    video_camera_1 <class 'pynwb.image.ImageSeries'>,\n",
+       "    video_camera_2 <class 'pynwb.image.ImageSeries'>\n",
+       "  }\n",
+       "  devices: {\n",
+       "    intrinsic_signal_optical_imaging_laser <class 'pynwb.device.Device'>,\n",
+       "    lickometer <class 'pynwb.device.Device'>,\n",
+       "    optogenetic_stimulation_laser <class 'pynwb.device.Device'>,\n",
+       "    rotary_encoder <class 'pynwb.device.Device'>,\n",
+       "    two_photon_microscope <class 'pynwb.device.Device'>\n",
+       "  }\n",
+       "  epochs: epochs <class 'pynwb.epoch.TimeIntervals'>\n",
+       "  experiment_description: Identifying mistakes is important for improving performance during acoustic behaviors like speech and musicianship. Although hearing is instrumental for monitoring and adapting these behaviors, the neural circuits that integrate motor, acoustic, and goal-related signals to detect errors and guide ongoing sensorimotor adaptation in mammals remain unidentified. Here, we develop a novel closed-loop, sound-guided behavior that requires mice to use real-time acoustic feedback to guide skilled ongoing forelimb movements. Large scale electrophysiology recordings reveal that the mouse auditory cortex integrates information about sound and movement, as well as encodes error- and learning-related signals during this sound-generating behavior. Distinct groups of auditory cortex neurons signal different error types, and the activity of these neurons predicts both within-trial and across-trial behavioral adaptations. Brief, behavior-triggered optogenetic suppression of auditory cortex during error signaling hinders behavioral corrections on both rapid and long time scales, indicating that cortical error signals are necessary for skilled acoustic behaviors. Together, these experiments identify a cortical role for detecting errors and learning from mistakes and suggest that the auditory cortex plays a critical role in skilled, sound-generating behavior in mammals.\n",
+       "  experimenter: ['Zempolich, Grant W.' 'Schneider, David M.']\n",
+       "  file_create_date: [datetime.datetime(2024, 12, 18, 9, 7, 21, 778849, tzinfo=tzoffset(None, -28800))]\n",
+       "  identifier: b6a37a08-f2ab-45af-b49b-225aa17c8012\n",
+       "  institution: New York University\n",
+       "  intervals: {\n",
+       "    epochs <class 'pynwb.epoch.TimeIntervals'>,\n",
+       "    trials <class 'pynwb.epoch.TimeIntervals'>\n",
+       "  }\n",
+       "  keywords: <StrDataset for HDF5 dataset \"keywords\": shape (3,), type \"|O\">\n",
+       "  lab: Schneider\n",
+       "  ogen_sites: {\n",
+       "    optogenetic_stimulus_site <class 'pynwb.ogen.OptogeneticStimulusSite'>\n",
+       "  }\n",
+       "  processing: {\n",
+       "    behavior <class 'pynwb.base.ProcessingModule'>,\n",
+       "    intrinsic_signal_optical_imaging <class 'pynwb.base.ProcessingModule'>\n",
+       "  }\n",
+       "  session_description: Mice performed the auditory guided task while optogenetic stimulation was delivered to the primary auditory cortex (A1).\n",
+       "  session_id: 231013\n",
+       "  session_start_time: 2023-10-13 00:00:00-04:00\n",
+       "  source_script: Created using NeuroConv v0.6.5\n",
+       "  source_script_file_name: /opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/neuroconv/basedatainterface.py\n",
+       "  stimulus: {\n",
+       "    optogenetic_series <class 'pynwb.ogen.OptogeneticSeries'>\n",
+       "  }\n",
+       "  subject: subject pynwb.file.Subject at 0x5028608960\n",
+       "Fields:\n",
+       "  age: P12W/\n",
+       "  age__reference: birth\n",
+       "  description: 12-week-old C57BL/6 or VGATChR2-EYFP mice were used for all behavioral, electrophysiology, and optogenetic experiments. In the VGAT-ChR2-EYFP mouse line, channelrhodopsin (ChR2) was coupled to the vesicular GABA transporter, inducing expression in GABAergic inhibitory neurons ubiquitously across cortex and allowing for real time optogenetic inhibition of brain regions of interest.\n",
+       "  genotype: C57BL/6 or VGATChR2-EYFP\n",
+       "  sex: U\n",
+       "  species: Mus musculus\n",
+       "  strain: C57BL/6\n",
+       "  subject_id: m53\n",
+       "\n",
+       "  timestamps_reference_time: 2023-10-13 00:00:00-04:00\n",
+       "  trials: trials <class 'pynwb.epoch.TimeIntervals'>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nwbfile_path = '/Volumes/T7/CatalystNeuro/Schneider/conversion_nwb/sub-m53_ses-231013.nwb'\n",
+    "io = NWBHDF5IO(nwbfile_path, 'r')\n",
+    "nwbfile = io.read()\n",
+    "display(nwbfile)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Retrieve Opto and Behavioral data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Opto\n",
+    "opto_stim_timestamps = np.asarray(nwbfile.stimulus[\"optogenetic_series\"].timestamps)\n",
+    "opto_stim_data = np.asarray(nwbfile.stimulus[\"optogenetic_series\"].data)\n",
+    "stim_power = 0.020\n",
+    "opto_onset_times = opto_stim_timestamps[opto_stim_data == stim_power]\n",
+    "opto_offset_times = opto_stim_timestamps[opto_stim_data == 0]\n",
+    "\n",
+    "# Behavioral Events\n",
+    "tone_in = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"toneIN\"].timestamps)\n",
+    "tone_out = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"toneOUT\"].timestamps)\n",
+    "target_out = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"targetOUT\"].timestamps)\n",
+    "valve = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"valve\"].timestamps)\n",
+    "\n",
+    "# Behavioral Time Series\n",
+    "encoder_data = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"behavioral_time_series\"][\"encoder\"].data)\n",
+    "encoder_timestamps = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"behavioral_time_series\"][\"encoder\"].timestamps)\n",
+    "lick_data = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"behavioral_time_series\"][\"lick\"].data)\n",
+    "lick_timestamps = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"behavioral_time_series\"][\"lick\"].timestamps)\n",
+    "\n",
+    "# Behavioral Trials\n",
+    "trials = nwbfile.trials.to_dataframe()\n",
+    "trials = trials[trials.opto_trial]\n",
+    "example_rewarded_trial = trials[trials[\"rewarded\"] == True].iloc[0]\n",
+    "example_unrewarded_trial = trials[trials[\"rewarded\"] == False].iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "example_trial = example_rewarded_trial\n",
+    "\n",
+    "# Define plot parameters\n",
+    "lineoffsets = 1\n",
+    "linelengths = 2\n",
+    "ylim = [-2, 2]\n",
+    "grace_time = 0.100\n",
+    "y = np.arange(-1, 1, 0.1)\n",
+    "alpha = 0.3\n",
+    "\n",
+    "# Define time windows for plotting\n",
+    "plot_start_time = example_trial[\"start_time\"] - grace_time\n",
+    "plot_end_time = example_trial[\"stop_time\"] + grace_time\n",
+    "opto_onset_mask = (opto_onset_times >= plot_start_time) & (opto_onset_times <= plot_end_time)\n",
+    "opto_offset_mask = (opto_offset_times >= plot_start_time) & (opto_offset_times <= plot_end_time)\n",
+    "tone_in_mask = (tone_in >= plot_start_time) & (tone_in <= plot_end_time)\n",
+    "tone_out_mask = (tone_out >= plot_start_time) & (tone_out <= plot_end_time)\n",
+    "target_out_mask = (target_out >= plot_start_time) & (target_out <= plot_end_time)\n",
+    "valve_mask = (valve >= plot_start_time) & (valve <= plot_end_time)\n",
+    "encoder_mask = (encoder_timestamps >= plot_start_time) & (encoder_timestamps <= plot_end_time)\n",
+    "lick_mask = (lick_timestamps >= plot_start_time) & (lick_timestamps <= plot_end_time)\n",
+    "\n",
+    "# Normalize times to trial start time\n",
+    "trial_start_time = example_trial[\"start_time\"]\n",
+    "normalized_opto_onset_times = opto_onset_times[opto_onset_mask] - trial_start_time\n",
+    "normalized_opto_offset_times = opto_offset_times[opto_offset_mask] - trial_start_time\n",
+    "normalized_tone_in = tone_in[tone_in_mask] - trial_start_time\n",
+    "normalized_tone_out = tone_out[tone_out_mask] - trial_start_time\n",
+    "normalized_target_out = target_out[target_out_mask] - trial_start_time\n",
+    "normalized_valve = valve[valve_mask] - trial_start_time\n",
+    "normalized_encoder_timestamps = encoder_timestamps[encoder_mask] - trial_start_time\n",
+    "normalized_lick_timestamps = lick_timestamps[lick_mask] - trial_start_time\n",
+    "\n",
+    "fig, axs = plt.subplots(4, 1, figsize=(20, 10), sharex=True)\n",
+    "axs[0].set_title(\"Example Rewarded Trial\")\n",
+    "axs[0].eventplot(normalized_tone_in, color=\"red\", label=\"Tone In\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_tone_out, color=\"blue\", label=\"Tone Out\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_target_out, color=\"green\", label=\"Target Out\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_valve, color=\"purple\", label=\"Valve\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].axvline(0, color=\"black\", linestyle=\"--\", label=\"Start Time\")\n",
+    "axs[0].axvline(example_trial[\"stop_time\"] - example_trial[\"start_time\"], color=\"black\", linestyle=\"--\", label=\"Stop Time\")\n",
+    "for i, (onset_time, offset_time) in enumerate(zip(normalized_opto_onset_times, normalized_opto_offset_times)):\n",
+    "    x1 = onset_time * np.ones(len(y))\n",
+    "    x2 = offset_time * np.ones(len(y))\n",
+    "    if i == 0:\n",
+    "        axs[0].fill_betweenx(y, x1, x2, color=\"blue\", alpha=alpha, label=\"Optogenetic Stimulation\")\n",
+    "    else:\n",
+    "        axs[0].fill_betweenx(y, x1, x2, color=\"blue\", alpha=alpha)\n",
+    "\n",
+    "axs[0].set_ylim(ylim)\n",
+    "axs[0].set_ylabel(\"Channel 1 Raw Voltage (uV)\")\n",
+    "axs[0].legend()\n",
+    "\n",
+    "axs[2].plot(normalized_encoder_timestamps, encoder_data[encoder_mask], color=\"k\", label=\"Encoder\")\n",
+    "axs[2].axhline(example_trial[\"ThresholdVector\"], color=\"black\", linestyle=\"--\", label=\"Threshold Vector\")\n",
+    "axs[2].axhline(example_trial[\"endZone_ThresholdVector\"], color=\"red\", linestyle=\"--\", label=\"End Zone Threshold Vector\")\n",
+    "axs[2].set_ylabel(\"Rotary Encoder (a.u.)\")\n",
+    "axs[2].legend()\n",
+    "\n",
+    "axs[3].plot(normalized_lick_timestamps, lick_data[lick_mask], color=\"k\", label=\"Lick\")\n",
+    "axs[3].set_ylabel(\"Lick Sensor (a.u.)\")\n",
+    "_ = axs[3].set_xlabel(\"Time Since Trial Start (s)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.0063527])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "normalized_opto_onset_times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.0845106])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "normalized_opto_offset_times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "start_time                    198.942435\n",
+       "stop_time                     200.023562\n",
+       "rewarded                            True\n",
+       "time_reward_s              142896.074106\n",
+       "opto_trial                          True\n",
+       "opto_time                  142894.990439\n",
+       "opto_time_end              142896.068597\n",
+       "ITI_respect                         True\n",
+       "ThresholdVector                     27.0\n",
+       "endZone_ThresholdVector             33.0\n",
+       "Name: 60, dtype: object"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_trial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "schneider_lab_to_nwb_env",
+   "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/001259/stream_nwbfile.py b/001259/stream_nwbfile.py
new file mode 100644
index 0000000..5208c0d
--- /dev/null
+++ b/001259/stream_nwbfile.py
@@ -0,0 +1,36 @@
+from pynwb import NWBHDF5IO
+from fsspec import filesystem
+from h5py import File
+from dandi.dandiapi import DandiAPIClient
+
+def stream_nwbfile(DANDISET_ID, file_path):
+    '''Stream NWB file from DANDI archive.
+    
+    Parameters
+    ----------
+    DANDISET_ID : str
+        Dandiset ID
+    file_path : str
+        Path to NWB file in DANDI archive
+    
+    Returns
+    -------
+    nwbfile : NWBFile
+        NWB file
+    io : NWBHDF5IO
+        NWB IO object (for closing)
+        
+    Notes
+    -----
+    The io object must be closed after use.
+    '''
+    with DandiAPIClient() as client:
+        client.dandi_authenticate()
+        asset = client.get_dandiset(DANDISET_ID, 'draft').get_asset_by_path(file_path)
+        s3_url = asset.get_content_url(follow_redirects=1, strip_query=True)
+    fs = filesystem("http")
+    file_system = fs.open(s3_url, "rb")
+    file = File(file_system, mode="r")
+    io = NWBHDF5IO(file=file, load_namespaces=True)
+    nwbfile = io.read()
+    return nwbfile, io
\ No newline at end of file