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StimScan utility docs (#14)
* First FAQ version * Further updates * Further FAQ updates * Update FAQ + more images * TODOs update * Small formatting * Initial docs for StimScan * Update signal-example.png * Formatting * Update faq.ipynb
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‎neuroplatform-docs/_static/images/faq/signal-example.png‎

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‎neuroplatform-docs/_toc.yml‎

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chapters:
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- file : np_utils/livemea
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- file : np_utils/stimparamloader
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- file : np_utils/stimscan
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- file : np_utils/spikesorting
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- file : np_utils/crosscorr

‎neuroplatform-docs/np_core/closed_loop.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"token = \"E9JTQDS62Y\" # Experiment token\n",
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"token = \"...\" # Experiment token\n",
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"exp = Experiment(token)\n",
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"print(f\"Electrodes: {exp.electrodes}\") # Electrodes that can be used with the token"
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]

‎neuroplatform-docs/np_core/faq.ipynb‎

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"- On the shared access, if you suspect there may be leftover triggers from a previous experiment, please contact us.\n",
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"- On a dedicated access, please remove any other triggers that may be active.\n",
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"\n",
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"#### How can I find the best parameters for stimulation ? How should I stimulate to get maximal responses from the organoid?\n",
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"\n",
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"Our [StimScan utility](np-utils:stimscan) may be useful to find the best parameters for your experiment.\n",
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"\n",
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"(faq:stim-artifacts)=\n",
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"#### How long should I wait after a stimulation to start taking events into account and avoid artifacts?\n",
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"\n",

‎neuroplatform-docs/np_utils/stimscan.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"(np-utils:stimscan)=\n",
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"# Stimulation parameter scan\n",
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"\n",
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"This utility allows you to automatically search for optimal stimulation parameters on a given MEA.\n",
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"\n",
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"```{note}\n",
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"Neuroplatform access (and booking for shared access) is required to use this utility.\n",
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"```\n",
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"\n",
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"\n",
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"## Installation\n",
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"\n",
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"To install all dependencies, run the following command:\n",
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"\n",
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"```bash\n",
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"pip install git+https://github.com/FinalSpark-np/np-utils.git#egg=np_utils[SSN]\n",
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"```\n",
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"\n",
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"## Usage\n",
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"\n",
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"### Imports\n",
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"\n",
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"Import the needed classes :\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from neuroplatform import Experiment, StimPolarity\n",
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"from np_utils.stim_scan import StimParamGrid, StimScan, MEAType"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Defining a StimParamGrid"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"grid = StimParamGrid(\n",
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" amplitudes=[1, 2, 3],\n",
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" durations=[100, 200],\n",
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" polarities=[StimPolarity.NegativeFirst, StimPolarity.PositiveFirst],\n",
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" mea_type=MEAType.MEA4x8, # OR MEAType.MEA32\n",
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")\n",
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"print(grid.total_combinations())\n",
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"grid.display_grid()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Running the scan\n",
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"````{margin}\n",
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"```{seealso}\n",
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"See [Experiment and token](core:exp-and-tokens) for relevant API usage.\n",
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"```\n",
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"````\t\n",
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"\n",
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"Next, define which channels to run the scan on (in absolute index, e.g. for MEA2, channels 32-64)\n",
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"Provide your experiment, the grid of parameters, and the delays as well as number of repeats per channel."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"EXPERIMENT = Experiment(\"...\") # use your token here\n",
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"scan = StimScan(\n",
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" fs_experiment=EXPERIMENT,\n",
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" parameter_grid=grid,\n",
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" delay_btw_stim=1, # delay between stimulations\n",
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" delay_btw_channels=5, # delay between channels\n",
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" repeats_per_channel=5, # number of repeats per channel for a single parameter set\n",
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" scan_channels=range(\n",
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" 32, 64\n",
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" ), # channels to scan, here MEA2. Must match electrodes allowed by your token\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"scan.run()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Displaying results\n",
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"\n",
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"#### Showing all stimulations\n",
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"\n",
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"To access the list of all stimulations and associated parameters, use :"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stim_df = scan.get_stimulation_parameter_history()\n",
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"stim_df"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Finally, to show all events after stimulation :"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"scan.plot_all_stims(\n",
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" s_before=1, s_after=2.5\n",
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") # arguments define how long to show before the first stim and after the last respectively"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You may also use `scan.plot_all_stims_for_param` and `scan.plot_all_stims_for_channel` to visualize the stimulation events specifically for a given parameter or channel.\n",
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"\n",
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"#### Additional plotting options\n",
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"\n",
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"##### Selecting specific channels\n",
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"\n",
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"The `show_electrodes` argument lets you pick which channels to display. By default, all channels are displayed.\n",
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"\n",
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"##### Event interval visualization\n",
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"\n",
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"Finally, you can use the `guideline_freq`argument to show lines every `guideline_freq` seconds. This can be useful to visualize the stimulation events in relation to the stimulation frequency.\n",
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"\n",
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"To do this, define the `guideline_freq` argument as the desired period in seconds as such : \"{n_sec}s\" (e.g. \"1s\" for 1 second).\n",
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"\n",
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"This is None by default, which will not display any guidelines.\n",
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"\n",
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"## Future additions\n",
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"\n",
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"Summary metrics as well as best parameter automated selection may be added in a future version.\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "fspark",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.11.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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