https://github.com/tbenst/glia
A package to support neuroscientists in analyzing MEAs.
https://github.com/tbenst/glia
electrophysiology neuroscience
Last synced: about 1 month ago
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A package to support neuroscientists in analyzing MEAs.
- Host: GitHub
- URL: https://github.com/tbenst/glia
- Owner: tbenst
- Created: 2015-10-16T02:54:54.000Z (about 10 years ago)
- Default Branch: master
- Last Pushed: 2024-04-10T01:35:34.000Z (over 1 year ago)
- Last Synced: 2025-04-01T14:38:02.703Z (7 months ago)
- Topics: electrophysiology, neuroscience
- Language: Jupyter Notebook
- Homepage:
- Size: 61.5 MB
- Stars: 2
- Watchers: 5
- Forks: 3
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
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README
# Glia
[](https://travis-ci.org/tbenst/glia)
A package to support neuroscientists in analyzing multi-electrode array recordings. Designed with the retina in mind.
Some fun visualizations:
[retina2pixel saliency map](https://tbenst.github.io/glia/ret2pix.html) of the impact of each pixel on a given MEA channel.
[pixel2retina "forward" saliency map](https://tbenst.github.io/glia/pix2ret.html) of the impact of each MEA channel on a pixel selected by the mouse.
## Installation
```
> pip install git+https://github.com/tbenst/glia.git
```
We install in development mode so can update with a simple `git pull`
## Documentation
Access the glia command line tool documentation with "glia -h." All sub commands also have documentation, eg "glia analyze -h".
### Typical command order:
```
# create .stim file & make plots for "integrity" check
> glia analyze R1_E1_AMES_120min_celltyping integrity
# create .frames file
> glia process R1_E1_AMES_120min_celltyping
# plot receptive fields
> glia analyze R1_E1_AMES_120min_celltyping sta
# create .npz/.h5 file for machine learning
> glia analyze R1_E1_AMES_120min_celltyping convert
# run classification & plot results
> glia classify R1_E1_AMES_120min_celltyping
```
## For Docker 1.17 (or later):
`sudo docker run --network=eyecandy_default -v $(pwd):/data tbenst/glia analyze -e "http://eyecandy:3000" /data/R2_E1 convert`
## How to analyze data:
1. Convert *.mcd files into *.voltages
1. Pull files from MEA computer onto local machine
2. Open docker and go to folder with data
e.g. `docker run -v /c/Users/sandt/Desktop/160913:/data tbenst/mcd`
-> this will automatically start the conversion process in the folder
Wait until all files are converted, i.e. the terminal says: process finished!
3. Find out header length
Open new terminal:
`chdir /Documents/Github/`
run:
`docker run --rm -v /c/Users/Administrator/OneDrive/jupyter-notebooks:/notebooks --link eyecandy_web_1:eyecandy -p 8888:8888 tbenst/jupyter-neuro`
go to Chrome and type: `localhost:8888`
go to “get header offset”
type in folder with *.voltages file
run script (last line will spit out header length)
2. spike sorting
4. Load data for spike sorting
Open Plexon:
File -> import data -> import binary file
Open file location
60 channels
Sampling frequency (usually 25000, information can also be found in header)
Header length (see point 3)
Press ok
5. Filter data and detect spikes
Open “waveforms”
Filter continuous data
Butterworth 4th order, 330 Hz
For all channels
Detect spikes:
Open “waveforms” -> detect spikes
Threshold -3.8 -> for all channels
6. Spike sorting
Open “sort”
Perform automatic sorting
`E-M: 15`
when finished: visually check the sorted data and `invalidate` noise spikes
save waveformes as: *.txt file; all units in one file. delimiter `,`
select: channel (raw), unit, timestamp
## Dev notes
sudo docker run -it -v $(pwd):/data tbenst/glia:acuity analyze -v -p 4 -e http://localhost:3000 /data/R1_E1_AMES_50min_acuity.txt integrity solid --wedge bar --by acuity acuity