Public | Automated Build

Last pushed: a month ago
Short Description
Automate build onto computers when updated
Full Description

Glia

A package to support neuroscientists in analyzing MEAs.

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!

  2. 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)

  3. spike sorting

    1. 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

    2. 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

    3. 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

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tbenst
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