Link Search Menu Expand Document

Run scripts from the processing module separately

The individual analysis steps of the processing module can easily be used separately. Below follows a few examples. All of them requires the session struct and/or the spikes struct. Most functions have a number of optional arguments allowing for customization.

Table of contents

  1. Create session struct
  2. Load spikes
  3. Extract spike waveforms from dat/binary file
  4. Calculate waveform metrics
  5. ACG and CCG metrics
  6. fit ACGs
  7. Calculate log ACGs
  8. Calculate log ISIs
  9. Monosynaptic connections
  10. Calculate PSTHs for events
  11. Deep-superficial classification of hippocampal recordings
  12. Calculate CCGs across all cells

Create session struct

  1. Define the basepath of the dataset to run. An optional Neuroscope compatible basename.xml can be used to define the electrode layout.
    basepath = '/your/data/path/basename/';
    cd(basepath)
    
  2. Generate session metadata struct using the template function and display the metadata in a GUI
    session = sessionTemplate(basepath,'showGUI',true);
    

Load spikes

Loading and creating the spikes struct is done using the script loadSpikes. If the spikes struct has not been created yet, the script will generate it and save it to the basepath. If it already exist in the basepath it will load it into Matlab:

spikes = loadSpikes('session',session);

Extract spike waveforms from dat/binary file

spikes = getWaveformsFromDat(spikes,session);

Calculate waveform metrics

sr = session.extracellular.sr;
waveform_metrics = calc_waveform_metrics(spikes,sr);

ACG and CCG metrics

acg_metrics = calc_ACG_metrics(spikes);

fit ACGs

From the calculated ACGs (previous section) you can calculate the ACG fits:

fit_params = fit_ACG(acg_metrics.acg_narrow);

Calculate log ACGs

The log ACGs are calculated with log-scaled bins from 1ms to 10sec:

acg = calc_logACGs(spikes.times)

Calculate log ISIs

The log ISIs are calculated with log-scaled bins from 1ms to 100sec:

isi = calc_logISIs(spikes.times);

Monosynaptic connections

ce_MonoSynConvClick.m is called to detect monosynaptic connections. Once complete, the connections can be manually curated using gui_MonoSyn.m.

mono_res = ce_MonoSynConvClick(spikes);
mono_res = gui_MonoSyn(mono_res);
saveStruct(mono_res,'cellinfo','session',session)

Calculate PSTHs for events

By providing an event struct, interval PSTHs can be created:

PSTH = calc_PSTH(event,spikes);

You can provide optional parameters defining the alignment and the windows size.

Deep-superficial classification of hippocampal recordings

This metric has it own tutorial dedicated to it here, but briefly:

  1. Detect ripples
    ripples = ce_FindRipples(session);
    
  2. Determine the depth
    deepSuperficialfromRipple = classification_DeepSuperficial(session);
    
  3. This will generate a deep-superficial classification file: sessionName.deepSuperficialfromRipple.channelinfo.mat. You can curate it using gui_DeepSuperficial:
    gui_DeepSuperficial(deepSuperficialfromRipple;
    

Calculate CCGs across all cells

First calculate the spindices (the input format of the CCG function), if they are missing in the spikes struct:

spikes_restrict.spindices = generateSpinDices(spikes_restrict.times);

Next calculate the CCGs between all pairs of cells

binSize = 0.001; % 1ms bin size
duration = 0.1; % -50ms:50ms window
[ccg,t] = CCG(spikes.spindices(:,1),spikes.spindices(:,2),'binSize',binSize,'duration',duration);

Finally, to plot the ACGs and CCGs write

figure, 
% Plotting the autocorrelogram (ACG) of the eight cell
subplot(2,1,1)
plot(t,ccg(:,8,8)), title('ASCG'), xlabel('Time (seconds)'), ylabel('Count')

% Plotting the cross correlogram (CCG) between a pair of cells
subplot(2,1,2)
plot(t,ccg(:,1,3)), title('CCG'), xlabel('Time (seconds)'), ylabel('Count')

The CCG example is also part of a CCG tutorial script.