Chronux Tutorial: Part IILOCFIT

Keith PurpuraWeill Cornell Medical College

Bijan PesaranCenter for Neural Science, NYU

Hemant BokilBoston Scientific Corporation

Spectra, Coherences etc

Local regression and likelihood

Fourier transforms using multiple tapers: mtfftc.m

Spectrum: mtspectrumc.m

Spectrogram: mtspecgramc.mCoherogram:mtcohgramc.m

Coherency:mtcoherencyc.m

Regression and likelihood: locfit.m

Plotting the fit: lfplot.mPlotting local confidence bands: lfband.m

Plotting global confidence bands: scb.m

Chronux data format Continuous/binned point process data

matrices with dimension time x channels/trials

e.g. 1000 x 10 dimensional matrix

interpreted as 1000 samples

10 channels/trials

Spikes times

struct array with dimension = number of channels/trials

e.g. data(1).times=[0.3 0.35 0.42 0.6]

data(2).times=[0.2 0.22 0.35]

2 spike trains with 4 and 3 spikes

Important parameter in mulitple Chronux functions

params: structure with multiple fields

Fs: sampling frequency (slightly different interpretation for spike times

tapers: controls the number of tapers

pad: controls the padding

fpass: frequency range of interest

err: controls error computation

trialave: controls whether or not to average over trials

Example II: Spike rates, spectra and coherence (from earlier lecture)

• Simultaneous two-cell recording from Macaque area LIP – dataset DynNeuroLIP.mat

Reach and

Saccade Task

DelayCueReach andSaccade

Pesaran et al (2008)

DelayCue

Example II

3 local field potentials (LFP) and 2 single units, LFP sampled at 1 kHz

Trial: 3 seconds of data for 9 trials to one of the directions: 1 s (Baseline), 2 s (Delay + post movement)

Baseline: 1 second of data for 74 trials (pooled across all directions)

TasksCompute the following for the Memory trials

Spike rates

LFP and spike spectra

Spike-field coherence

Spike-Spike coherence

Compare spike-spike coherence during the memory period and the baseline period.

The main script for this tutorial

lip_master_script2.m

Calls other scripts to run through the various analyses

>> fit=locfit(data,'family','rate');>> lfplot(fit); >> lfband(fit);

Spike rate: 1 trial

Basic locfit usage (rate estimate)

Regression

>> fit=locfit(x,y);

Density estimate:replace 'rate‘ by ‘dens’

>> fit=locfit(data,'family','rate‘,’nn’,0.3);>> lfplot(fit); >> lfband(fit);

Setting the bandwidth –fixed (h), nearest neighbor (nn)

h: fixed/absolutebandwidth e.g. h=1is interpreted as 1 s if data is in seconds

nn: fixed fraction of the total number of points e.g. nn=0.3 takes the 30% closest points to a given point

Default: nn=0.7, h=0

Multiple trials

pool the spikes and compute fitrescale fits and confidence intervals

Electrophysiology Analysis Protocol

Electrophysiolgy: Data Conditioning