1. Chronux Tutorial: Part II LOCFIT
Keith Purpura
Weill Cornell Medical College
Bijan Pesaran
Center for Neural Science, NYU
Hemant Bokil
Boston Scientific Corporation

2. Spectra, Coherences etc
Fourier transforms using multiple tapers: mtfftc.m
Spectrum: mtspectrumc.m
Coherency:mtcoherencyc.m
Spectrogram: mtspecgramc.m
Coherogram:mtcohgramc.m
Local regression and likelihood
The basic routines are listed here for continuous processes. Corresponding routines for spike times have names ending in pt, and corresponding routines for binned spike counts have names ending in pb. Note that almost all Chronux routines come with error bars.
Regression and likelihood: locfit.m
Plotting the fit: lfplot.m
Plotting local confidence bands: lfband.m
Plotting global confidence bands: scb.m

3. Chronux data format Continuous/binned point process data
matrices with dimension time x channels/trials
e.g 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=[ ]
data(2).times=[ ]
2 spike trains with 4 and 3 spikes
Chronux’s standard data format is listed here. We will also expand the Chronux internal format shortly to include cell arrays. We will incorporate other ways of storing data by including filters to convert data from other formats to the Chronux format – filters to convert from the plexon format to the Chronux format will be released soon.

4. 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

5. Example II: Spike rates, spectra and coherence (from earlier lecture)
Simultaneous two-cell recording from Macaque area LIP – dataset DynNeuroLIP.mat
Reach and
Saccade
Cue
Delay
Cue
Delay
Reach and Saccade Task
Slide from the morning’s lectures.
Pesaran et al (2008)

6. 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)
Description of data

7. Tasks Compute 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.

8. The main script for this tutorial lip_master_script2.m
Calls other scripts to run through the various analyses
Type lip_master_script at the Matlab command prompt and press return

14. Basic locfit usage (rate estimate)
Spike rate: 1 trial
Basic locfit usage (rate estimate)
>> fit=locfit(data,'family','rate');
>> lfplot(fit);
>> lfband(fit);
Density estimate:
replace
'rate‘ by ‘dens’
Regression
>> fit=locfit(x,y);

17. Setting the bandwidth –fixed (h), nearest neighbor (nn)
h: fixed/absolute
bandwidth e.g. h=1
is 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
>> fit=locfit(data,'family','rate‘,’nn’,0.3);
>> lfplot(fit);
>> lfband(fit);

19. Multiple trials pool the spikes and compute fit
rescale fits and confidence intervals
Pause at this place

21. Electrophysiology Analysis Protocol

22. Electrophysiolgy: Data Conditioning