===== Tips =====
If you have short tips on how to make use of the [[http://neuroanalysis.org/|STAToolkit]], please add them here. If your contribution is very lengthy, consider putting together a separate tutorial page (e.g., [[usage:tutorials:mytutorial]]), and making a reference to it on this page.
==== Utilities for summarizing a spike train data structure ====
Sometimes it is useful to provide a "snapshot" of a data structure X, i.e., a brief summary of how many sites (channels) and categories (conditions), how many spikes, their times, etc.
I wrote two utilities, one that summarizes the number of spikes, one that summarizes their times; both are provided (without warranty!) below.
The following Matlab code provides a snapshot of the sites (channels), categories (conditions), and number of spikes:
function counts=statin_summ(X,ifshow)
% counts=statin_summ(X,ifshow) summarizes a STAToolkit input
%
% counts{category}(trial,site)=tally of counts
% ifshow=1 to show category-by-category summary (default)
% ifshow=2 to show a min-max summary
% ifshow=3 for both
%
% See also: SWATCH2STATIN, TLS2SWATCH, STATIN_SUMMT.
%
if (nargin<=1) ifshow=1; end
%
if (ifshow>0)
disp(sprintf('number of sites: %3.0f',X.N));
disp(sprintf('number of categories: %3.0f',X.M));
disp(' categ trials counts on each channel')
dstring=repmat('%7.0f ',1,X.N+1);
end
counts=cell(0);
trials=zeros(1,X.M);
for m=1:X.M
trials(m)=size(X.categories(m,1).trials,1);
counts_each=zeros(trials(m),X.N);
for n=1:X.N
for p=1:trials(m)
counts_each(p,n)=length(X.categories(m,1).trials(p,n).list);
end
end
counts{m}=counts_each;
counts_site(m,:)=sum(counts_each,1);
if (mod(ifshow,2)==1)
disp(sprintf(cat(2,'%7.0f ',dstring),m,trials(m),counts_site(m,:)));
end
end
if (mod(ifshow,2)==1)
disp(sprintf(cat(2,' all ',dstring),sum(trials),sum(counts_site)));
end
if (ifshow>=2)
disp(sprintf(cat(2,' min ',dstring),min(trials),min(counts_site)));
disp(sprintf(cat(2,' max ',dstring),max(trials),max(counts_site)));
disp(sprintf(cat(2,' med ',dstring),median(trials),median(counts_site)));
end
return
The following Matlab code provides a snapshot of the sites (channels), categories (conditions), and times of spikes:
function times=statin_summt(X,ifshow)
% times=statin_summt(X,ifshow) summarizes the event times in a STAToolkit input
%
% times{category}(firstlast,site)=first and last event times (NaN if none)
% ifshow=1 to show category-by-category summary (default)
% ifshow=2 to show a min-max summary
% ifshow=3 for both
%
% See also: SWATCH2STATIN, TLS2SWATCH, STATIN_SUMM.
%
if (nargin<=1) ifshow=1; end
%
if (ifshow>0)
disp(sprintf('number of sites: %3.0f',X.N));
disp(sprintf('number of categories: %3.0f',X.M));
disp(' categ trials [min max] times on each channel')
dstring=repmat('[%8.3f %8.3f] ',1,X.N);
end
trials=zeros(1,X.M);
times=cell(0);
%make times_sites for each m
times_sites=repmat([Inf -Inf]',[1 X.N X.M]);
for m=1:X.M
trials(m)=size(X.categories(m,1).trials,1);
for n=1:X.N
for p=1:trials(m)
tvals=X.categories(m,1).trials(p,n).list;
if length(tvals)>0
times_sites(1,n,m)=min(times_sites(1,n,m),min(tvals));
times_sites(2,n,m)=max(times_sites(2,n,m),max(tvals));
end
end
end
times{m}=times_sites(:,:,m);
if (mod(ifshow,2)==1)
disp(sprintf(cat(2,'%7.0f %7.0f ',dstring),m,trials(m),times_sites(:,:,m)));
end
end
if (ifshow>=2)
disp(sprintf(cat(2,' all %7.0f ',dstring),sum(trials),...
[min(times_sites(1,:,:),[],3);max(times_sites(2,:,:),[],3)]));
end
return
--- //[[jdvicto@med.cornell.edu|Jonathan D. Victor]] 2010/02/24 04:54//
==== Exchange Resampling ====
User Susan Travers asked about exchange resampling, a technique used in combination with the metric space analysis by the original authors, J.D. Victor and K.P. Purpura. She wanted to include this analysis, which is not a part of the STAToolkit. I put Susan in touch with Jonathan Victor by email, who provided (without any warranty!) the following Matlab code:
function Xres=xin_res(X,mode)
% Xres=xin_res(X,mode) creates an resampled dataset of a single-channel STAToolkit
% input structure X
%
% X: a spike train input structure
% mode
% 'exch' (default): exchange resampling: spikes within each category are
% randomly swapped among trials, so that each trial in Xres has the same
% number of spikes as in the original dataset
% 'pois': all spike times from a trial are randomly reassigned to all
% trials, without replacement; resampled trials will no longer have the
% same number of spikes as the original trials
% Xres: the resampled spike train
%
% See also: TLSINFO_SURRMAKE.
%
Xres=X;
if (nargin<=1) mode='exch'; end
for ic=1:X.M
counts=[];
times=[];
indices=[];
for itrial=1:X.categories(ic).P;
nspikes=double(X.categories(ic).trials(itrial).Q); %this was int32
counts=[counts nspikes];
times=[times X.categories(ic).trials(itrial).list];
indices=[indices repmat(itrial,1,nspikes)];
end
if strcmp(mode,'exch')
indices=indices(randperm(sum(counts))); %resampled indices
end
if strcmp(mode,'pois')
indices=ceil(double(X.categories(ic).P)*rand(1,sum(counts))); %resampled indices
end
for itrial=1:X.categories(ic).P;
res_times=sort(times(find(indices==itrial)));
Xres.categories(ic).trials(itrial).list=res_times;
Xres.categories(ic).trials(itrial).Q=int32(length(res_times));
end %itrial
end %ic
return
Note that this function takes a STAToolkit data structure, does exchange or Poisson resampling, and returns the resampled structure, but it only works for single-neuron (not multineuron) data. --- //[[mar2022@med.cornell.edu|Michael Repucci]] 2009/07/10 17:28//
==== To Jackknife or Bootstrap ====
In response to a user inquiry, Jonathan Victor offered the following information:
You raise an interesting issue [to jackknife or bootstrap], and I can give you my reasons for what we do.
While it is true that in general statisticians prefer boot to jack, there is a specific reason for not wanting to do so here.
When you do a bootstrap, you will often have datasets in which you've chosen exactly the same spike train twice. This will **never** happen if you had collected more data. It's not a problem if, say, you were estimating firing rate, but if you are estimating information, it would tend to bias the result -- since it would suggest that detailed temporal patterns can be reproduced. This bias does not arise with a jackknife.
Put another way -- your point about having more surrogate data sets (as you do with the boot) is correct -- it makes the error bars less variable -- but they are not necessarily more reliable. That is, the additional surrogates (that you get with the boot) are of questionable value if the surrogates are not typical of the data -- and bootstrap surrogates will always have more "clumpiness" than the original data.
Bottom line, I think that the jackknife is more conservative; one risks "fooling oneself" with all of the surrogates that you can generate via the bootstrap. But if you have a gross excess of data (whatever that is), then, in principle, the bootstrap would be better.
One final caveat -- neither the jackknife nor the bootstrap give provably **rigorous** error bars, in that one of the formal conditions for them to work is violated -- specifically, there are circumstances in which an infinitesimal change in a data point leads to a discrete jump in information. Both jackknife and bootstrap formally require continuity, and that kind of circumstance violates continuity. (But it is often the case that statistics are useful even if some formal condition is violated -- e.g., Gaussianness -- so we don't worry about this -- it's just full disclosure.)
--- //[[mar2022@med.cornell.edu|Michael Repucci]] 2009/08/31 18:00//