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genSerialCorr.m
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genSerialCorr.m
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function spikeTrains = genSerialCorr(N, M, param)
% Renewal process and a serially correlated process with same ISI.
% spikeTrains = genSerialCorr(N, M, param)
%
% Input
% N: trials per spikeTrains
% M: number of sets of trials
% param.T: length of the spike train
% param.mISI: mean ISI
% param.urISI: half width of uniform distribution for the ISI.
% param.type: 'PTST' or 'equPoisson' (equi-rate Poisson process)
%
% $Id$
% Copyright 2009 Memming. All rights reserved.
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
% - Redistributions of source code must retain the above copyright notice,
% this list of conditions and the following disclaimer.
% - Redistributions in binary form must reproduce the above copyright notice,
% this list of conditions and the following disclaimer in the documentation
% and/or other materials provided with the distribution.
% - Neither the name of the iocane project nor the names of its contributors
% may be used to endorse or promote products derived from this software
% without specific prior written permission.
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
% POSSIBILITY OF SUCH DAMAGE.
T = param.T;
mISI = param.mISI;
urISI = param.urISI;
type = param.type;
mISI = mISI - urISI;
spikeTrainsTemplate.N = N;
spikeTrainsTemplate.duration = T;
spikeTrainsTemplate.source = [type ' - $Id$'];
spikeTrainsTemplate.subtype = type;
spikeTrainsTemplate.data = cell(N, 1);
spikeTrainsTemplate.samplingRate = Inf;
switch(lower(type))
case {'correlated', 'serial correlation'}
isCorrelated = 1;
case {'renewal', 'uncorrelated'}
isCorrelated = 0;
otherwise
error('Unknown type: use either correlated or renewal');
end
for kM = 1:M
spikeTrains(kM) = spikeTrainsTemplate;
for k = 1:N
st = [0];
if ~isCorrelated
while st(end) < T
isi = mISI + rand * urISI + rand * urISI;
st = [st; st(end) + isi];
end
else
lastJitter = rand * urISI;
while st(end) < T
currentJitter = rand * urISI;
isi = mISI + currentJitter + lastJitter;
lastJitter = currentJitter;
st = [st; st(end) + isi];
end
end
st = st(2:end-1);
spikeTrains(kM).data{k} = st;
end
end
% vim:ts=8:sts=4:sw=4