Matlab Code for Spike Time Distances Between Labeled (Multineuronal) Spike
Trains
main cost-based metrics page
algorithm page for cost-based metrics
Matlab code for Multineuronal Spike Time Metric
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function labdist_fast = labdist_fast(sa,la,sb,lb,q,k)
%LABDIST_FAST(SA,LA,SB,LB,Q,K)
% Calculates the multi-unit metric distance between two spike trains
% Uses a fast version of the algorithm
% SA, SB - spike times on the two spike trains
% LA, LB - spike labels (positive integers)
% Q - timing precision parameter
% K - label reassigning parameter
%
% Dmitriy Aronov, 6/20/01
%Assign labels in the form 1,2,...,L and count spikes of each label
lbs = unique([la lb]);
L = size(lbs,2);
for c = 1:L
j = find(la==lbs(c));
la(j) = c;
numa(c) = size(j,2);
j = find(lb==lbs(c));
lb(j) = c;
numb(c) = size(j,2);
end
%Choose the spike train to separate to subtrains
if prod(numb+1) < prod(numa+1)
t = la;
la = lb;
lb = t;
t = sa;
sa = sb;
sb = t;
t = numa;
numa = numb;
numb = t;
end
for c = 1:L
tb{c} = sb(find(lb==c));
end
%Set up an indexing system
ind = [];
for c = 1:L
j = repmat(0:numb(c),prod(numb(c+1:end)+1),1);
j = repmat(reshape(j,prod(size(j)),1),prod(numb(1:c-1)+1),1);
ind = [ind j];
end
ind = sortrows([sum(ind,2) ind]);
ind = ind(:,2:end);
%Initialize the array
m = zeros(size(ind,1),size(sa,2)+1);
m(1,:) = 0:size(sa,2);
m(:,1) = sum(ind,2);
%Perform the calculation
for v = 2:size(m,1)
for w = 2:size(m,2)
alt = m(v,w-1) + 1;
th = ind(v,:);
for c = find(th > 0);
ps = th;
ps(c) = ps(c) - 1;
n = find(sum(abs(ind-repmat(ps,size(ind,1),1)),2)==0);
alt = [alt m(n,w) + 1];
qcost = q*abs(sa(w-1) - tb{c}(th(c)));
kcost = k*not(la(w-1) == c);
alt = [alt m(n,w-1) + qcost + kcost];
end
m(v,w) = min(alt);
end
end
labdist_fast = m(end,end);
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