# Faster Matlab Code for Spike Time Distances Between Labeled (Multineuronal) Spike Trains

#### Thomas Kreuz: tkreuz@ucsd.edu

main cost-based metrics page
algorithm page for cost-based metrics

## Faster Matlab code for Multineuronal Spike Time Metric

function labdist_faster = labdist_faster(sa,la,sb,lb,q,k) %LABDIST_FASTER(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; modified by Thomas Kreuz, 10/19/08 %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)*(sum(numa)+1) > prod(numa+1)*(sum(numb)+1) t = la; la = lb; lb = t; t = sa; sa = sb; sb = t; t = numa; % numa=numb; numb = t; end tb=zeros(L,max(numb)); for c = 1:L tb(c,1:numb(c))=sb(logical(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,numel(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) fa2=find(m(:,1)==m(v,1)-1); fa=fa2(logical(sum(ind(fa2,:)-repmat(ind(v,:),length(fa2),1)==0,2)==L-1)); fth=find(ind(v,:)>0)'; bsv=diag(tb(fth,ind(v,fth))); for w = 2:size(m,2) m(v,w)=min([m(v,w-1)+1; m(fa,w)+1; m(fa,w-1)+q*abs(sa(w-1)-bsv)+k*not(la(w-1)==fth)]); end end labdist_faster = m(end,end);