Faster Matlab Code for Spike Time Distances Between Labeled (Multineuronal) Spike Trains, Parallel in q and k, Space-Optimized

Thomas Kreuz:

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

Faster Matlab code for Multineuronal Spike Time Metric, Parallel in q and k, Space-Optomized

function labdist_faster_qk_mat = labdist_faster_qkpara_opt(sa,la,sb,lb,q,k,max_size) % LABDIST_FASTER_QKPARA(SA,LA,SB,LB,Q,K), parallel in q and k and avoiding "out of memory"-error % 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 - vector of timing precision parameters % K - vector of label reassigning parameters % Max_Size - Maximum size of matrix m4 (to prevent "out of memory"-error % (if the input parameter is omitted it is defined below) % % Thomas Kreuz, 10/25/08; based on code by Dmitriy Aronov, 6/20/01; if nargin<7 max_size=10000000; end %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) % corrected t = la; la = lb; lb = t; t = sa; sa = sb; sb = t; t = numa; numa=numb; numb = t; % disp('Spike trains exchanged') end tb=zeros(L,max(numb)); for c = 1:L tb(c,1:numb(c))=sb(logical(lb==c)); end % subdivide parameter vectors (if necessary) m2_size=prod(numb+1)*(sum(numa)+1); if m2_size>max_size disp(' ') error('Too many spikes! Calculation impossible !!!') else lkqs=[length(k) length(q)]; m4_size=prod(lkqs)*m2_size; lkq=lkqs; paras=zeros(2,max(lkqs)); paras(1,1:lkqs(1))=k; paras(2,1:lkqs(2))=q; if m4_size>max_size while m4_size>max_size [ml,mli]=max(lkq); lkq(mli)=ceil(lkq(mli)/2); m4_size=prod(lkq)*m2_size; end kq_runs=ceil(lkqs./lkq); kq_calls=zeros(2,max(kq_runs)+1); for kqc=1:2 kq_calls(kqc,2:kq_runs(kqc)+1)=fix(lkqs(kqc)/kq_runs(kqc))*ones(1,kq_runs(kqc)); kq_calls(kqc,2:mod(lkqs(kqc),kq_runs(kqc))+1)=kq_calls(kqc,2:mod(lkqs(kqc),kq_runs(kqc))+1)+1; end kqm=zeros(2,max(kq_runs),max(max(kq_calls))); for kqc=1:2 for kqc2=1:kq_runs(kqc) kqm(kqc,kqc2,1:kq_calls(kqc,kqc2+1))=paras(kqc,sum(kq_calls(kqc,1:kqc2))+(1:kq_calls(kqc,kqc2+1))); end end else kq_runs=[1 1]; kq_calls=[zeros(1,2)' lkqs']; kqm=zeros(2,1,max(max(kq_calls))); kqm(1,1,1:kq_calls(1,2))=k; kqm(2,1,1:kq_calls(2,2))=q; end 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 m2 = zeros(size(ind,1),size(sa,2)+1); m2(1,:) = 0:size(sa,2); m2(:,1) = sum(ind,2); labdist_faster_qk_mat=zeros(length(k),length(q)); for kc=1:kq_runs(1) for qc=1:kq_runs(2) % kqc=[kc qc] kv=shiftdim(kqm(1,kc,1:kq_calls(1,kc+1)),2)'; qv=shiftdim(kqm(2,qc,1:kq_calls(2,qc+1)),2)'; clear m4 m4 = repmat(shiftdim(m2,-2),[length(kv),length(qv),1,1]); %Perform the calculation for v = 2:size(m4,3) fa2=find(shiftdim(m4(1,1,:,1),2)==m4(1,1,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(m4,4) m4(:,:,v,w)=min(cat(3,m4(:,:,v,w-1)+1,m4(:,:,fa,w)+1,m4(:,:,fa,w-1)+ ... repmat(shiftdim(qv'*abs(sa(w-1)-bsv'),-1),[length(kv),1,1])+ ... permute(repmat(shiftdim(kv'*not(la(w-1)==fth)',-1),[length(qv),1,1]),[2 1 3])),[],3); end end labdist_faster_qk_mat(sum(kq_calls(1,1:kc))+(1:kq_calls(1,kc+1)),sum(kq_calls(2,1:qc))+(1:kq_calls(2,qc+1))) = m4(:,:,end,end); end end