Description of sta

0001 function[s,t,E] = sta(data_spk,data_lfp,smp,plt,w,T,D,err)
0002 % Spike Triggered Average
0003 %     Usage: [s,t,E] = sta(data_spk,data_lfp,smp,plt,w,T,D,err)
0004 %
0005 % Inputs
0006 %
0007 % Note that all times have to be consistent. If data_spk
0008 % is in seconds, so must be sig and t. If data_spk is in
0009 % samples, so must sig and t. The default is seconds.
0010 %
0011 % data_spk    - strucuture array of spike times data
0012 %               or NaN padded matrix
0013 % data_lfp    - array of lfp data(samples x trials)
0014 %
0015 % Optional...
0016 % plt 'n'|'r' etc
0017 % width kernel smoothing in s
0018 % T = [-0.1 0.1] - extract this range about each spk
0019 % D = plot spike triggered average out to [D1 D2]
0020 % err = calcluate error bars (bootstrap)
0021 %
0022 % Outputs:
0023 %
0024 % s  spike triggered average
0025 % t  times
0026 % E  bootstrap standard err
0027 
0028 if nargin < 3;error('Require spike, lfp and lfp times');end
0029 if isstruct(data_spk)
0030    [data_spk]=padNaN(data_spk); % create a zero padded data matrix from input structural array
0031    sz=size(data_spk); 
0032    if sz(1)>sz(2); data_spk=data_spk'; end;% transpose data to get in form compatible with Murray's routine
0033 else
0034    sz=size(data_spk);
0035    if sz(1)>sz(2); data_spk=data_spk'; end;% transpose data to get in form compatible with Murray's routine
0036 end
0037 sz=size(data_lfp);
0038 if sz(1)>sz(2); data_lfp=data_lfp'; end;% transpose data to get in form compatible with Murray's routine
0039 verbose = 1;
0040 t = smp;
0041 if nargin < 4; plt = 'r'; end
0042 if nargin < 5; w = 0; end
0043 if nargin < 6; T = [min(t) max(t)]; end
0044 if nargin < 7; D = 0.25*[-1 1]; end
0045 if nargin < 8; err = 1;end
0046 
0047 if isempty(plt); plt = 'r'; end
0048 if isempty(w); w = 0; end
0049 if isempty(T); T = [min(t) max(t)]; end
0050 if isempty(D); D = 0.25*[-1 1]; end
0051 if isempty(err); err = 1;end
0052 
0053 if w > (T(2)-T(1))/2
0054   disp('Smoothing > data segment : should be in seconds : turn off smoothing')
0055   w = 0;
0056 end
0057 
0058 sz = size(data_spk);
0059 NT = sz(1);
0060 mlfp = 0;
0061 slfp = 0;
0062 Nspk = 0;
0063 smp = t(2)-t(1);
0064 if D(1) <= 0 && D(2) >= 0
0065   t1 = [D(1):smp:(-smp+eps) 0:smp:D(2)+eps];
0066 else
0067   t1 = (round(D(1)/smp)*smp):smp:D(2);
0068 end
0069 
0070 % count up the spikes...
0071 
0072 if err
0073   for n=1:NT
0074     indx = find(t>T(1)&t<T(2));
0075 %     lfp = data_lfp(n,indx);
0076     spk = data_spk(n,data_spk(n,:)>T(1) && data_spk(n,:)<T(2) && data_spk(n,:)~=0);
0077     tt = t(indx);
0078     if ~isempty(spk) > 0
0079       ND = length(spk);
0080       for s=1:ND
0081         spktime = spk(s);      
0082         t0 = tt-spktime + eps;
0083         if min(t0)>(D(1)-smp) || max(t0)<(D(2)+smp); break;end
0084         Nspk = Nspk + 1;
0085       end
0086     end
0087   end
0088   Err = zeros(Nspk,length(t1));
0089   Nspk = 0;
0090 end
0091 
0092 for n=1:NT
0093   indx = find(t>T(1)&t<T(2));
0094   lfp = data_lfp(n,indx);
0095   spk = data_spk(n,data_spk(n,:)>T(1) && data_spk(n,:)<T(2) && data_spk(n,:)~=0);
0096   tt = t(indx);
0097   if ~isempty(spk) > 0
0098     ND = length(spk);
0099     for s=1:ND
0100       spktime = spk(s);      
0101       t0 = tt-spktime + eps;
0102       if min(t0) < (D(1)-smp) && max(t0) > (D(2)+smp);
0103         indx = find(t0<D(1));
0104         indx = indx(length(indx));   
0105         offset = (t0(indx)-D(1))/smp; 
0106         indx = indx:(indx+length(t1)-1);
0107         lfp_t1 = lfp(indx) + (lfp(indx+1)-lfp(indx))*offset;
0108         Nspk = Nspk + 1;
0109         mlfp = mlfp + lfp_t1;
0110         slfp = slfp + lfp_t1.^2;      
0111         Err(Nspk,:) = lfp_t1;
0112       end
0113     end    
0114   end  
0115 end
0116 if Nspk == 0
0117   if verbose;disp('No spikes in interval');end
0118   t = t1;
0119   s = zeros(length(t),1);
0120   E = zeros(length(t),1);
0121   return
0122 end
0123 mlfp = mlfp/Nspk;
0124 slfp = slfp/Nspk;
0125 stdlfp = sqrt((slfp - mlfp.^2)/Nspk);
0126 
0127 % local smoother...
0128 
0129 N = fix(w/smp);
0130 if N > 5
0131   mlfp = locsmooth(mlfp,N,fix(N/2)); 
0132 end
0133 
0134 % bootstrap errorbars...
0135 
0136 if err == 1;
0137   Nboot = 20;  
0138   blfp = 0;
0139   slfp = 0;
0140   for n = 1:Nboot
0141     indx = floor(Nspk*rand(1,Nspk)) + 1;
0142     lfptmp = mean(Err(indx,:));
0143     if N > 5
0144       lfptmp = locsmooth(lfptmp,N,fix(N/2));
0145     end
0146     blfp = blfp + lfptmp;
0147     slfp = slfp + lfptmp.^2;
0148   end
0149   stdlfp = sqrt((slfp/Nboot - blfp.^2/Nboot^2));
0150 end  
0151 
0152 s = mlfp-mean(mlfp);
0153 E = stdlfp;
0154 t = t1;
0155 
0156 %cols = 'krbgycm';
0157 if plt == 'n';return;end
0158 plot(t1,s,plt)
0159 xax = get(gca,'xlim');
0160 %yax = get(gca,'ylim');
0161 if err == 1
0162   me = real(2*mean(stdlfp));
0163   line(xax,me*[1 1],'color','b')
0164   line(xax,-me*[1 1],'color','b')
0165   line(xax,0*[1 1],'color','k')
0166 %  errorbar(0.1*xax(2)+0.9*xax(1),0.1*yax(2)+0.9*yax(1), ...
0167 %         mean(stdlfp),'k')
0168 %plot(0.1*xax(2)+0.9*xax(1),0.1*yax(2)+0.9*yax(1),'k.')
0169 end
0170      
0171 title(['spike triggered average : ' ...
0172       num2str(Nspk) ' used : '     ...
0173       ' Errorbars are two std err']);
0174 %line(get(gca,'xlim'),mean(mlfp)*[1 1],'color','k')
0175 line([0 0],get(gca,'ylim'),'color','k')
0176 hold off
0177 
0178 
0179 
0180 
0181 
0182 
0183 
0184
sta

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