transformSpikes

 %function [newSpikes2,newTimestamps2] = transformSpikes( spikes, timestamps, noiseAutocorr )
 
 figure(776)
 plot( spikeWaveformsNegative(:,:)' );
 figure(775)
 plot( filteredSignal )
 
 stdEst=std(noiseTraces(:));
 
 noise=[];
 for i=1:size(noiseTraces,1)
     noise(i,:)=noiseTraces(i,:)./std(noiseTraces(i,:));
 end
 
 
 spikeWaveformsNegative=spikeWaveformsNegative./stdEst;
 
 corrEst=[];
 for i=1:size(noise,1)
     %[X,R]=corrmtx(noise(i,:),63,'covariance');
     cc=xcorr( noise(i,:), 63, 'biased');
     corrEst(i,:)=cc(64:end);
 end
 
 autocorr=mean(corrEst);