vlt.neuro.spiketrains.spiketrain2rate

  SPIKETRAIN2RATE - compute a 'blurred' spike rate from 0/1 spike train

  RATE = vlt.neuro.spiketrains.spiketrain2rate(SPIKETRAIN, DT, BLUR, ...)

  Given a vector of 0/1 binned SPIKETRAIN data and the sampling interval DT
  of the SPIKETRAIN, estimate a RATE of the spiking process by blurring with
  a parameter BLUR.

  This function takes extra parameters as name/value pairs that modify its behavior:
  Parameter (default)        | Description
  -----------------------------------------------------------------------------
  BlurFunc ('gaussian')      | The blur function to use. Default is 'gaussian'.

  See also: vlt.data.namevaluepair, vlt.neuro.spiketrains.spiketimes2bins


  Example:
    dt = 0.001; t_start = 0; t_end = 50/2;
    maxrate = 50; tFreq = 2; phase = 0; rate_offset = 20;
    ST = vlt.neuro.spiketrains.spiketrain_sinusoidal(maxrate,tFreq,phase,rate_offset,t_start,t_end,dt); % 50 cycles at 2Hz
    T = t_start:dt:t_end;
    SC = vlt.neuro.spiketrains.spiketimes2bins(ST,T);
    R = vlt.neuro.spiketrains.spiketrain2rate(SC,dt,0.005);
    figure;
    subplot(2,1,1);
    plot(T,R);
    hold on;
    plot(T,vlt.math.rectify(rate_offset+maxrate*sin(2*pi*tFreq*T+phase)),'g');
    xlabel('Time(s)'); ylabel('Rate (spikes/s)'); box off;
    subplot(2,1,2);
    plot(cumsum(R),cumsum(vlt.math.rectify(rate_offset+maxrate*sin(2*pi*tFreq*T+phase))),'bo');
    hold on;
    plot([0 sum(R)],[0 sum(R)],'k--');
    xlabel('Cumulative rate measured (spikes)'); ylabel('Actual cumulative rate (spikes)'); 
    axis equal square; box off;