[pointprocesses] improved code

pointprocesses/code/binnedRate.m

@@ -18,7 +18,7 @@ function [time, rate] = binned_rate(spikes, bin_width, dt, t_max)
     rate = zeros(size(time));
     h = hist(spikes, bins) ./ bin_width;
     for i = 2:length(bins)
-        rate(round(bins(i - 1) / dt) + 1:round(bins(i) / dt)) = h(i);
+        rate(round(bins(i-1)/dt) + 1:round(bins(i)/dt)) = h(i);
     end
 end
 

pointprocesses/code/convolutionRate.m

@@ -10,19 +10,18 @@ function [time, rate] = convolution_rate(spikes, sigma, dt, t_max)
 %   t_max : the trial duration in seconds.
 %
 % Returns:
-    two vectors containing the time and the rate.
+%   two vectors containing the time and the rate.
 
-time = 0:dt:t_max - dt;
-rate = zeros(size(time));
-spike_indices = round(spikes / dt);
-rate(spike_indices) = 1;
-kernel = gaussKernel(sigma, dt);
-
-rate = conv(rate, kernel, 'same');
+    time = 0:dt:t_max - dt;
+    rate = zeros(size(time));
+    spike_indices = round(spikes / dt);
+    rate(spike_indices) = 1;
+    kernel = gaussKernel(sigma, dt);
+    rate = conv(rate, kernel, 'same');
 end
 
 
 function y = gaussKernel(s, step)
     x = -4 * s:step:4 * s;
-    y = exp(-0.5 .* (x ./ s) .^ 2) ./ sqrt(2 * pi) / s;
+    y = exp(-0.5 .* (x ./ s).^ 2) ./ sqrt(2 * pi) / s;
 end

pointprocesses/code/counthist.m

@@ -37,8 +37,8 @@ function [counts, bins] = counthist(spikes, w)
 
     % plot:
     if nargout == 0
-        bar( bins, counts );
-        xlabel( 'counts k' );
-        ylabel( 'P(k)' );
+        bar(bins, counts);
+        xlabel('counts k');
+        ylabel('P(k)');
     end
 end

pointprocesses/code/instantaneousRate.m

@@ -19,6 +19,6 @@ function [time, rate] = instantaneous_rate(spikes, dt, t_max)
     spike_indices = [1 round(spikes ./ dt)];
 
     for i = 2:length(spike_indices)
-        rate(spike_indices(i - 1):spike_indices(i)) = inst_rate(i - 1);
+        rate(spike_indices(i-1):spike_indices(i)) = inst_rate(i-1);
     end
 end

pointprocesses/code/rasterplot.m

@@ -5,8 +5,8 @@ function rasterplot(spikes, tmax)
 %   spikes: a cell array of vectors of spike times in seconds
 %   tmax: plot spike raster upto tmax seconds
 
-ntrials = length(spikes);
-for k = 1:ntrials
+    ntrials = length(spikes);
+    for k = 1:ntrials
         times = spikes{k};
         times = times(times<tmax);
         if tmax < 1.5
@@ -15,15 +15,15 @@ for k = 1:ntrials
         for i = 1:length( times )
             line([times(i) times(i)],[k-0.4 k+0.4], 'Color', 'k'); 
         end
-end
-if tmax < 1.5
+    end
+    if tmax < 1.5
         xlabel('Time [ms]');
         xlim([0.0 1000.0*tmax]);
-else
+    else
         xlabel('Time [s]');
         xlim([0.0 tmax]);
-end
-ylabel('Trials');
-ylim([0.3 ntrials+0.7 ]);
+    end
+    ylabel('Trials');
+    ylim([0.3 ntrials+0.7]);
 end
 

pointprocesses/lecture/pointprocesses-chapter.tex

@@ -25,9 +25,9 @@
 \item Multitrial firing rates
 \item Better explain difference between ISI method and PSTHes. The
   latter is dependent on precision of spike times the former not.
-\item Choice of bin width for PSTH, kernel width, also in relation sto
+\item Choice of bin width for PSTH, kernel width, also in relation to
   stimulus time scale
-\item Kernle firing rate: discuss different kernel shapes, in
+\item Kernel firing rate: discuss different kernel shapes, in
   particular causal kernels (gamma, exponential), relation to synaptic
   potentials
 \end{itemize}