forgotten changes
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@ -1,16 +1,15 @@
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load('ampullary.mat')
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load('electroreceptor_stimulus.mat')
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sample_rate = 20000; % Hz
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max_time = 0;
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for i = 1:size(times,2)
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max_time = max([max_time, max(times{i})]);
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end
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%% create PSTH on the basis of the interspike intervals
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max_time = 10;
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%% create instantaneous firing rate on the basis of the interspike intervals
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t = times{1};
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firing_rate = [0 1./diff(t)];
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start = 1;
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resp = zeros(1, round(max_time * sample_rate));
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response = zeros(1, round(max_time * sample_rate));
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for i = 1:length(t)
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resp(1,start:round(t(i) * sample_rate)) = firing_rate(i);
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response(1,start:round(t(i) * sample_rate)) = firing_rate(i);
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start = round(t(i) * sample_rate);
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end
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fig = figure();
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@ -18,7 +17,7 @@ set(gcf, 'PaperUnits', 'centimeters');
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set(gcf, 'PaperSize', [11.7 9.0]);
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set(gcf, 'PaperPosition',[0.0 0.0 11.7 9.0]);
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set(gcf,'Color', 'white')
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plot((1/sample_rate:1/sample_rate:max_time), resp)
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plot((1/sample_rate:1/sample_rate:max_time), response)
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xlabel('time [s]')
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ylabel('firing rate [Hz]')
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ylim([0 300])
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@ -26,6 +25,7 @@ xlim([0 1])
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title('instanataneous firing rate')
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saveas(fig, 'isi.pdf','pdf')
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%% create PSTH using the binning method
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bin_width = 0.0125; % s
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edges = 0:bin_width:max_time;
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@ -39,22 +39,33 @@ for i = 1:size(times,2)
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firing_rate = firing_rate + (n / bin_width / size(times,2));
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end
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end
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response = zeros(1, round(max_time * sample_rate));
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start_index = 1;
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for i = 1:length(edges)
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end_index = round(edges(i) * sample_rate);
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response(start_index:end_index) = firing_rate(i);
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start_index = end_index +1;
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end
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fig = figure();
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set(gcf, 'PaperUnits', 'centimeters');
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set(gcf, 'PaperSize', [11.7 9.0]);
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set(gcf, 'PaperPosition',[0.0 0.0 11.7 9.0]);
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set(gcf,'Color', 'white')
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plot(time, firing_rate)
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plot(0:1/sample_rate:max_time-1/sample_rate, response)
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ylim([0 300])
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xlim([0 1])
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xlabel('time [s]')
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ylabel('firing rate [Hz]')
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title('binning method')
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saveas(fig, 'binning.pdf', 'pdf')
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%% create PSTH using the kernel-convolution method
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kernel_width = 0.0125; %s
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binary_spikes = zeros(size(times,2), round(max_time*sample_rate));
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resps = zeros(size(binary_spikes));
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responses = zeros(size(binary_spikes));
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window = hann(kernel_width*sample_rate,'symmetric');
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window = window/sum(window);
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@ -65,16 +76,25 @@ for i = 1:size(times,2)
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temp(1) = 1;
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end
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binary_spikes(i, temp) = 1;
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resps(i,:) = conv(binary_spikes(i,:), window, 'same')*sample_rate;
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responses(i,:) = conv(binary_spikes(i,:), window, 'same')*sample_rate;
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end
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fig = figure();
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set(gcf, 'PaperUnits', 'centimeters');
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set(gcf, 'PaperSize', [11.7 9.0]);
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set(gcf, 'PaperPosition',[0.0 0.0 11.7 9.0]);
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set(gcf,'Color', 'white')
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plot((1/sample_rate:1/sample_rate:max_time), mean(resps,1))
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subplot(2,1,1)
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plot((1/sample_rate:1/sample_rate:max_time), mean(responses,1))
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ylim([0 300])
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xlim([0 1])
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xlabel('time [s]')
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ylabel('firing rate [Hz]')
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title('convolution method')
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subplot(2,1,2)
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plot(stimulus_strong(:,1), stimulus_strong(:,2))
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ylim([-1 1])
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xlim([0 1])
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xlabel('time [s]')
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ylabel('stimulus intensity [arb. units]')
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@ -76,7 +76,7 @@ def load_spike_data(cell, max_time):
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if data[0][2][-1]/1000 < max_time:
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return None
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else:
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return data[0][2][data[0][2] <= max_time*1000]
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return data[0][2][data[0][2] <= max_time*1000]/1000
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if __name__ == '__main__':
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@ -103,12 +103,11 @@ if __name__ == '__main__':
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index += 1
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continue
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spikes = load_spike_data(cell, 20)
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if spikes is None:
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print 'not enough baseline spikes'
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index += 1
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continue
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eod_data = load_eod_data(cell, 20, 200000)
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eod_data = load_eod_data(cell, 20, 20000)
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name = cell.split('/')[-1] + '_baseline.mat'
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sp.savemat(name, {'spike_times':spikes, 'eod':eod_data})
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found += 1
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@ -38,7 +38,7 @@ plot(x,y,'ok');
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%%
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t = linspace(-5,5,100);
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param0 = [0,0];
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param0 = [1,1];
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step = 0.01;
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param = param0;
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