added lifspikes.m to fano time project
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function spikes = lifadaptspikes( trials, input, tmaxdt, D, tauadapt, adaptincr )
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% Generate spike times of a leaky integrate-and-fire neuron
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% with an adaptation current
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% trials: the number of trials to be generated
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% input: the stimulus either as a single value or as a vector
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% tmaxdt: in case of a single value stimulus the duration of a trial
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% in case of a vector as a stimulus the time step
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% D: the strength of additive white noise
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% tauadapt: adaptation time constant
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% adaptincr: adaptation strength
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tau = 0.01;
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if nargin < 4
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D = 1e0;
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end
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if nargin < 5
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tauadapt = 0.1;
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end
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if nargin < 6
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adaptincr = 1.0;
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end
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vreset = 0.0;
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vthresh = 10.0;
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dt = 1e-4;
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if max( size( input ) ) == 1
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input = input * ones( ceil( tmaxdt/dt ), 1 );
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else
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dt = tmaxdt;
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end
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spikes = cell( trials, 1 );
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for k=1:trials
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times = [];
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j = 1;
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v = vreset;
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a = 0.0;
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noise = sqrt(2.0*D)*randn( length( input ), 1 )/sqrt(dt);
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for i=1:length( noise )
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v = v + ( - v - a + noise(i) + input(i))*dt/tau;
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a = a + ( - a )*dt/tauadapt;
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if v >= vthresh
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v = vreset;
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a = a + adaptincr/tauadapt;
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spiketime = i*dt;
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if spiketime > 4.0*tauadapt
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times(j) = spiketime - 4.0*tauadapt;
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j = j + 1;
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end
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end
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end
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spikes{k} = times;
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end
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end
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35
projects/project_fano_time/lifspikes.m
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35
projects/project_fano_time/lifspikes.m
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@ -0,0 +1,35 @@
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function spikes = lifspikes(trials, input, tmax)
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% Generate spike times of a leaky integrate-and-fire neuron.
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% trials: the number of trials to be generated
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% input: the stimulus intensity
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% tmax: the duration of a trial
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% D: the strength of additive white noise
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tau = 0.01;
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if nargin < 4
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D = 1e0;
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end
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vreset = 0.0;
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vthresh = 10.0;
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D = 0.01;
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dt = 5e-5;
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n = ceil(tmax/dt);
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spikes = cell(trials, 1);
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for k=1:trials
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times = [];
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j = 1;
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v = vreset + (vthresh-vreset)*rand(1);
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noise = sqrt(2.0*D)*randn(n, 1)/sqrt(dt);
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for i=1:length(noise)
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v = v + (- v + noise(i) + input)*dt/tau;
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if v >= vthresh
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v = vreset;
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spiketime = i*dt;
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times(j) = spiketime;
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j = j + 1;
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end
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end
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spikes{k} = times;
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end
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end
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