SNN脉冲神经图像识别【MATLAB代码】

classdef Tempotron < handle % A class representing a tempotron, as described in % Gutig & Sompolinsky (2006). % The (subthreshold) membrane voltage of the tempotron % is a weighted sum from all incoming spikes and the % resting potential of the neuron. The contribution of % each spike decays exponentiall with time, the speed of % this decay is determined by two parameters tau and tau_s, % denoting the decay time constants of membrane integration % and synaptic currents, respectively. properties V_rest tau tau_s log_tts threshold = 1.0; efficacies t_spi = 10; V_norm end methods function obj = Tempotron(V_rest, tau, tau_s, synaptic_efficacies, threshold) % set parameters as attributes obj.V_rest = V_rest; obj.tau = tau; obj.tau_s = tau_s; % usually smaller than tau, the recommended value: tau/4 obj.log_tts = log(obj.tau/obj.tau_s); if nargin>4, obj.threshold = threshold; end obj.efficacies = synaptic_efficacies(:); obj.t_spi = 10; % spike integration time, compute this with formula % compute normalisation factor V_0 obj.V_norm = obj.compute_norm_factor(tau, tau_s); end function V_0 = compute_norm_factor(obj, tau, tau_s) % Compute and return the normalisation factor: % V_0 = 1/K((tau * tau_s * log(tau/tau_s)) / (tau - tau_s)-0) % That normalises the function: % % K(t-t_i) = V_0 (exp(-(t-t_i)/tau) - exp(-(t-t_i)/tau_s) % Such that it amplitude is 1 and the unitary PSP % amplitudes are given by the synaptic efficacies. tmax = (tau * tau_s * log(tau/tau_s)) / (tau - tau_s); v_max = obj.K(1, tmax, 0); V_0 = 1/v_max; end function value = K(obj, V_0, t, t_i) % Compute the function % K(t-t_i) = V_0 (exp(-(t-t_i)/tau) - exp(-(t-t_i)/tau_s) % It gives a horizon line for tau = tau_s and a peak (valley) % for tau > tau_s (tau < tau_s). low = t<t_i; value = (~low).*(V_0 * (exp(-(t-t_i)/obj.tau) - exp(-(t-t_i)/obj.tau_s))); end function V = compute_membrane_potential(obj, t, spike_times) % Compute the membrane potential of the neuron given % by the function: % V(t) = sum_i w_i sum_{t_i} K(t-t_i) + V_rest % Where w_i denote the synaptic efficacies and t_i denote % ith afferent. % % spike_times: an cell array with at position i the spike times of % the i-th afferent % create an array with the contributions of the % spikes for each synaps spike_contribs = obj.compute_spike_contributions(t, spike_times); % multiply with the synaptic efficacies total_incoming = spike_contribs.*obj.efficacies; % add sum and add V_rest to get membrane potential V = sum(total_incoming) + obj.V_rest; end function deriv = compute_derivative(obj, t, spike_times) % Compute the derivative of the membrane potential % of the neuron at time t. % This derivative is given by: % V'(t) = V_0 sum_i w_i sum_{t_n} (exp(-(t-t_n)/tau_s)/tau_s - exp(-(t-t_n)/tau)/tau) % for t_n < t % sort spikes in chronological order spike_times = spike_times(:).'; ha = 0; ns = cellfun('length',spike_times); spikes_chron = zeros(sum(ns),2); for synapse = 1:length(spike_times) temp = spike_times{synapse}; if ~isempty(temp) time = temp(:); spikes_chron(ha+1:ha+ns(synapse),:) = [time, synapse*ones(ns(synapse),1)]; ha = ha + ns(synapse); end end spikes_chron = sortrows(spikes_chron,1); % Make a list of spike times and their corresponding weights spikes = [spikes_chron(:,1), obj.efficacies(spikes_chron(:,2))];% efficacies长度必须和spike_times一样 % At time t we want to incorporate all the spikes for which % t_spike < t ind = spikes(:,1) <= t; sum_tau = sum(spikes(ind,2).*exp(spikes(ind,1)/obj.tau)); sum_tau_s = sum(spikes(ind,2).*exp(spikes(ind,1)/obj.tau_s)); factor_tau = exp(-t/obj.tau)/obj.tau; factor_tau_s = exp(-t/obj.tau_s)/obj.tau_s; deriv = obj.V_norm * (factor_tau_s*sum_tau_s - factor_tau*sum_tau); end function spike_contribs = compute_spike_contributions(obj, t, spike_times) % Compute the decayed contribution of the incoming spikes. % nr of synapses N_synapse = length(spike_times); % loop over spike times to compute the contributions % of individual spikes spike_contribs = zeros(N_synapse,1); for neuron_pos = 1:N_synapse spike_time = spike_times{neuron_pos}; % disp(num2str(obj.K(obj.V_rest, t, spike_time)) spike_contribs(neuron_pos) = sum(obj.K(obj.V_norm, t,spike_time)); end end function train(obj, io_pairs, steps, learning_rate) % Train the tempotron on the given input-output pairs, % applying gradient decscend to adapt the weights. % steps: the maximum number of training steps % io_pairs: a list with tuples of spike times and the % desired response on them % learning_rate: the learning rate of the gradient descend % Run until maximum number of steps is reached or % no weight updates occur anymore for i = 1:steps % go through io-pairs in random order np = length(io_pairs); io_pairs = io_pairs(randperm(np)); for j = 1:np [spike_times, target] = deal(io_pairs{j}{:}); obj.adapt_weights(spike_times, target, learning_rate); end end end function [t, membrane_potentials] = get_membrane_potentials(obj, t_start, t_end, spike_times, interval) % Get a list of membrane potentials from t_start to t_end % as a result of the inputted spike times. if nargin<5, interval = 0.1; end % create vectorised version of membrane potential function potential_vect = @(t)obj.compute_membrane_potential(t,spike_times); % compute membrane potentials t = t_start:interval:(t_end-interval); membrane_potentials = arrayfun(potential_vect,t); end function [t,derivatives] = get_derivatives(obj, t_start, t_end, spike_times, interval) % Get a list of the derivative of the membrane potentials from % t_start to t_end as a result of the inputted spike times. if nargin<5, interval = 0.1; end % create a vectorised version of derivative function deriv_vect = @(t)obj.compute_derivative(t,spike_times); % exclude spike times from being vectorised % compute derivatives t = t_start:interval:(t_end-interval