【数据分析】基于DA-STDP学习的Izhikevich神经元尖峰神经网络附matlab代码-优快云博客

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🔥 内容介绍

本文简要探讨基于Dopamine-modulated Spike-Timing-Dependent Plasticity (DA-STDP)学习规则的Izhikevich神经元尖峰神经网络。Izhikevich神经元模型以其计算效率和生物逼真性而著称，能够模拟神经元的多种放电模式。将其与DA-STDP学习规则结合，则赋予网络更强的学习能力和适应性。

DA-STDP学习规则模拟了多巴胺神经递质对突触可塑性的调制作用。不同于传统的STDP规则仅依赖于前后神经元脉冲时间的相对关系，DA-STDP 引入了多巴胺信号作为奖励信号，影响突触权重的更新方向和幅度。当网络行为满足预期目标时，多巴胺水平升高，增强奖励性突触连接；反之，则减弱非奖励性连接。

基于DA-STDP学习的Izhikevich神经元网络，能够在复杂的时空模式中学习和提取特征。其应用前景广泛，例如在机器人控制、模式识别和脑机接口等领域具有潜在的优势。然而，该网络的训练效率和参数优化仍是需要进一步研究的关键问题，例如如何有效地设计多巴胺奖励机制以及如何避免局部最优解。未来的研究有望在这些方面取得突破，进一步提升该网络的性能和应用价值。

📣 部分代码

% Load all data% Initialize a cell to store loaded datadata_structs = cell(length(files), 1);for i = 1:length(files)    data_structs{i} = load(files{i}); % Load each file and store itend% Initialize variables to store learning rateslearning_rates = zeros(length(files), 1);% Create a new figure for the comparative plot with a larger sizefigure('Position', [100, 100, 800, 600]); % Width 800px, Height 600pxhold on; % Keep the plot open to add multiple traces% Iterate over each loaded file and calculate the learning ratefor i = 1:length(files)    % Extract the relevant variable (synaptic histogram)    shist_data = data_structs{i}.shist;        % Create a time vector for the full duration    time_vector_full = 0.001 * (1:length(shist_data))'; % Convert to seconds    % Find the first moment when shist_data(:, 1) reaches Wm = 4    idx_end = find(shist_data(:, 1) >= 4, 1, 'first');    if isempty(idx_end)        idx_end = length(shist_data(:, 1)); % If it does not reach 4, use the last index    end    % Calculate synaptic strength for shist(:, 1) up to the found index    synaptic_strength = shist_data(1:idx_end, 1);    time_vector = time_vector_full(1:idx_end);    % Fit a straight line to the data to calculate the learning rate    p = polyfit(time_vector, synaptic_strength, 1);    learning_rates(i) = p(1); % Store the slope as the learning rate    disp(['Learning rate (' labels{i} '): ', num2str(learning_rates(i))]); % Display the learning rate    % Plot the synaptic strength data    plot(time_vector_full, shist_data(:, 1), 'Color', colors{i}, 'LineWidth', 2.5, ...         'DisplayName', [labels{i} ' (LR: ' num2str(learning_rates(i)) ')']);end% Customize the plotxlabel('Time (s)', 'FontSize', 12, 'FontWeight', 'bold', 'FontName', 'Arial'); % X-axis labelylabel('Synaptic strength', 'FontSize', 12, 'FontWeight', 'bold', 'FontName', 'Arial'); % Y-axis labeltitle('Synaptic Strength Over Time', 'FontSize', 14, 'FontWeight', 'bold', 'FontName', 'Arial'); % Plot titlelegend('show', 'Location', 'northoutside', 'FontSize', 10, 'FontName', 'Arial'); % Show legend at the topset(gca, 'FontSize', 12, 'LineWidth', 1.5, 'FontName', 'Arial'); % Customize axes% Remove grid lines from the plotset(gca, 'XGrid', 'off', 'YGrid', 'off');hold off; % Release the plot% Save the figure as a PNG filesaveas(gcf, 'synaptic_strength_comparison.png');