脉冲神经网络(SNN)核心算法

python

class SpikingNeuralNetwork:

    def init(self, topology):

        self.neurons = []  # 神经元集合

        self.synapses = [] # 突触连接集合

        self.learning_rules = {}  # 学习规则

    

    def forward_pass(self, input_spike_train):

        """前向传播算法"""

        spike_history = []

        

        for t in range(time_steps):

            # 计算每个神经元的膜电位

            membrane_potentials = self._compute_membrane_potentials(t)

            

            # 生成脉冲

            spikes = self._fire_neurons(membrane_potentials)

            spike_history.append(spikes)

            

            # 更新突触权重（STDP学习）

            if self.training:

                self._stdp_learning(spike_history)

        

        return spike_history

    

    def _stdp_learning(self, spike_history):

        """脉冲时序依赖可塑性算法"""

        for synapse in self.synapses:

            pre_spikes = spike_history[synapse.pre_neuron]

            post_spikes = spike_history[synapse.post_neuron]

            

            # 计算脉冲时间差

            time_diffs = self._calculate_spike_time_differences(pre_spikes, post_spikes)

            

            # 根据STDP规则更新权重

            weight_update = self._stdp_function(time_diffs)

            synapse.weight += learning_rate * weight_update