Sample Code on how to build a ten neuron SNN USING STDP Learning rule

import numpy as np import matplotlib.pyplot as plt # Parameters num_neurons = 10 num_layers = 3 num_timesteps = 100 dt = 0.001 # Neuron model V_rest = -70 V_threshold = -55 V_reset = -75 tau_m = 0.02 tau_ref = 0.002 # Synaptic connections W_ff = np.random.rand(num_neurons, num_neurons) * 0.1 W_fb = np.random.rand(num_neurons, num_neurons) * 0.1 # Spike-Timing Dependent Plasticity (STDP) learning rule tau_stdp = 0.02 A_plus = 0.01 A_minus = -0.01 # Input data X = np.random.rand(num_neurons, num_timesteps) # Initialize variables V = np.zeros((num_neurons, num_timesteps)) V[:, 0] = V_rest spikes = np.zeros((num_neurons, num_timesteps)) # Simulation loop for t in range(1, num_timesteps): # Calculate feedforward current I_ff = np.dot(W_ff, spikes[:, t-1]) # Calculate feedbackward current I_fb = np.dot(W_fb, spikes[:, t-1]) # Calculate total input current I_in = X[:, t] + I_ff + I_fb # Update membrane potential V[:, t] = V[:, t-1] + (-V[:, t-1] + V_rest + I_in * dt / tau_m) / tau_m # Generate spikes spike_indices = np.where(V[:, t] > V_threshold)[0] spikes[spike_indices, t] = 1 V[spike_indices, t] = V_reset # Refractory period refractory_indices = np.where(spikes[:, t-1] == 1)[0] V[refractory_indices, t] = V_rest # STDP learning rule for i in range(num_neurons): for j in range(num_neurons): if spikes[i, t-1] == 1 and spikes[j, t] == 1: delta_w = A_plus * np.exp(-(t - 1 - tau_stdp) / tau_stdp) W_ff[i, j] += delta_w elif spikes[i, t-1] == 0 and spikes[j, t] == 1: delta_w = A_minus * np.exp(-(t - 1 - tau_stdp) / tau_stdp) W_fb[j, i] += delta_w # Plot results plt.figure(figsize=(10, 6)) plt.subplot(2, 1, 1) plt.title("Membrane Potential") plt.plot(V.T) plt.xlabel("Time") plt.ylabel("Voltage (mV)") plt.ylim(V_reset - 10, V_threshold + 10) plt.subplot(2, 1, 2) plt.title("Spikes") plt.plot(spikes.T, ".") plt.xlabel("Time") plt.ylabel("Spike") plt.show()