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

#machinelearning #python #snn

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()

Please note that you can always change the parameters based on the type of network you are building and you can always change the learning rules too. Please run this, make changes that suit your network and let me know what you have done.
Happy coding.