Tutorial 07: Spiking Neurons and Activity

Goal: run a spiking workflow with both high-level and module-level APIs.

Step 1: High-level SNNCompute path

import numpy as np
import grilly

snn = grilly.SNNCompute(n_neurons=512)
embedding = np.random.randn(512).astype(np.float32)
summary = snn.process(embedding)

print("spike_activity:", summary["spike_activity"])
print("firing_rate:", summary["firing_rate"])

Step 2: Module-level LIF neuron

import grilly.nn as nn

lif = nn.LIFNeuron(n_neurons=256, dt=0.001, tau_mem=20.0, v_thresh=1.0)
current = np.random.randn(256).astype(np.float32)
spikes = lif(current)
print("spikes shape:", spikes.shape)

Step 3: Run a small time simulation

rates = []
lif.reset()
for _ in range(50):
    s = lif(current)
    rates.append(float(s.mean()))
print("mean firing:", sum(rates) / len(rates))

Step 4: Add synaptic learning layers

The same grilly.nn namespace includes:

HebbianLayer
STDPLayer
GIFNeuron

These can be integrated after you validate baseline LIF dynamics.