Shashank Sharma

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Getting Started with SNNTorch: A Beginner Tutorial

13 February 2026 2 min readsnntorch · spiking neural networks · pytorch · neuromorphic

Spiking Neural Networks (SNNs) are a key component of neuromorphic computing.
Unlike standard neural networks, SNNs communicate using discrete spikes over time, making them more biologically realistic and potentially far more energy efficient.

In this tutorial, we will cover the basics of SNNTorch, one of the most popular PyTorch-based libraries for building SNNs.

Why SNNTorch?

SNNTorch is useful because:

  • It is built directly on PyTorch
  • It supports backpropagation with surrogate gradients
  • It provides ready-to-use spiking neuron models
  • It is beginner-friendly for deep learning researchers

Installation

Install SNNTorch using pip:

pip install snntorch

Core Idea: Neurons Spike Over Time

In a normal neural network:

  • Input → activation → output

In an SNN:

  • Input → membrane potential accumulates
  • When threshold is reached → neuron fires a spike
  • The process repeats over timesteps

Importing SNNTorch

A typical setup looks like:

import torch
import torch.nn as nn
import snntorch as snn
from snntorch import surrogate

The Leaky Integrate-and-Fire (LIF) Neuron

The most common neuron model is the LIF neuron.

It has:

  • membrane potential
  • decay over time
  • threshold-based spike firing

Example:

beta = 0.9  # decay factor

lif_neuron = snn.Leaky(beta=beta)

Building a Simple Spiking Network

Let’s define a simple 2-layer SNN:

class SimpleSNN(nn.Module):
    def __init__(self):
        super().__init__()

        self.fc1 = nn.Linear(784, 256)
        self.lif1 = snn.Leaky(beta=0.95)

        self.fc2 = nn.Linear(256, 10)
        self.lif2 = snn.Leaky(beta=0.95)

    def forward(self, x):

        # Initialize membrane potentials
        mem1 = self.lif1.init_leaky()
        mem2 = self.lif2.init_leaky()

        spk2_rec = []

        # Simulate over time
        for step in range(25):

            cur1 = self.fc1(x)
            spk1, mem1 = self.lif1(cur1, mem1)

            cur2 = self.fc2(spk1)
            spk2, mem2 = self.lif2(cur2, mem2)

            spk2_rec.append(spk2)

        return torch.stack(spk2_rec)

Key Points

Notice:

  • We simulate over timesteps
  • Neurons output spikes (spk)
  • Membrane state (mem) is carried forward

Loss Function for SNNs

Because spikes are non-differentiable, SNNTorch uses surrogate gradients.

Example:

spike_grad = surrogate.fast_sigmoid()

lif = snn.Leaky(beta=0.9, spike_grad=spike_grad)

This allows gradient-based learning.

Training an SNN (High-Level)

Training looks similar to PyTorch:

optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

for data, targets in dataloader:

    spk_out = model(data)

    loss = loss_fn(spk_out.sum(dim=0), targets)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

Where SNNTorch is Used

SNNTorch is widely used for:

  • neuromorphic vision tasks
  • event-based sensors
  • low-power AI research
  • brain-inspired architectures

Conclusion

SNNTorch provides a clean bridge between:

  • deep learning (PyTorch)
  • neuromorphic computing (spikes + time)

If you already know PyTorch, it is one of the best frameworks to start exploring Spiking Neural Networks.

Next Steps

In future posts, I will cover:

  • Training SNNs on MNIST
  • Event-based datasets
  • Deployment on neuromorphic hardware
  • Framework comparisons: Lava vs Rockpool vs SNNTorch

Stay tuned.