Deconstructing Spiking Neural Networks (SNNs)
Explore biological neuron models, surrogate-gradient training, pure PyTorch implementations, and energy-efficient benchmarks against standard ANNs.
Part 1
Biology & the LIF Model
From biological action potentials to the Leaky Integrate-and-Fire (LIF) model—and why the Heaviside spike function breaks standard backpropagation.
Part 2
PyTorch Implementation
Building a complete LIF layer with surrogate gradients in pure
PyTorch—no SNN frameworks required.
View Code on GitHub
Part 3
Same Accuracy. Half the Energy.
Benchmarking the SNN against a parameter-identical ANN on MNIST, measuring firing-rate sparsity and computing the 51.7% energy reduction.