Tagged: PyTorch

Spiking neural networks: teaching a model to think in spikes

I built a leaky integrate-and-fire spiking network in snnTorch that reaches 98% on MNIST using discrete spikes over time instead of continuous activations. Here is how event-driven inference actually works, and why it matters for edge hardware.