ICNCE 2026

Spiking neural networks (SNNs) occupy a unique intersection amid computational neuroscience and neuromorphic engineering: they are both models of biological neural computation and practical candidates for energy-efficient inference on neuromorphic hardware. Yet training them to perform well on real-world tasks has long been a major impediment, owing to the non-differentiable nature of spike generation.
This tutorial provides a self-contained introduction to SNNs and modern methods for training them. We begin with a concise recap of spiking neuron models, focusing on leaky integrate-and-fire neurons, which are widely used in network modeling and provide the mathematical basis for the rest of the session. We then turn to surrogate gradient methods, which resolve the non-differentiability of spikes by substituting a smooth approximation during the backward pass while preserving spike-based dynamics in the forward pass. We discuss both the practical recipe and the theoretical underpinnings.
A recurring practical obstacle when training deep SNNs is initialization: networks initialized naively often operate in saturated or quiescent regimes that impede gradient flow from the outset. We present fluctuation-driven initialization, a principled approach that places networks in a dynamically active, fluctuation-driven regime at the start of training, dramatically improving convergence in both rate- and temporal-coded settings.
Finally, we examine the question of on-chip and biologically plausible learning. We introduce online local learning rules, derived from the surrogate gradient framework, that approximate backpropagation through time while respecting locality constraints compatible with neuromorphic hardware and synaptic plasticity mechanisms observed in the brain.
Throughout, theory is motivated by practical implementation considerations, and connections to neuromorphic hardware deployment are highlighted. Attendees will leave with a coherent picture of the state of the art in SNN training and the tools needed to apply these methods in their own research.