SNNs

Spiking neural networks (SNNs) are a class of artificial neural networks in which neurons communicate by discrete events called spikes. Time and the precise timing of spikes play a central role in computation, unlike traditional rate-based neural networks that rely on average activation. SNNs draw inspiration from biological neural systems, where neurons emit spikes only when their membrane potential crosses a threshold.

In SNNs, neurons are typically modeled with leaky integrate-and-fire or more complex conductance-based models. Inputs are converted into spike trains, and information is encoded either by spike rate or by the precise timing of spikes (temporal coding). In a network, spikes propagate along synapses that implement synaptic weights, and learning rules adjust those weights based on spike patterns.

Training SNNs poses challenges because the spike function is non-differentiable, complicating gradient-based optimization. Researchers address this with surrogate gradient methods, backpropagation through time, or hybrid approaches that combine spike-timing dependent plasticity (STDP) with supervised learning signals. Unsupervised STDP can extract features, while supervised SNNs use spike-based error propagation or conversion from trained conventional networks.

SNNs have gained prominence with neuromorphic hardware designed for event-driven computation and low power consumption, such as Loihi and TrueNorth. These platforms exploit sparsity and asynchronous processing to achieve energy efficiency, suitable for real-time sensing and embedded applications.

Applications include processing of temporal and event-based data, robotics, audio and vision tasks using event cameras. Challenges remain in training efficiency, standard benchmarking, and integrating SNNs with conventional deep learning workflows. Ongoing research seeks to close the gap between biological plausibility, computational efficiency, and practical performance.