Spiking Neural Networks (SNNs) are third-generation neural networks that mimic biological neurons more closely than standard formulations — communicating via discrete binary spikes in time rather than continuous numerical values, enabling extreme energy efficiency.

What Is an SNN?

• Neuron Model: Leaky Integrate-and-Fire (LIF). Membrane potential accumulates charge; when it hits threshold, it "spikes" and resets.
• Signal: Binary ($0$ or $1$) but carries information in the timing (rate coding or temporal coding).
• Hardware: Ideally suited for Neuromorphic chips (Loihi) which are event-driven.

Why They Matter

• Energy: Sparse binary spikes mean expensive multiplications are replaced by cheap additions (or no op if 0).
• Efficiency: Can be 100-1000x more energy efficient than ANNs for certain temporal tasks.
• Training: Traditionally hard to train (non-differentiable spike), but Surrogate Gradient methods (SuperSpike) have solved this recently.

Spiking Neural Networks are silicon brains — bringing the temporal dynamics and sparsity of biology into artificial intelligence algorithms.