Spiking Neural Networks

Spiking Neural Networks (SNNs) are third-generation neural models that mimic biological neurons, communicating via discrete, temporal spikes for superior energy efficiency and low-latency processing.

SNNs are the third generation of neural models, fundamentally shifting from continuous-value transmission to event-driven, asynchronous computation. Neurons fire a discrete 'spike' only when their internal membrane potential (often modeled by the Leaky Integrate-and-Fire equation) crosses a specific threshold. This sparse communication directly mirrors biological processes, delivering significant gains: SNNs are highly efficient, making them ideal for edge computing and real-time robotics applications. This architecture is the foundation for specialized neuromorphic hardware (e.g., Intel's Loihi, IBM's TrueNorth), targeting energy consumption reductions of 1000x over traditional deep learning on specific temporal tasks.

https://en.wikipedia.org/wiki/Spiking_neural_network