Spiking Neural Networks

Spiking Neural Networks (SNNs) are a type of artificial neural network that more closely mimic the way biological neurons communicate. Unlike traditional neural networks that use continuous values, SNNs transmit information through discrete spikes, which occur at specific times. This temporal aspect allows SNNs to process information more efficiently and potentially consume less energy. Common use cases include real-time processing of sensory data, robotics, and neuromorphic computing, where hardware is designed to emulate the neural structure of the brain. SNNs are particularly suited for tasks requiring temporal precision, such as speech recognition and event-based vision systems.