A spiking neural network (SNN) is a type of neural network that simulates the behavior of biological neurons more closely than traditional neural networks. Neurons in SNNs communicate by sending discrete spikes (or events) rather than continuous signals.
SNNs are event-driven, meaning that neurons only "fire" when their input reaches a certain threshold. This mimics the behavior of real neurons and makes SNNs more energy-efficient and biologically realistic.
SNNs are used in neuromorphic computing and are particularly suited for tasks like real-time processing of sensory data, such as vision or auditory recognition.