Spiking neuron networks (SNNs) are the computational units of third-generation artificial neural networks (ANNs) that mimic biological neural networks more indistinguishable than ANNs. The most significant characteristics of spiking neural networks are energy efficiency and biological plausibility. SNNs integrate the concept of time for their operating model, which performs in a time-dependent spiking manner. Commonly utilized learning methods for spiking neural networks are categorized as unsupervised learning and supervised learning. A few notable advantages of spiking neural networks over conventional artificial neural networks are brain similarity, computational power, and energy efficiency in event-driven neural processing.

Spike coding methods, spike neural network architectures, and simulation of spiking neural networks are developed for implementing SNN models. Various functions carried out by spiking neural networks are data/pattern classification, estimation, prediction, signal processing, and robotic control applications. In neuroscience, researchers have been investigated simulating brain-scale SNNs to study brain functions. Spike neural networks are applied in different fields of proficiency, and its future scopes will focus on incorporating reinforcement learning in SNNs, also employed in edges devices for the Internet of Things (IoT) and reservoir computing.