A Systematic Review of Spiking Neural Networks and Their Applications

These days, there is increasing curiosity regarding the topic of spiking neural networks (SNNs). Compared to artificial neural networks (ANNs), which are the subsequent equivalents, they bear a greater resemblance to the real neural networks found in the brain. SNNs are based on events such as neuromorphic factors; hardware based on SNNs may be less energy-intensive than ANNs. Since the energy usage would be far lower than that of typical deep learning models housed in the cloud today, this could result in a significant reduction in maintenance costs for neural network models. Such gear is still not readily accessible, however. This chapter presents a Systematic Review of Spiking Neural Networks and Their Applications. This study examines the benefits and drawbacks of various neural model types, coding techniques, methods for learning, and Neuromorphic platforms for computing. Based on these analyses, some anticipated developments are suggested, including balancing biological imitation and computing costs for neuron theories, the process of compounding coding techniques, unsupervised algorithms for learning in SNN, and digital-analog computation systems.