Spiking Neural Networks (SNNs) are a class of artificial neural networks inspired by biological neurons and their spiking behavior. Unlike traditional neural networks, which primarily use continuous values for computations, SNNs simulate the discrete spikes (action potentials) observed in biological neurons. These spikes encode information temporally, enabling SNNs to process and transmit data with an event-driven approach rather than through continuous signals. This temporal encoding is believed to offer advantages such as increased efficiency in computation and the ability to process asynchronous inputs naturally. SNNs have applications in areas requiring real-time processing, such as robotics, sensory data analysis, and brain-computer interfaces. Research in SNNs focuses on developing efficient learning algorithms (like Spike-Time-Dependent Plasticity) and understanding how biological neural networks achieve complex cognitive functions using spike-based processing. This book provides comprehensive insights into the field of spiking neural networks. It is a compilation of chapters that discuss the most vital concepts and emerging trends in the field of neural networks. The extensive content of this book provides the readers with a thorough understanding of the subject.