Spiking Neural Networks (SNNs): A Path towards Brain-Inspired AI

Abstract

Spiking Neural Networks (SNNs) represent a significant step toward brain-inspired artificial intelligence by mimicking the temporal dynamics and energy efficiency of biological neurons. Unlike traditional artificial neural networks, SNNs process information through discrete spikes, enabling event-driven computation and efficient learning mechanisms. This paradigm shift enhances real-time processing, low-power consumption, and neuromorphic computing applications. With advancements in hardware and training algorithms, SNNs hold great promise for edge computing, robotics, and cognitive modelling. This paper explores the fundamental principles of SNNs, their advantages over conventional deep learning models, and the challenges in developing large-scale, efficient spiking architectures.