Spiking Neural Networks: A Comprehensive Survey of Training Methodologies, Hardware Implementations and Applications

Abstract

Spiking neural networks (SNN) represent a paradigm shift toward discrete, event-driven neural computation that mirrors biological brain mechanisms. This survey systematically examines current SNN research, focusing on training methodologies, hardware implementations, and practical applications. We analyze four major training paradigms: ANN-to-SNN conversion, direct gradient-based training, spike-timing-dependent plasticity (STDP), and hybrid approaches. Our review encompasses major specialized hardware platforms: Intel Loihi, IBM TrueNorth, SpiNNaker, and BrainScaleS, analyzing their capabilities and constraints. We survey applications spanning computer vision, robotics, edge computing, and brain-computer interfaces, identifying where SNN provide compelling advantages. Our comparative analysis reveals SNN offer significant energy efficiency improvements (1 000–10 000× reduction) and natural temporal processing, while facing challenges in scalability and training complexity. We identify critical research directions including improved gradient estimation, standardized benchmarking protocols, and hardware-software co-design approaches. This survey provides researchers and practitioners with a comprehensive understanding of current SNN capabilities, limitations, and future prospects.