Spiking Neural Networks: A Comprehensive Survey of Training Methodologies, Hardware Implementations and Applications
Ameer Hamza Khan, Xinwei Cao, Chunbo Luo, Shiqing Zhang, Wenping Guo, Vasilios N. Katsikis, Shuai Li
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.