Evolutionary Spiking Neural Networks: A Survey

Abstract

Spiking neural networks (SNNs) are gaining increasing attention as potential computationally efficient alternatives to traditional artificial neural networks(ANNs). However, the unique information propagation mechanisms and the complexity of SNN neuron models pose challenges for adopting traditional methods developed for ANNs to SNNs. These challenges include both weight learning and architecture design. While surrogate gradient learning has shown some success in addressing the former challenge, the latter remains relatively unexplored. Recently, a novel paradigm utilizing evolutionary computation methods has emerged to tackle these challenges. This approach has resulted in the development of a variety of energy-efficient and high-performance SNNs across a wide range of machine learning benchmarks. In this paper, we present a survey of these works and initiate discussions on potential challenges ahead.

References in corpus (10)

• Spikformer: When Spiking Neural Network Meets Transformer
• Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting
• Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks
• Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks
• Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective
• A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration
• Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips
• A Comprehensive Review of Spiking Neural Networks: Interpretation, Optimization, Efficiency, and Best Practices
• Evolving Connectivity for Recurrent Spiking Neural Networks
• A survey on learning models of spiking neural membrane systems and spiking neural networks