Biological neural networks continue to inspire breakthroughs in neural network performance. And yet, one key area of neural computation that has been under-appreciated and under-investigated is biologically plausible, energy-efficient spiking neural networks, whose potential is especially attractive for low-power, mobile, or otherwise hardware-constrained settings. We present a literature review of recent developments in the interpretation, optimization, efficiency, and accuracy of spiking neural networks. Key contributions include identification, discussion, and comparison of cutting-edge methods in spiking neural network optimization, energy-efficiency, and evaluation, starting from first principles so as to be accessible to new practitioners.

翻译：生物神经网络持续为神经网络性能突破提供灵感。然而，生物可解释且能效高的脉冲神经网络作为神经计算领域的关键方向，其潜力在低功耗、移动设备或硬件受限场景中尤为突出，但长期以来未得到充分重视与深入研究。本文围绕脉冲神经网络的解释、优化、效率及准确性等最新进展进行文献综述。核心贡献包括：从基本原理出发，识别、讨论并比较脉冲神经网络优化、能效评估及性能评价领域的前沿方法，力求为初学者提供可理解的入门指引。