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:神经网络的生物学启发仍然在促进神经网络性能的突破。然而,一个被低估和研究不足的神经计算领域是生物学合理、能效高的脉冲神经网络,其潜力对于低功耗、移动或硬件受限的环境尤其有吸引力。我们提供了一份关于脉冲神经网络最近发展的文献综述,涵盖了解释、优化、效率和准确性方面的关键进展。主要贡献包括确定、讨论和比较在脉冲神经网络优化、能效和评估方面的尖端方法,从基本原理开始讲解,以便新手容易入门。