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:生物神经网络继续激发神经网络性能上的突破。然而,一个被低估和未被广泛研究的神经计算领域是符合生物学可行性和能源效率的脉冲神经网络,其潜力特别适用于低功率、移动或其他硬件受限制的设置。本文综述了脉冲神经网络在解释、优化、效率和准确性方面的最新发展。关键贡献包括识别、讨论和比较在脉冲神经网络优化、节能和评估方面的尖端方法,从第一原理开始,使其对新从业者具有可访问性。