Neuromorphic computing holds the promise to achieve the energy efficiency and robust learning performance of biological neural systems. To realize the promised brain-like intelligence, it needs to solve the challenges of the neuromorphic hardware architecture design of biological neural substrate and the hardware amicable algorithms with spike-based encoding and learning. Here we introduce a neural spike coding model termed spiketrum, to characterize and transform the time-varying analog signals, typically auditory signals, into computationally efficient spatiotemporal spike patterns. It minimizes the information loss occurring at the analog-to-spike transformation and possesses informational robustness to neural fluctuations and spike losses. The model provides a sparse and efficient coding scheme with precisely controllable spike rate that facilitates training of spiking neural networks in various auditory perception tasks. We further investigate the algorithm-hardware co-designs through a neuromorphic cochlear prototype which demonstrates that our approach can provide a systematic solution for spike-based artificial intelligence by fully exploiting its advantages with spike-based computation.

翻译：神经形态计算有望实现生物神经系统的能效优势和鲁棒学习性能。为达成这一脑启发智能目标，需要解决生物神经基底结构下的神经形态硬件架构设计难题，以及基于脉冲编码与学习的硬件友好型算法挑战。本文提出一种名为"spiketrum"的神经脉冲编码模型，用于表征并转换时变模拟信号（典型如听觉信号）为计算高效的时空脉冲模式。该模型在模拟-脉冲转换过程中最小化信息损失，并对神经波动与脉冲丢失具有信息鲁棒性。通过精确可控的脉冲率提供稀疏高效的编码方案，可促进脉冲神经网络在各种听觉感知任务中的训练。我们进一步通过神经形态耳蜗原型探索算法-硬件协同设计，证明该方法能够通过充分发挥脉冲计算的先天优势，为基于脉冲的人工智能提供系统性解决方案。