Neuromorphic Auditory Perception by Neural Spiketrum
Huajin Tang Pengjie Gu Jayawan Wijekoon MHD Anas Alsakkal Ziming Wang Jiangrong Shen Rui Yan
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.