Neuromorphic Auditory Perception by Neural Spiketrum

• URL: http://arxiv.org/abs/2309.05430v1
• Date: Mon, 11 Sep 2023 13:06:19 GMT
• Title: Neuromorphic Auditory Perception by Neural Spiketrum
• Authors: Huajin Tang, Pengjie Gu, Jayawan Wijekoon, MHD Anas Alsakkal, Ziming Wang, Jiangrong Shen, and Rui Yan
• Abstract summary: We introduce a neural spike coding model called spiketrumtemporal, to transform the time-varying analog signals into efficient spike patterns. 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.
• Score: 27.871072042280712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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