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Neuromorphic computing holds the promise to achieve the energy efficiency androbust learning performance of biological neural systems. To realize thepromised brain-like intelligence, it needs to solve the challenges of theneuromorphic hardware architecture design of biological neural substrate andthe hardware amicable algorithms with spike-based encoding and learning. Herewe introduce a neural spike coding model termed spiketrum, to characterize andtransform the time-varying analog signals, typically auditory signals, intocomputationally efficient spatiotemporal spike patterns. It minimizes theinformation loss occurring at the analog-to-spike transformation and possessesinformational robustness to neural fluctuations and spike losses. The modelprovides a sparse and efficient coding scheme with precisely controllable spikerate that facilitates training of spiking neural networks in various auditoryperception tasks. We further investigate the algorithm-hardware co-designsthrough a neuromorphic cochlear prototype which demonstrates that our approachcan provide a systematic solution for spike-based artificial intelligence byfully exploiting its advantages with spike-based computation.

Neuromorphic Auditory Perception by Neural Spiketrum