Neuromorphic Auditory Perception by Neural Spiketrum

TL;DR

This paper introduces the spiketrum model for transforming auditory signals into efficient spike patterns, enhancing neuromorphic auditory perception with robust, sparse coding and demonstrating its hardware implementation in a cochlear prototype.

Contribution

It presents a novel spike coding model that minimizes information loss and supports robust, efficient auditory perception in neuromorphic systems, along with a prototype hardware demonstration.

Findings

Spiketrum effectively encodes auditory signals with minimal information loss.

The model provides controllable spike rates for efficient neural network training.

Hardware prototype demonstrates practical neuromorphic auditory processing.

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.