An Efficient and Perceptually Motivated Auditory Neural Encoding and Decoding Algorithm for Spiking Neural Networks.

Malu Zhang,Yansong Chua,Jibin Wu,Zihan Pan,Eliathamby Ambikairajah,Haizhou Li

doi:10.3389/fnins.2019.01420

Abstract

The auditory front-end is an integral part of a spiking neural network (SNN) when performing auditory cognitive tasks. It encodes the temporal dynamic stimulus, such as speech and audio, into an efficient, effective and reconstructable spike pattern to facilitate the subsequent processing. However, most of the auditory front-ends in current studies have not made use of recent findings in psychoacoustics and physiology concerning human listening. In this paper, we propose a neural encoding and decoding scheme that is optimized for audio processing. The neural encoding scheme, that we call Biologically plausible Auditory Encoding (BAE), emulates the functions of the perceptual components of the human auditory system, that include the cochlear filter bank, the inner hair cells, auditory masking effects from psychoacoustic models, and the spike neural encoding by the auditory nerve. We evaluate the perceptual quality of the BAE scheme using PESQ; the performance of the BAE based on sound classification and speech recognition experiments. Finally, we also built and published two spike-version of speech datasets: the Spike-TIDIGITS and the Spike-TIMIT, for researchers to use and benchmarking of future SNN research.

Highlights

The temporal or rate based Spiking Neural Networks (SNN), supported by stronger biological evidence than the conventional artificial neural networks (ANN), represents a promising research direction
Since our goal is to apply the masking effects in the precise timing neural code, we propose the strategy as follows: 1. The spike pattern PK×N pij is generated from the raw spectrogram SK×N sij without masking effects, by some temporal neural coding methods, which will be discussed in section “Neural Spike Encoding” Here the index i, j refers to the time-frequency bin in the spectrogram, with i referring to the frequency bin, and j referring to the time frame index
Time-Domain Cochlear Filter Bank Adopting an event-based approach to emulate the human auditory system, we propose a neuronal implementation of the event-driven cochlear filter bank, of which the computation can be parallelized as follows

Summary

Introduction

The temporal or rate based Spiking Neural Networks (SNN), supported by stronger biological evidence than the conventional artificial neural networks (ANN), represents a promising research direction. In the domain of rate-coding, we studied the computational efficiency of SNN (Pan et al, 2019). Further evidence has supported the theory of temporal coding with spike times. To learn a temporal spike pattern, a number of learning rules have been proposed, which include the single-spike Tempotron (Gütig and Sompolinsky, 2006), conductance-based Tempotron (Gütig and Sompolinsky, 2009), the multi-spike learning rule ReSuMe The more recent studies are aggregate-label learning (Gütig, 2016), and a novel probability-based multi-layer SNN learning rule (SLAYER) (Shrestha and Orchard, 2018)

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Discussion

Conclusion