Adaptive Axonal Delays in feedforward spiking neural networks for accurate spoken word recognition

2023/02/16 by Pengfei Sun, Ehsan Eqlimi, Sun, Pengfei +7 · 3 citations
Engineering · Neuroscience · #Advanced Memory and Neural Computing #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Ferroelectric and Negative Capacitance Devices #Neural and Evolutionary Computing (cs.NE) #Neural dynamics and brain function #Sound (cs.SD) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2302.08607

openalex publication_date 2023/02/16 · openalex created_date 2023/02/22 · openalex updated_date 2026/07/28

Abstract

Spiking neural networks (SNN) are a promising research avenue for building accurate and efficient automatic speech recognition systems. Recent advances in audio-to-spike encoding and training algorithms enable SNN to be applied in practical tasks. Biologically-inspired SNN communicates using sparse asynchronous events. Therefore, spike-timing is critical to SNN performance. In this aspect, most works focus on training synaptic weights and few have considered delays in event transmission, namely axonal delay. In this work, we consider a learnable axonal delay capped at a maximum value, which can be adapted according to the axonal delay distribution in each network layer. We show that our proposed method achieves the best classification results reported on the SHD dataset (92.45%) and NTIDIGITS dataset (95.09%). Our work illustrates the potential of training axonal delays for tasks with complex temporal structures.

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• Batch Normalization-Free Fully Integer Quantized Neural Networks via Progressive Tandem Learning
• Parallel Delayed Memory Units for Enhanced Temporal Modeling in Biomedical and Bioacoustic Signal Analysis
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