Skip to main content

ORIGINAL RESEARCH article

Front. Neurosci.
Sec. Neural Technology
Volume 18 - 2024 | doi: 10.3389/fnins.2024.1449181
This article is part of the Research Topic Brain-Inspired Computing: From Neuroscience to Neuromorphic Electronics for new forms of Artificial Intelligence View all 5 articles

Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks

Provisionally accepted
Alexandre Bittar Alexandre Bittar 1,2*Philip N. Garner Philip N. Garner 1
  • 1 Idiap Research Institute, Martigny, Switzerland
  • 2 Swiss Federal Institute of Technology Lausanne, Lausanne, Vaud, Switzerland

The final, formatted version of the article will be published soon.

    Understanding cognitive processes in the brain demands sophisticated models capable of replicating neural dynamics at large scales. We present a physiologically inspired speech recognition architecture, compatible and scalable with deep learning frameworks, and demonstrate that end-to-end gradient descent training leads to the emergence of neural oscillations in the central spiking neural network. Significant cross-frequency couplings, indicative of these oscillations, are measured within and across network layers during speech processing, whereas no such interactions are observed when handling background noise inputs. Furthermore, our findings highlight the crucial inhibitory role of feedback mechanisms, such as spike frequency adaptation and recurrent connections, in regulating and synchronising neural activity to improve recognition performance. Overall, on top of developing our understanding of synchronisation phenomena notably observed in the human auditory pathway, our architecture exhibits dynamic and efficient information processing, with relevance to neuromorphic technology.

    Keywords: Neural oscillations, spiking neural networks, speech recognition, brain-inspired computing, deep learning, Surrogate gradient, spike-frequency adaptation, neuromorphic computing

    Received: 14 Jun 2024; Accepted: 28 Aug 2024.

    Copyright: © 2024 Bittar and Garner. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Alexandre Bittar, Idiap Research Institute, Martigny, Switzerland

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.