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

TL;DR

This paper introduces a scalable, physiologically inspired spiking neural network architecture for speech recognition, demonstrating emergent neural oscillations and cross-frequency couplings during speech processing, with implications for understanding brain dynamics and neuromorphic systems.

Contribution

The study presents a novel end-to-end trainable spiking neural network model that exhibits neural oscillations and cross-frequency couplings during speech recognition, highlighting the role of inhibitory feedback mechanisms.

Findings

Neural oscillations emerge during speech processing but not noise handling.

Cross-frequency couplings are observed within and across layers during speech recognition.

Feedback mechanisms like adaptation and recurrent connections regulate neural synchronisation.

Abstract

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.