Expressive Power of Recurrent Spiking Neural Networks for Sequence Modeling

Duc Anh Nguyen, Massimiliano Datres, Ernesto Araya, Gitta Kutyniok

Keywords: spiking neural networks, neuromorphic computing, expressivity, theory

TL;DR: We study the expressivity of recurrent spiking neural networks with sequential data

Abstract: Spiking neural networks (SNNs) provide a biologically inspired and potentially efficient framework for processing sequential data, but their expressive power, particularly in the dynamical setting, remains poorly understood. We establish a universality result showing that recurrent SNNs can approximate a broad class of sequence-to-sequence mappings, thereby providing theoretical support for their use in temporal learning and time series processing.

Track: Research Track (max 4 pages)

Submission Number: 99