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Expressive Power of Recurrent Spiking Neural Networks for Sequence Modeling

MCML Authors

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.

inproceedings NDA+26

TSALM @ICLR 2026

Workshop on Time Series in the Age of Large Models at the 14th International Conference on Learning Representations. Rio de Janeiro, Brazil, Apr 23-27, 2026. To be published. Preprint available.

Authors

D. A. Nguyen • M. Datres • E. Araya • G. Kutyniok

Links

URL

Research Area

 A2 | Mathematical Foundations

BibTeXKey: NDA+26

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