A LIF-based Legendre Memory Unit as neuromorphic State Space Model benchmarked on a second-long spatio-temporal task

Chasing energy efficiency through biologically inspired computing has produced significant interest in neuromorphic computing as a new approach to overcome some of the limitations of conventional Machine Learning (ML) solutions. In this context, State Space Models (SSMs) are arising as a powerful tool to model the temporal evolution of a system through differential equations. Their established ability to process time-dependent information and their compact mathematical framework make them indeed attractive for integration with neuromorphic principles, leveraging the time-driven basis on which the latter are inherently based. To further explore such integration and co-operation, we investigated the adoption of a neuromorphic SSM, based on the redesign of the Legendre Memory Unit (LMU) through populations of Leaky Integrate-and-Fire (LIF) neurons, for a spatio-temporal task. Our LIF-based LMU (L<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>MU) turned out to outperform recurrent Spiking Neural Networks (SNNs) on the event-based Braille letter reading task, providing additional hints on the feasibility of SSMs as upcoming alternative in the neuromorphic domain.