Advancing spatio-temporal processing through adaptation in spiking neural networks

Maximilian Baronig, Romain Ferrand, Silvester Sabathiel and Robert Legenstein ()
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Maximilian Baronig: Graz University of Technology
Romain Ferrand: Graz University of Technology
Silvester Sabathiel: Silicon Austria Labs GmbH
Robert Legenstein: Graz University of Technology

Nature Communications, 2025, vol. 16, issue 1, 1-26

Abstract: Abstract Implementations of spiking neural networks on neuromorphic hardware promise orders of magnitude less power consumption than their non-spiking counterparts. The standard neuron model for spike-based computation on such systems has long been the leaky integrate-and-fire neuron. A computationally light augmentation of this neuron model with an adaptation mechanism has recently been shown to exhibit superior performance on spatio-temporal processing tasks. The root of the superiority of these so-called adaptive leaky integrate-and-fire neurons however is not well understood. In this article, we thoroughly analyze the dynamical, computational, and learning properties of adaptive leaky integrate-and-fire neurons and networks thereof. Our investigation reveals significant challenges related to stability and parameterization when employing the conventional Euler-Forward discretization for this class of models. We report a rigorous theoretical and empirical demonstration that these challenges can be effectively addressed by adopting an alternative discretization approach – the Symplectic Euler method, allowing to improve over state-of-the-art performances on common event-based benchmark datasets. Our further analysis of the computational properties of these networks shows that they are particularly well suited to exploit the spatio-temporal structure of input sequences without any normalization techniques.

Date: 2025
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DOI: 10.1038/s41467-025-60878-z

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