Advancing Spatio-Temporal Processing in Spiking Neural Networks through Adaptation

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 (LIF) neuron. A computationally light augmentation of the LIF 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 LIF neurons however is not well understood. In this article, we thoroughly analyze the dynamical, computational, and learning properties of adaptive LIF 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 networks of adaptive LIF neurons shows that they are particularly well suited to exploit the spatio-temporal structure of input sequences without any normalization techniques.

Published in Nature Communications, July 2025

References in corpus (6)

• The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks
• ReckOn: A 28nm Sub-mm2 Task-Agnostic Spiking Recurrent Neural Network Processor Enabling On-Chip Learning over Second-Long Timescales
• Resurrecting Recurrent Neural Networks for Long Sequences
• Advancing Spatio-Temporal Processing in Spiking Neural Networks through Adaptation
• Membrane Potential Batch Normalization for Spiking Neural Networks
• Mutual Information Analysis of Neuromorphic Coding for Distributed Wireless Spiking Neural Networks

Cited by in corpus (4)

• Advancing Spatio-Temporal Processing in Spiking Neural Networks through Adaptation
• End-to-End Keyword Spotting on FPGA Using Graph Neural Networks with a Neuromorphic Auditory Sensor
• Three factor delay learning rules for spiking neural networks
• Zero-Shot Temporal Resolution Domain Adaptation for Spiking Neural Networks