Advancing spatio-temporal processing through adaptation in spiking neural networks
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 computationa...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60878-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849238404644995072 |
|---|---|
| author | Maximilian Baronig Romain Ferrand Silvester Sabathiel Robert Legenstein |
| author_facet | Maximilian Baronig Romain Ferrand Silvester Sabathiel Robert Legenstein |
| author_sort | Maximilian Baronig |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bc9cbc81f41345288e4ed3b6009511bf |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-bc9cbc81f41345288e4ed3b6009511bf2025-08-20T04:01:36ZengNature PortfolioNature Communications2041-17232025-07-0116112610.1038/s41467-025-60878-zAdvancing spatio-temporal processing through adaptation in spiking neural networksMaximilian Baronig0Romain Ferrand1Silvester Sabathiel2Robert Legenstein3Institute of Machine Learning and Neural Computation, Graz University of TechnologyInstitute of Machine Learning and Neural Computation, Graz University of TechnologySilicon Austria Labs GmbHInstitute of Machine Learning and Neural Computation, Graz University of TechnologyAbstract 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.https://doi.org/10.1038/s41467-025-60878-z |
| spellingShingle | Maximilian Baronig Romain Ferrand Silvester Sabathiel Robert Legenstein Advancing spatio-temporal processing through adaptation in spiking neural networks Nature Communications |
| title | Advancing spatio-temporal processing through adaptation in spiking neural networks |
| title_full | Advancing spatio-temporal processing through adaptation in spiking neural networks |
| title_fullStr | Advancing spatio-temporal processing through adaptation in spiking neural networks |
| title_full_unstemmed | Advancing spatio-temporal processing through adaptation in spiking neural networks |
| title_short | Advancing spatio-temporal processing through adaptation in spiking neural networks |
| title_sort | advancing spatio temporal processing through adaptation in spiking neural networks |
| url | https://doi.org/10.1038/s41467-025-60878-z |
| work_keys_str_mv | AT maximilianbaronig advancingspatiotemporalprocessingthroughadaptationinspikingneuralnetworks AT romainferrand advancingspatiotemporalprocessingthroughadaptationinspikingneuralnetworks AT silvestersabathiel advancingspatiotemporalprocessingthroughadaptationinspikingneuralnetworks AT robertlegenstein advancingspatiotemporalprocessingthroughadaptationinspikingneuralnetworks |