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The geometry of robustness in spiking neural networks
Neural systems are remarkably robust against various perturbations, a phenomenon that still requires a clear explanation. Here, we graphically illustrate how neural networks can become robust. We study spiking networks that generate low-dimensional representations, and we show that the neurons’ subt...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9307274/ https://www.ncbi.nlm.nih.gov/pubmed/35635432 http://dx.doi.org/10.7554/eLife.73276 |
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author | Calaim, Nuno Dehmelt, Florian A Gonçalves, Pedro J Machens, Christian K |
author_facet | Calaim, Nuno Dehmelt, Florian A Gonçalves, Pedro J Machens, Christian K |
author_sort | Calaim, Nuno |
collection | PubMed |
description | Neural systems are remarkably robust against various perturbations, a phenomenon that still requires a clear explanation. Here, we graphically illustrate how neural networks can become robust. We study spiking networks that generate low-dimensional representations, and we show that the neurons’ subthreshold voltages are confined to a convex region in a lower-dimensional voltage subspace, which we call a 'bounding box'. Any changes in network parameters (such as number of neurons, dimensionality of inputs, firing thresholds, synaptic weights, or transmission delays) can all be understood as deformations of this bounding box. Using these insights, we show that functionality is preserved as long as perturbations do not destroy the integrity of the bounding box. We suggest that the principles underlying robustness in these networks — low-dimensional representations, heterogeneity of tuning, and precise negative feedback — may be key to understanding the robustness of neural systems at the circuit level. |
format | Online Article Text |
id | pubmed-9307274 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-93072742022-07-23 The geometry of robustness in spiking neural networks Calaim, Nuno Dehmelt, Florian A Gonçalves, Pedro J Machens, Christian K eLife Computational and Systems Biology Neural systems are remarkably robust against various perturbations, a phenomenon that still requires a clear explanation. Here, we graphically illustrate how neural networks can become robust. We study spiking networks that generate low-dimensional representations, and we show that the neurons’ subthreshold voltages are confined to a convex region in a lower-dimensional voltage subspace, which we call a 'bounding box'. Any changes in network parameters (such as number of neurons, dimensionality of inputs, firing thresholds, synaptic weights, or transmission delays) can all be understood as deformations of this bounding box. Using these insights, we show that functionality is preserved as long as perturbations do not destroy the integrity of the bounding box. We suggest that the principles underlying robustness in these networks — low-dimensional representations, heterogeneity of tuning, and precise negative feedback — may be key to understanding the robustness of neural systems at the circuit level. eLife Sciences Publications, Ltd 2022-05-30 /pmc/articles/PMC9307274/ /pubmed/35635432 http://dx.doi.org/10.7554/eLife.73276 Text en © 2022, Calaim, Dehmelt, Gonçalves et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Computational and Systems Biology Calaim, Nuno Dehmelt, Florian A Gonçalves, Pedro J Machens, Christian K The geometry of robustness in spiking neural networks |
title | The geometry of robustness in spiking neural networks |
title_full | The geometry of robustness in spiking neural networks |
title_fullStr | The geometry of robustness in spiking neural networks |
title_full_unstemmed | The geometry of robustness in spiking neural networks |
title_short | The geometry of robustness in spiking neural networks |
title_sort | geometry of robustness in spiking neural networks |
topic | Computational and Systems Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9307274/ https://www.ncbi.nlm.nih.gov/pubmed/35635432 http://dx.doi.org/10.7554/eLife.73276 |
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