The geometry of robustness in spiking neural networks
- Champalimaud Research, Portugal
- University of Tübingen, Germany
Abstract
Neural systems are remarkably robust against various perturbations, a phenomenon that still requires a clear explanation. Here, we graphically illustrate howneural 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, synapticweights, or transmission delays) can all be understood as deformations of this bounding box. Using these insights, we showthat 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.
Data availability
The current manuscript is a computational study, so no data have been generated for this manuscript. Modelling code is uploaded on https://github.com/machenslab/boundingbox
Article and author information
Author details
Florian Alexander Dehmelt
Christian K Machens
Funding
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2022, Calaim et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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The geometry of robustness in spiking neural networks
eLife 11:e73276.
https://doi.org/10.7554/eLife.73276
