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Neural heterogeneity promotes robust learning

View ORCID ProfileNicolas Perez-Nieves, View ORCID ProfileVincent C. H. Leung, View ORCID ProfilePier Luigi Dragotti, View ORCID ProfileDan F. M. Goodman
doi: https://doi.org/10.1101/2020.12.18.423468
Nicolas Perez-Nieves
1Department of Electrical and Electronic Engineering, Imperial College London, UK
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Vincent C. H. Leung
1Department of Electrical and Electronic Engineering, Imperial College London, UK
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Pier Luigi Dragotti
1Department of Electrical and Electronic Engineering, Imperial College London, UK
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Dan F. M. Goodman
1Department of Electrical and Electronic Engineering, Imperial College London, UK
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  • For correspondence: d.goodman@imperial.ac.uk
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Abstract

The brain has a hugely diverse, heterogeneous structure. By contrast, many functional neural models are homogeneous. We compared the performance of spiking neural networks trained to carry out difficult tasks, with varying degrees of heterogeneity. Introducing heterogeneity in membrane and synapse time constants substantially improved task performance, and made learning more stable and robust across multiple training methods, particularly for tasks with a rich temporal structure. In addition, the distribution of time constants in the trained networks closely matches those observed experimentally. We suggest that the heterogeneity observed in the brain may be more than just the byproduct of noisy processes, but rather may serve an active and important role in allowing animals to learn in changing environments.

Summary Neural heterogeneity is metabolically efficient for learning, and optimal parameter distribution matches experimental data.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Minor tweaks before submission.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted January 12, 2021.
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Neural heterogeneity promotes robust learning
Nicolas Perez-Nieves, Vincent C. H. Leung, Pier Luigi Dragotti, Dan F. M. Goodman
bioRxiv 2020.12.18.423468; doi: https://doi.org/10.1101/2020.12.18.423468
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Neural heterogeneity promotes robust learning
Nicolas Perez-Nieves, Vincent C. H. Leung, Pier Luigi Dragotti, Dan F. M. Goodman
bioRxiv 2020.12.18.423468; doi: https://doi.org/10.1101/2020.12.18.423468

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