Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks

View ORCID ProfileFriedemann Zenke, View ORCID ProfileTim P. Vogels
doi: https://doi.org/10.1101/2020.06.29.176925
Friedemann Zenke
1Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
2Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Friedemann Zenke
  • For correspondence: fzenke@gmail.com
Tim P. Vogels
1Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Tim P. Vogels
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Brains process information in spiking neural networks. Their intricate connections shape the diverse functions these networks perform. In comparison, the functional capabilities of models of spiking networks are still rudimentary. This shortcoming is mainly due to the lack of insight and practical algorithms to construct the necessary connectivity. Any such algorithm typically attempts to build networks by iteratively reducing the error compared to a desired output. But assigning credit to hidden units in multi-layered spiking networks has remained challenging due to the non-differentiable nonlinearity of spikes. To avoid this issue, one can employ surrogate gradients to discover the required connectivity in spiking network models. However, the choice of a surrogate is not unique, raising the question of how its implementation influences the effectiveness of the method. Here, we use numerical simulations to systematically study how essential design parameters of surrogate gradients impact learning performance on a range of classification problems. We show that surrogate gradient learning is robust to different shapes of underlying surrogate derivatives, but the choice of the derivative’s scale can substantially affect learning performance. When we combine surrogate gradients with a suitable activity regularization technique, robust information processing can be achieved in spiking networks even at the sparse activity limit. Our study provides a systematic account of the remarkable robustness of surrogate gradient learning and serves as a practical guide to model functional spiking neural networks.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* friedemann.zenke{at}fmi.ch

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.
Back to top
PreviousNext
Posted June 29, 2020.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks
Friedemann Zenke, Tim P. Vogels
bioRxiv 2020.06.29.176925; doi: https://doi.org/10.1101/2020.06.29.176925
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks
Friedemann Zenke, Tim P. Vogels
bioRxiv 2020.06.29.176925; doi: https://doi.org/10.1101/2020.06.29.176925

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (3691)
  • Biochemistry (7800)
  • Bioengineering (5678)
  • Bioinformatics (21295)
  • Biophysics (10582)
  • Cancer Biology (8179)
  • Cell Biology (11946)
  • Clinical Trials (138)
  • Developmental Biology (6764)
  • Ecology (10401)
  • Epidemiology (2065)
  • Evolutionary Biology (13874)
  • Genetics (9709)
  • Genomics (13074)
  • Immunology (8150)
  • Microbiology (20020)
  • Molecular Biology (7859)
  • Neuroscience (43070)
  • Paleontology (321)
  • Pathology (1279)
  • Pharmacology and Toxicology (2260)
  • Physiology (3353)
  • Plant Biology (7232)
  • Scientific Communication and Education (1313)
  • Synthetic Biology (2008)
  • Systems Biology (5539)
  • Zoology (1128)