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

Cerebellar learning using perturbations

Guy Bouvier, Johnatan Aljadeff, Claudia Clopath, Célian Bimbard, Jonas Ranft, Antonin Blot, Jean-Pierre Nadal, Nicolas Brunel, Vincent Hakim, View ORCID ProfileBoris Barbour
doi: https://doi.org/10.1101/053785
Guy Bouvier
1Institut de biologie de l’Ecole normale supérieure (IBENS), Ecole normale supérieure, CNRS, INSERM, PSL University, Paris, France.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Johnatan Aljadeff
2Departments of Statistics and Neurobiology, University of Chicago, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Claudia Clopath
3Department of Bioengineering, Imperial College London, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Célian Bimbard
1Institut de biologie de l’Ecole normale supérieure (IBENS), Ecole normale supérieure, CNRS, INSERM, PSL University, Paris, France.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jonas Ranft
1Institut de biologie de l’Ecole normale supérieure (IBENS), Ecole normale supérieure, CNRS, INSERM, PSL University, Paris, France.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Antonin Blot
1Institut de biologie de l’Ecole normale supérieure (IBENS), Ecole normale supérieure, CNRS, INSERM, PSL University, Paris, France.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jean-Pierre Nadal
4Laboratoire de Physique Statistique, Ecole normale supérieure, CNRS, PSL University, Sorbonne Université, Paris, France.
5Centre d’Analyse et de Mathématique Sociales, EHESS, CNRS, PSL University, France.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nicolas Brunel
2Departments of Statistics and Neurobiology, University of Chicago, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Vincent Hakim
4Laboratoire de Physique Statistique, Ecole normale supérieure, CNRS, PSL University, Sorbonne Université, Paris, France.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Boris Barbour
1Institut de biologie de l’Ecole normale supérieure (IBENS), Ecole normale supérieure, CNRS, INSERM, PSL University, Paris, France.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Boris Barbour
  • For correspondence: boris.barbour@ens.fr
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

The cerebellum aids the learning and execution of fast coordinated movements, with acquired information being stored by plasticity of parallel fibre—Purkinje cell synapses. According to the current consensus, erroneously active parallel fibre synapses are depressed by complex spikes arising when climbing fibres signal movement errors. However, this theory cannot solve the credit assignment problem of using the limited information from a global movement evaluation to optimise behaviour by guiding the plasticity in numerous neurones. We identify the possible implementation of an algorithm solving this problem, whereby spontaneous complex spikes perturb ongoing movements, create an eligibility trace for plasticity and signal resulting error changes to guide plasticity. These error changes are extracted by adaptively cancelling the average error. This framework, stochastic gradient descent with estimated global errors, generates specific predictions for synaptic plasticity rules that contradict the current consensus. However, in vitro plasticity experiments under physiological conditions verified our predictions, highlighting the sensitivity of plasticity studies to unphysiological conditions. Using numerical and analytical approaches we demonstrate the convergence and estimate the capacity of learning in our implementation. Finally, a similar mechanism may operate during optimisation of action sequences by the basal ganglia, where dopamine could both initiate movements and signal rewards, analogously to the dual perturbation and correction role of the climbing fibre outlined here.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted July 25, 2018.
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.
Cerebellar learning using perturbations
(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
Cerebellar learning using perturbations
Guy Bouvier, Johnatan Aljadeff, Claudia Clopath, Célian Bimbard, Jonas Ranft, Antonin Blot, Jean-Pierre Nadal, Nicolas Brunel, Vincent Hakim, Boris Barbour
bioRxiv 053785; doi: https://doi.org/10.1101/053785
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Cerebellar learning using perturbations
Guy Bouvier, Johnatan Aljadeff, Claudia Clopath, Célian Bimbard, Jonas Ranft, Antonin Blot, Jean-Pierre Nadal, Nicolas Brunel, Vincent Hakim, Boris Barbour
bioRxiv 053785; doi: https://doi.org/10.1101/053785

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 (4860)
  • Biochemistry (10810)
  • Bioengineering (8053)
  • Bioinformatics (27345)
  • Biophysics (13998)
  • Cancer Biology (11137)
  • Cell Biology (16077)
  • Clinical Trials (138)
  • Developmental Biology (8793)
  • Ecology (13307)
  • Epidemiology (2067)
  • Evolutionary Biology (17374)
  • Genetics (11692)
  • Genomics (15938)
  • Immunology (11042)
  • Microbiology (26127)
  • Molecular Biology (10667)
  • Neuroscience (56654)
  • Paleontology (420)
  • Pathology (1737)
  • Pharmacology and Toxicology (3009)
  • Physiology (4555)
  • Plant Biology (9647)
  • Scientific Communication and Education (1617)
  • Synthetic Biology (2693)
  • Systems Biology (6985)
  • Zoology (1511)