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Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits

Alexandre Payeur, View ORCID ProfileJordan Guerguiev, Friedemann Zenke, View ORCID ProfileBlake A. Richards, View ORCID ProfileRichard Naud
doi: https://doi.org/10.1101/2020.03.30.015511
Alexandre Payeur
1uOttawa Brain and Mind Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
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Jordan Guerguiev
2Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, Canada
3Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada
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Friedemann Zenke
4Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
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Blake A. Richards
5Mila, Montréal, QC, Canada
6Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada
7School of Computer Science, McGill University, Montréal, QC, Canada
8Learning in Machines and Brains Program, Canadian Institute for Advanced Research, Toronto, ON, Canada
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  • For correspondence: rnaud@uottawa.ca blake.richards@mcgill.ca
Richard Naud
1uOttawa Brain and Mind Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
9Department of Physics, University of Ottawa, Ottawa, ON, Canada
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  • For correspondence: rnaud@uottawa.ca blake.richards@mcgill.ca
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Abstract

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well-established that it depends on pre and postsynaptic activity. However, models that rely solely on pre and postsynaptic activity for synaptic changes have, to date, not been able to account for learning complex tasks that demand credit assignment in hierarchical networks. Here, we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then neurons higher in a hierarchical circuit can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when paired with short-term synaptic dynamics, regenerative activity in the apical dendrites, and synaptic plasticity in feedback pathways, a burst-dependent learning rule can solve challenging tasks that require deep network architectures. Our results demonstrate that well-known properties of dendrites, synapses, and synaptic plasticity are sufficient to enable sophisticated learning in hierarchical circuits.

Competing Interest Statement

A provisional patent based on the results reported has been filed.

Footnotes

  • Changes to text and some of the figures.

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-NC-ND 4.0 International license.
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Posted September 14, 2020.
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Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits
Alexandre Payeur, Jordan Guerguiev, Friedemann Zenke, Blake A. Richards, Richard Naud
bioRxiv 2020.03.30.015511; doi: https://doi.org/10.1101/2020.03.30.015511
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Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits
Alexandre Payeur, Jordan Guerguiev, Friedemann Zenke, Blake A. Richards, Richard Naud
bioRxiv 2020.03.30.015511; doi: https://doi.org/10.1101/2020.03.30.015511

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