Optimal plasticity for memory maintenance during ongoing synaptic change
Abstract
Synaptic connections in many brain circuits fluctuate, exhibiting substantial turnover and remodelling over hours to days. Surprisingly, experiments show that most of this flux in connectivity persists in the absence of learning or known plasticity signals. How can neural circuits retain learned information despite a large proportion of ongoing and potentially disruptive synaptic changes? We address this question from first principles by analysing how much compensatory plasticity would be required to optimally counteract ongoing fluctuations, regardless of whether fluctuations are random or systematic. Remarkably, we find that the answer is largely independent of plasticity mechanisms and circuit architectures: compensatory plasticity should be at most equal in magnitude to fluctuations, and often less, in direct agreement with previously unexplained experimental observations. Moreover, our analysis shows that a high proportion of learning-independent synaptic change is consistent with plasticity mechanisms that accurately compute error gradients.
Data availability
All code is publicly available on github at this URL:https://github.com/Dhruva2/OptimalPlasticityRatios
Article and author information
Author details
Funding
European Commission (StG 2016 716643 FLEXNEURO)
- Dhruva Raman
- Timothy O'Leary
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Srdjan Ostojic, Ecole Normale Superieure Paris, France
Version history
- Preprint posted: August 19, 2020 (view preprint)
- Received: September 8, 2020
- Accepted: September 13, 2021
- Accepted Manuscript published: September 14, 2021 (version 1)
- Version of Record published: October 11, 2021 (version 2)
Copyright
© 2021, Raman & O'Leary
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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