Optimal plasticity for memory maintenance during ongoing synaptic change

Abstract
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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

Dhruva Raman

University of Cambridge, Cambridge, United Kingdom

For correspondence
dvr23@cam.ac.uk

Competing interests
No competing interests declared.

"This ORCID iD identifies the author of this article:" 0000-0002-8992-1353
Timothy O'Leary

University of Cambridge, Cambridge, United Kingdom

For correspondence
tso24@cam.ac.uk

Competing interests
Timothy O'Leary, Reviewing editor, eLife.

"This ORCID iD identifies the author of this article:" 0000-0002-1029-0158

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.

Copyright

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.