Self-healing codes: How stable neural populations can track continually reconfiguring neural representations.
View / Open Files
Publication Date
2022-02-15Journal Title
Proc Natl Acad Sci U S A
ISSN
0027-8424
Publisher
Proceedings of the National Academy of Sciences
Volume
119
Issue
7
Language
eng
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Rule, M. E., & O'Leary, T. (2022). Self-healing codes: How stable neural populations can track continually reconfiguring neural representations.. Proc Natl Acad Sci U S A, 119 (7) https://doi.org/10.1073/pnas.2106692119
Abstract
As an adaptive system, the brain must retain a faithful representation of the world while continuously integrating new information. Recent experiments have measured population activity in cortical and hippocampal circuits over many days and found that patterns of neural activity associated with fixed behavioral variables and percepts change dramatically over time. Such "representational drift" raises the question of how malleable population codes can interact coherently with stable long-term representations that are found in other circuits and with relatively rigid topographic mappings of peripheral sensory and motor signals. We explore how known plasticity mechanisms can allow single neurons to reliably read out an evolving population code without external error feedback. We find that interactions between Hebbian learning and single-cell homeostasis can exploit redundancy in a distributed population code to compensate for gradual changes in tuning. Recurrent feedback of partially stabilized readouts could allow a pool of readout cells to further correct inconsistencies introduced by representational drift. This shows how relatively simple, known mechanisms can stabilize neural tuning in the short term and provides a plausible explanation for how plastic neural codes remain integrated with consolidated, long-term representations.
Keywords
Homeostasis, Lifelong Learning, Hebbian Plasticity, Representational Drift, Nerve Net, Neurons, Animals, Neuronal Plasticity, Models, Neurological
Sponsorship
European Research Council (716643)
Identifiers
PMC8851551, 35145024
External DOI: https://doi.org/10.1073/pnas.2106692119
This record's URL: https://www.repository.cam.ac.uk/handle/1810/334994
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Licence URL: https://creativecommons.org/licenses/by-nc-nd/4.0/
Statistics
Total file downloads (since January 2020). For more information on metrics see the
IRUS guide.