Drift of neural ensembles driven by slow fluctuations of intrinsic excitability
File(s)
Published version
Author(s)
Delamare, Geoffroy
Zaki, Yosif
Cai, Denise J
Clopath, Claudia
Type
Journal Article
Abstract
Representational drift refers to the dynamic nature of neural representations in the brain despite
the behavior being seemingly stable. Although drift has been observed in many different brain regions, the mechanisms underlying it are not known. Since intrinsic neural excitability is suggested to
play a key role in regulating memory allocation, fluctuations of excitability could bias the reactivation of previously stored memory ensembles and therefore act as a motor for drift. Here, we propose
a rate-based plastic recurrent neural network with slow fluctuations of intrinsic excitability. We first
show that subsequent reactivations of a neural ensemble can lead to drift of this ensemble. The
model predicts that drift is induced by co-activation of previously active neurons along with neurons
with high excitability which leads to remodelling of the recurrent weights. Consistent with previous
experimental works, the drifting ensemble is informative about its temporal history. Crucially, we
show that the gradual nature of the drift is necessary for decoding temporal information from the
activity of the ensemble. Finally, we show that the memory is preserved and can be decoded by an
output neuron having plastic synapses with the main region.
the behavior being seemingly stable. Although drift has been observed in many different brain regions, the mechanisms underlying it are not known. Since intrinsic neural excitability is suggested to
play a key role in regulating memory allocation, fluctuations of excitability could bias the reactivation of previously stored memory ensembles and therefore act as a motor for drift. Here, we propose
a rate-based plastic recurrent neural network with slow fluctuations of intrinsic excitability. We first
show that subsequent reactivations of a neural ensemble can lead to drift of this ensemble. The
model predicts that drift is induced by co-activation of previously active neurons along with neurons
with high excitability which leads to remodelling of the recurrent weights. Consistent with previous
experimental works, the drifting ensemble is informative about its temporal history. Crucially, we
show that the gradual nature of the drift is necessary for decoding temporal information from the
activity of the ensemble. Finally, we show that the memory is preserved and can be decoded by an
output neuron having plastic synapses with the main region.
Date Issued
2024-05-07
Date Acceptance
2023-04-07
Citation
eLife, 2024, 12
ISSN
2050-084X
Publisher
eLife Sciences Publications Ltd
Journal / Book Title
eLife
Volume
12
Copyright Statement
Copyright © 2023, Delamare et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.(https://creativecommons.org/licenses/by/4.0/).
License URL
Publication Status
Published
Article Number
RP8805
Date Publish Online
2024-12-15