Theta-modulation drives the emergence of connectivity patterns underlying replay in a network model of place cells

Abstract
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Abstract

Place cells of the rodent hippocampus fire action potentials when the animal traverses a particular spatial location in any environment. Therefore for any given trajectory one observes a repeatable sequence of place cell activations. When the animal is quiescent or sleeping, one can observe similar sequences of activation known as replay, which underlie the process of memory consolidation. However, it remains unclear how replay is generated. Here we show how a temporally asymmetric plasticity rule during spatial exploration gives rise to spontaneous replay in a model network by shaping the recurrent connectivity to reflect the topology of the learned environment. Crucially, the rate of this encoding is strongly modulated by ongoing rhythms. Oscillations in the theta range optimize learning by generating repeated pre-post pairings on a time-scale commensurate with the window for plasticity, while lower and higher frequencies generate learning rates which are lower by orders of magnitude.

Data availability

All electrophysiological data has been uploaded to the Dryad website. The DOI is doi:10.5061/dryad.n9c1rb0

The following data sets were generated

1. Theodoni P
2. Rovira B
3. Wang Y
4. Roxin A
(2018) Data from: Theta-modulation drives the emergence of connectivity patterns underlying replay in a network model of place cells
Available at Dryad Digital Repository under a CC0 Public Domain Dedication.

https://doi.org/10.5061/dryad.n9c1rb0

Article and author information

Author details

Panagiota Theodoni

Computational Neuroscience, Centre de Recerca Matemàtica, Bellaterra, Spain

Competing interests
The authors declare that no competing interests exist.
Bernat Rovira

Computational Neuroscience, Centre de Recerca Matemàtica, Bellaterra, Spain

Competing interests
The authors declare that no competing interests exist.
Yingxue Wang

Max Planck Florida Institute for Neuroscience, Jupiter, United States

Competing interests
The authors declare that no competing interests exist.
Alex Roxin

Computational Neuroscience, Centre de Recerca Matemàtica, Bellaterra, Spain

For correspondence
aroxin@crm.cat

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-1015-8138

Funding

Ministerio de Economía y Competitividad (BFU-2012-33413)

Alex Roxin

Ministerio de Economía y Competitividad (MTM-2015-71509)

Alex Roxin

Generalitat de Catalunya (CERCA program)

Alex Roxin

Howard Hughes Medical Institute

Yingxue Wang

Max-Planck-Gesellschaft

Yingxue Wang

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.