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Medial prefrontal cortex population activity is plastic irrespective of learning

View ORCID ProfileAbhinav Singh, Adrien Peyrache, View ORCID ProfileMark D. Humphries
doi: https://doi.org/10.1101/027102
Abhinav Singh
1Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PT, UK
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Adrien Peyrache
2Montreal Neurological Institute, McGill University, Montreal, QC H3A 1A1, Canada
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Mark D. Humphries
1Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PT, UK
3School of Psychology, University of Nottingham, Nottingham, NG7 2RD, UK
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  • For correspondence: mark.humphries@nottingham.ac.uk
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Abstract

The prefrontal cortex is thought to learn the relationships between actions and their outcomes. But little is known about what changes to population activity in prefrontal cortex are specific to learning these relationships. Here we characterise the plasticity of population activity in the medial prefrontal cortex of male rats learning rules on a Y-maze. First, we show that the population always changes its patterns of joint activity between the periods of sleep either side of a training session on the maze, irrespective of successful rule learning during training. Next, by comparing the structure of population activity in sleep and training, we show that this population plasticity differs between learning and non-learning sessions. In learning sessions, the changes in population activity in posttraining sleep incorporate the changes to the population activity during training on the maze. In non-learning sessions, the changes in sleep and training are unrelated. Finally, we show evidence that the non-learning and learning forms of population plasticity are driven by different neuron-level changes, with the non-learning form entirely accounted for by independent changes to the excitability of individual neurons, and the learning form also including changes to firing rate couplings between neurons. Collectively, our results suggest two different forms of population plasticity in prefrontal cortex during the learning of action-outcome relationships, one a persistent change in population activity structure decoupled from overt rule-learning, the other a directional change driven by feedback during behaviour.

Significance statement The prefrontal cortex is thought to represent our knowledge about what action is worth doing in which context. But we do not know how the activity of neurons in prefrontal cortex collectively changes when learning which actions are relevant. Here we show in a trial-and-error task that population activity in prefrontal cortex is persistently changing, irrespective of learning. Only during episodes of clear learning of relevant actions are the accompanying changes to population activity carried forward into sleep, suggesting a long-lasting form of neural plasticity. Our results suggest that representations of relevant actions in prefrontal cortex are acquired by reward imposing a direction onto ongoing population plasticity.

Acknowledgments

We thank Silvia Maggi and Rasmus Petersen for comments on early drafts of this manuscript, and the Humphries lab (Javier Caballero, Mat Evans) for discussions. A.S. and M.D.H were supported by a Medical Research Council Senior non-Clinical Fellowship award MR/J008648/1 to M.D.H, and Medical Research Council Grant MR/P005659/1. A.P. was supported by a Canada Research Chair Tier 2 (154808). The original data were obtained through funding from the EU Framework 6 “ICEA” project.

Footnotes

  • Conflict of interest The authors declare no conflicts of interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted January 09, 2019.
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Medial prefrontal cortex population activity is plastic irrespective of learning
Abhinav Singh, Adrien Peyrache, Mark D. Humphries
bioRxiv 027102; doi: https://doi.org/10.1101/027102
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Medial prefrontal cortex population activity is plastic irrespective of learning
Abhinav Singh, Adrien Peyrache, Mark D. Humphries
bioRxiv 027102; doi: https://doi.org/10.1101/027102

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