PT  - JOURNAL ARTICLE
AU  - Abhinav Singh
AU  - Adrien Peyrache
AU  - Mark Humphries
TI  - Signatures of internal model updating during learning in rat prefrontal cortex
AID  - 10.1101/027102
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 027102
4099  - http://biorxiv.org/content/early/2016/03/24/027102.short
4100  - http://biorxiv.org/content/early/2016/03/24/027102.full
AB  - Perception and decision rely on internal models of the world. In primary sensory areas of cortex, joint activity of neural populations likely represents the statistical structure of the external environment. Unknown is whether internal neural models are a general computational principle of cortex, extending to learning, higher-order cortices, and actions. We tested that generality by examining changes of population activity patterns from rat prefrontal cortex in the course of learning rules in a maze. The statistical distributions of activity patterns converged between waking and slow-wave sleep during rule learning. Changes were greatest for patterns predicting correct choice and expressed at the choice point. Together, our results are consistent with the convergence over learning of inferred (in waking) and prior (in sleep) expectations of a task derived from an internal model. The construction and use of an internal model may be a recurring theme of cortical computation.HighlightsStatistical distributions of activity patterns in PFC change over learningReflecting a convergence between task-evoked and sleep distributionsUpdated patterns predict task outcome and occur at a choice pointSuggesting PFC builds and uses an internal model based on samplesHighlightseTOC Blurb: Singh et al show that changes to the statistical distribution of population activity in rat prefrontal cortex are consistent with the learning and use of an internal model for task behaviour.