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Prefrontal Computation as Active Inference.

Authors
  • Parr, Thomas1
  • Rikhye, Rajeev Vijay2, 3
  • Halassa, Michael M2, 3, 4
  • Friston, Karl J1
  • 1 Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, WC1N 3BG, UK.
  • 2 Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
  • 3 McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
  • 4 Stanley Center for Psychiatric Genetics, Broad Institute, Cambridge, MA 02139, USA.
Type
Published Article
Journal
Cerebral Cortex
Publisher
Oxford University Press
Publication Date
Mar 21, 2020
Volume
30
Issue
2
Pages
682–695
Identifiers
DOI: 10.1093/cercor/bhz118
PMID: 31298270
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

The prefrontal cortex is vital for a range of cognitive processes, including working memory, attention, and decision-making. Notably, its absence impairs the performance of tasks requiring the maintenance of information through a delay period. In this paper, we formulate a rodent task-which requires maintenance of delay-period activity-as a Markov decision process and treat optimal task performance as an (active) inference problem. We simulate the behavior of a Bayes optimal mouse presented with 1 of 2 cues that instructs the selection of concurrent visual and auditory targets on a trial-by-trial basis. Formulating inference as message passing, we reproduce features of neuronal coupling within and between prefrontal regions engaged by this task. We focus on the micro-circuitry that underwrites delay-period activity and relate it to functional specialization within the prefrontal cortex in primates. Finally, we simulate the electrophysiological correlates of inference and demonstrate the consequences of lesions to each part of our in silico prefrontal cortex. In brief, this formulation suggests that recurrent excitatory connections-which support persistent neuronal activity-encode beliefs about transition probabilities over time. We argue that attentional modulation can be understood as the contextualization of sensory input by these persistent beliefs. © The Author(s) 2019. Published by Oxford University Press.

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