Shifts in attention drive context-dependent subspace encoding in anterior cingulate cortex in mice during decision making

Márton Albert Hajnal (), Duy Tran, Zsombor Szabó, Andrea Albert, Karen Safaryan, Michael Einstein, Mauricio Vallejo Martelo, Pierre-Olivier Polack, Peyman Golshani () and Gergő Orbán ()
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Márton Albert Hajnal: HUN-REN Wigner Research Centre for Physics
Duy Tran: University of California, Los Angeles
Zsombor Szabó: HUN-REN Wigner Research Centre for Physics
Andrea Albert: HUN-REN Wigner Research Centre for Physics
Karen Safaryan: University of California, Los Angeles
Michael Einstein: University of California, Los Angeles
Mauricio Vallejo Martelo: University of California, Los Angeles
Pierre-Olivier Polack: Rutgers University
Peyman Golshani: University of California, Los Angeles
Gergő Orbán: HUN-REN Wigner Research Centre for Physics

Nature Communications, 2024, vol. 15, issue 1, 1-17

Abstract: Abstract Attention supports decision making by selecting the features that are relevant for decisions. Selective enhancement of the relevant features and inhibition of distractors has been proposed as potential neural mechanisms driving this selection process. Yet, how attention operates when relevance cannot be directly determined, and the attention signal needs to be internally constructed is less understood. Here we recorded from populations of neurons in the anterior cingulate cortex (ACC) of mice in an attention-shifting task where relevance of stimulus modalities changed across blocks of trials. In contrast with V1 recordings, decoding of the irrelevant modality gradually declined in ACC after an initial transient. Our analytical proof and a recurrent neural network model of the task revealed mutually inhibiting connections that produced context-gated suppression as observed in mice. Using this RNN model we predicted a correlation between contextual modulation of individual neurons and their stimulus drive, which we confirmed in ACC but not in V1.

Date: 2024
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DOI: 10.1038/s41467-024-49845-2

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