Abstract

Decision-making requires flexibility to rapidly switch one’s actions in response to sensory stimuli depending on information stored in memory. We identified cortical areas and neural activity patterns underlying this flexibility during virtual navigation, where mice switched navigation toward or away from a visual cue depending on its match to a remembered cue. Optogenetics screening identified V1, posterior parietal cortex (PPC), and retrosplenial cortex (RSC) as necessary for accurate decisions. Calcium imaging revealed neurons that can mediate rapid navigation switches by encoding a mixture of a current and remembered visual cue. These mixed selectivity neurons emerged through task learning and predicted the mouse’s choices by forming efficient population codes before correct, but not incorrect, choices. They were distributed across posterior cortex, even V1, and were densest in RSC and sparsest in PPC. We propose flexibility in navigation decisions arises from neurons that mix visual and memory information within a visual-parietal-retrosplenial network.

Animals flexibly and rapidly adapt navigation routes to the environment and context. Here, the authors find that the flexibility in navigation decisions arises from cells distributed in posterior cortex, each of which mixes sensory and memory information.

Details

Title
A distributed and efficient population code of mixed selectivity neurons for flexible navigation decisions
Author
Kira, Shinichiro 1   VIAFID ORCID Logo  ; Safaai, Houman 2   VIAFID ORCID Logo  ; Morcos, Ari S. 1   VIAFID ORCID Logo  ; Panzeri, Stefano 3   VIAFID ORCID Logo  ; Harvey, Christopher D. 1   VIAFID ORCID Logo 

 Harvard Medical School, Department of Neurobiology, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X) 
 Harvard Medical School, Department of Neurobiology, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); Istituto Italiano di Tecnologia, Neural Computation Laboratory, Rovereto, Italy (GRID:grid.25786.3e) (ISNI:0000 0004 1764 2907) 
 Istituto Italiano di Tecnologia, Neural Computation Laboratory, Rovereto, Italy (GRID:grid.25786.3e) (ISNI:0000 0004 1764 2907); University Medical Center Hamburg-Eppendorf (UKE), Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), Hamburg, Germany (GRID:grid.13648.38) (ISNI:0000 0001 2180 3484) 
Pages
2121
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2800435172
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.