Single Circuit in V1 Capable of Switching Contexts During Movement Using an Inhibitory Population as a Switch.


Journal

Neural computation
ISSN: 1530-888X
Titre abrégé: Neural Comput
Pays: United States
ID NLM: 9426182

Informations de publication

Date de publication:
17 02 2022
Historique:
received: 06 01 2021
accepted: 21 09 2021
pubmed: 12 1 2022
medline: 4 3 2022
entrez: 11 1 2022
Statut: ppublish

Résumé

As animals adapt to their environments, their brains are tasked with processing stimuli in different sensory contexts. Whether these computations are context dependent or independent, they are all implemented in the same neural tissue. A crucial question is what neural architectures can respond flexibly to a range of stimulus conditions and switch between them. This is a particular case of flexible architecture that permits multiple related computations within a single circuit. Here, we address this question in the specific case of the visual system circuitry, focusing on context integration, defined as the integration of feedforward and surround information across visual space. We show that a biologically inspired microcircuit with multiple inhibitory cell types can switch between visual processing of the static context and the moving context. In our model, the VIP population acts as the switch and modulates the visual circuit through a disinhibitory motif. Moreover, the VIP population is efficient, requiring only a relatively small number of neurons to switch contexts. This circuit eliminates noise in videos by using appropriate lateral connections for contextual spatiotemporal surround modulation, having superior denoising performance compared to circuits where only one context is learned. Our findings shed light on a minimally complex architecture that is capable of switching between two naturalistic contexts using few switching units.

Identifiants

pubmed: 35016220
pii: 109061
doi: 10.1162/neco_a_01472
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

541-594

Subventions

Organisme : NIDA NIH HHS
ID : R90 DA033461
Pays : United States

Informations de copyright

© 2022 Massachusetts Institute of Technology.

Auteurs

Doris Voina (D)

Applied Mathematics, University of Washington, Seattle, WA 98195 U.S.A. dvoina@uw.edu.

Stefano Recanatesi (S)

Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, U.S.A. stefano.recanatesi@gmail.com.

Brian Hu (B)

Allen Institute for Brain Science, Seattle, WA 98109 U.S.A. brian.hu@kitware.com.

Eric Shea-Brown (E)

Applied Mathematics, University of Washington, Seattle, WA 98195, U.S.A. etsb@uw.edu.

Stefan Mihalas (S)

Applied Mathematics, University of Washington, Seattle, WA 98195, U.S.A.
Allen Institute for Brain Science, Seattle, WA 98109, U.S.A. stefanm@alleninstitute.org.

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