Learning a Model of Shape Selectivity in V4 Cells Reveals Shape Encoding Mechanisms in the Brain.
V4
learning shape
shape selectivity
sparse coding
Journal
The Journal of neuroscience : the official journal of the Society for Neuroscience
ISSN: 1529-2401
Titre abrégé: J Neurosci
Pays: United States
ID NLM: 8102140
Informations de publication
Date de publication:
31 05 2023
31 05 2023
Historique:
received:
02
08
2022
revised:
10
04
2023
accepted:
15
04
2023
medline:
2
6
2023
pubmed:
15
5
2023
entrez:
15
5
2023
Statut:
ppublish
Résumé
The mechanisms involved in transforming early visual signals to curvature representations in V4 are unknown. We propose a hierarchical model that reveals V1/V2 encodings that are essential components for this transformation to the reported curvature representations in V4. Then, by relaxing the often-imposed prior of a single Gaussian, V4 shape selectivity is learned in the last layer of the hierarchy from Macaque V4 responses. We found that V4 cells integrate multiple shape parts from the full spatial extent of their receptive fields with similar excitatory and inhibitory contributions. Our results uncover new details in existing data about shape selectivity in V4 neurons that with additional experiments can enhance our understanding of processing in this area. Accordingly, we propose designs for a stimulus set that allow removing shape parts without disturbing the curvature signal to isolate part contributions to V4 responses.
Identifiants
pubmed: 37185098
pii: JNEUROSCI.1467-22.2023
doi: 10.1523/JNEUROSCI.1467-22.2023
pmc: PMC10255038
doi:
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
4129-4143Informations de copyright
Copyright © 2023 Mehrani and Tsotsos.
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