Convolutional neural networks for vision neuroscience: significance, developments, and outstanding issues.
Convolutional Neural Networks (CNN)
V1-independent vision
blindsight
pulvinar
superior colliculus
ventral stream
visual system
Journal
Frontiers in computational neuroscience
ISSN: 1662-5188
Titre abrégé: Front Comput Neurosci
Pays: Switzerland
ID NLM: 101477956
Informations de publication
Date de publication:
2023
2023
Historique:
received:
29
01
2023
accepted:
19
06
2023
medline:
24
7
2023
pubmed:
24
7
2023
entrez:
24
7
2023
Statut:
epublish
Résumé
Convolutional Neural Networks (CNN) are a class of machine learning models predominately used in computer vision tasks and can achieve human-like performance through learning from experience. Their striking similarities to the structural and functional principles of the primate visual system allow for comparisons between these artificial networks and their biological counterparts, enabling exploration of how visual functions and neural representations may emerge in the real brain from a limited set of computational principles. After considering the basic features of CNNs, we discuss the opportunities and challenges of endorsing CNNs as
Identifiants
pubmed: 37485400
doi: 10.3389/fncom.2023.1153572
pmc: PMC10359983
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
1153572Informations de copyright
Copyright © 2023 Celeghin, Borriero, Orsenigo, Diano, Méndez Guerrero, Perotti, Petri and Tamietto.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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