Object Recognition at Higher Regions of the Ventral Visual Stream via Dynamic Inference.
IT cortex
Viterbi algorithm
decoding
dynamic inference
object recognition
sequence estimation
Journal
Frontiers in computational neuroscience
ISSN: 1662-5188
Titre abrégé: Front Comput Neurosci
Pays: Switzerland
ID NLM: 101477956
Informations de publication
Date de publication:
2020
2020
Historique:
received:
06
01
2020
accepted:
30
04
2020
entrez:
14
7
2020
pubmed:
14
7
2020
medline:
14
7
2020
Statut:
epublish
Résumé
The ventral visual stream (VVS) is a fundamental pathway involved in visual object identification and recognition. In this work, we present a hypothesis of a sequence of computations performed by the VVS during object recognition. The operations performed by the inferior temporal (IT) cortex are represented as not being akin to a neural-network, but rather in-line with a dynamic inference instantiation of the untangling notion. The presentation draws upon a technique for dynamic maximum a posteriori probability (MAP) sequence estimation based on the Viterbi algorithm. Simulation results are presented to show that the decoding portion of the architecture that is associated with the IT can effectively untangle object identity when presented with synthetic data. More importantly, we take a step forward in visual neuroscience by presenting a framework for an inference-based approach that is biologically inspired via attributes implicated in primate object recognition. The analysis will provide insight in explaining the exceptional proficiency of the VVS.
Identifiants
pubmed: 32655388
doi: 10.3389/fncom.2020.00046
pmc: PMC7325008
doi:
Types de publication
Journal Article
Langues
eng
Pagination
46Informations de copyright
Copyright © 2020 Sorooshyari, Sheng and Poor.
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