Evidence map›Paper›PMID 40787627›Full record

ArticleIBRO neuroscience reports2025

View-invariant object representation in anterior and posterior inferotemporal cortex: A machine learning approach.

Jun-Ya Okamura, Daisuke Fukano, Keisuke Murakami, Gang Wang

Abstract read
In one paragraph

Article in IBRO neuroscience reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Jun-Ya OkamuraInformation Science and Biomedical Engineering Program, Graduate School of Science and Engineering, Kagoshima University, Kagoshima 890-0065, Japan.
Daisuke FukanoInformation Science and Biomedical Engineering Program, Graduate School of Science and Engineering, Kagoshima University, Kagoshima 890-0065, Japan.
Keisuke MurakamiInformation Science and Biomedical Engineering Program, Graduate School of Science and Engineering, Kagoshima University, Kagoshima 890-0065, Japan.
Gang WangInformation Science and Biomedical Engineering Program, Graduate School of Science and Engineering, Kagoshima University, Kagoshima 890-0065, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inferotemporal (IT) cortex is the final visual area in the ventral stream where object information is processed. Previous electrophysiological studies showed viewing angle tolerance of 30-60° of single IT cells to the objects experienced in discrimination at each of several viewing angles, and to the objects experienced in learning association of different views. IT is divided into anterior (cytoarchitectonic area TE) and posterior (TEO) parts. It was reported that single cells in area TE showed the viewing angle tolerance while those in area TEO did not. In the present study population activities were compared between cell populations in area TE and those in area TEO using machine learning algorithm. An object set consisted of four similar objects created by deforming a prototype object, and four views each separated by 30°. A population vector was created by aligning responses of the cells to each object image. A classifier was trained by support vector machine (SVM) to create a hyperplane that separated one object from the other three objects at the same viewing angles, and then tested by response vectors to the object images at different viewing angles. In area TE, dynamics of the performance evaluated by d' showed viewing angle tolerance of 30-90° to the objects with prior experience in learning association of different views. In area TEO, populations of the cells showed the viewing angle tolerance of 30°. Significant increase of the d' values in area TE in the late time period for the objects with prior experience in learning association of different views may suggest view-invariance is more represented in late time period than early time period. These results suggest that viewpoint invariance is expressed more strongly in the TE region, and expressed in part in the population of the TEO cells.

Indexed as

Inferotemporal cortexMachine learningObject recognitionTETEOView-invariance

Identifiers

PMID40787627
PMCPMC12332959

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.