Evidence map›Paper›PMID 35283736›Full record

ArticleFrontiers in systems neuroscience2022

Recurrent Connections Might Be Important for Hierarchical Categorization.

Narihisa Matsumoto, Yusuke Taguchi, Masaumi Shimizu, Shun Katakami, Masato Okada, Yasuko Sugase-Miyamoto

Open access · goldAbstract read
In one paragraph

Article in Frontiers in systems neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 53% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Narihisa MatsumotoHuman Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan.
Yusuke TaguchiHuman Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan.
Masaumi ShimizuGraduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan.
Shun KatakamiGraduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan.
Masato OkadaGraduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan.
Yasuko Sugase-MiyamotoHuman Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan.
The University of Tokyo · JPNational Institute of Advanced Industrial Science and Technology · JPUniversity of Tsukuba · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Visual short-term memory is an important ability of primates and is thought to be stored in area TE. We previously reported that the initial transient responses of neurons in area TE represented information about a global category of faces, e.g., monkey faces vs. human faces vs. simple shapes, and the latter part of the responses represented information about fine categories, e.g., facial expression. The neuronal mechanisms of hierarchical categorization in area TE remain unknown. For this study, we constructed a combined model that consisted of a deep neural network (DNN) and a recurrent neural network and investigated whether this model can replicate the time course of hierarchical categorization. The visual images were stored in the recurrent connections of the model. When the visual images with noise were input to the model, the model outputted the time course of the hierarchical categorization. This result indicates that recurrent connections in the model are important not only for visual short-term memory but for hierarchical categorization, suggesting that recurrent connections in area TE are important for hierarchical categorization.

Indexed as

deep learningmodelingshort-term memoryvisual categoryvisual cortex

Identifiers

PMID35283736
PMCPMC8911877
OpenAlexW4213426189

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.