English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
 Local TagsRelease HistoryDetailsSummary
  Diverse task-driven modeling of macaque V4 reveals functional specialization towards semantic tasks

Cadena, S., Willeke, K., Restivo, K., Denfield, G., Sinz, F., Bethge, M., et al. (2024). Diverse task-driven modeling of macaque V4 reveals functional specialization towards semantic tasks. PLOS Computational Biology, 20(5): e1012056. doi:10.1371/journal.pcbi.1012056.

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://hdl.handle.net/21.11116/0000-000F-6783-9 Version Permalink: https://hdl.handle.net/21.11116/0000-000F-6784-8
Genre: Journal Article

Files

show Files
hide Files
:
journal.pcbi.1012056.pdf (Publisher version), 3MB
View   Save
File Permalink:
https://hdl.handle.net/21.11116/0000-000F-6785-7
Name:
Publisher Version
Description:
-
OA-Status:
Gold
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
View
Copyright Date:
-
Copyright Info:
-
License:
http://creativecommons.org/licenses/by/4.0/

Locators

show

Creators

show
hide
 Creators:
Cadena, S.A., Author
Willeke, K.F., Author
Restivo, K., Author
Denfield, G., Author
Sinz, F.H., Author
Bethge, M., Author
Tolias, A.S., Author
Ecker, A.S.1, Author
Affiliations:
1Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, Am Faßberg 17, 37077 Göttingen, DE, ou_2063285              

Content

show
hide
Free keywords: -
 Abstract: Responses to natural stimuli in area V4—a mid-level area of the visual ventral stream—are well predicted by features from convolutional neural networks (CNNs) trained on image classification. This result has been taken as evidence for the functional role of V4 in object classification. However, we currently do not know if and to what extent V4 plays a role in solving other computational objectives. Here, we investigated normative accounts of V4 (and V1 for comparison) by predicting macaque single-neuron responses to natural images from the representations extracted by 23 CNNs trained on different computer vision tasks including semantic, geometric, 2D, and 3D types of tasks. We found that V4 was best predicted by semantic classification features and exhibited high task selectivity, while the choice of task was less consequential to V1 performance. Consistent with traditional characterizations of V4 function that show its high-dimensional tuning to various 2D and 3D stimulus directions, we found that diverse non-semantic tasks explained aspects of V4 function that are not captured by individual semantic tasks. Nevertheless, jointly considering the features of a pair of semantic classification tasks was sufficient to yield one of our top V4 models, solidifying V4’s main functional role in semantic processing and suggesting that V4’s selectivity to 2D or 3D stimulus properties found by electrophysiologists can result from semantic functional goals.

Details

show
hide
Language(s): eng - English
 Dates: 2024-05-23
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1012056
 Degree: -

Event

show

Legal Case

show

Project information

show hide
Project name : --
Grant ID : -
Funding program : -
Funding organization : -

Source 1

show
hide
Title: PLOS Computational Biology
  Abbreviation : PLOS Comput Biol
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 20 (5) Sequence Number: e1012056 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1