The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies

There is growing symbiosis between artificial and biological intelligence sciences: neural principles inspire new intelligent machines, which are in turn used to advance our theoretical understanding of the brain. To promote further collaboration between biological and artificial intelligence resear...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Gifford, Alessandro T, Bersch, Domenic, St-Laurent, Marie, Pinsard, Basile, Boyle, Julie, Bellec, Lune, Oliva, Aude, Roig, Gemma, Cichy, Radoslaw M
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Gifford, Alessandro T
Bersch, Domenic
St-Laurent, Marie
Pinsard, Basile
Boyle, Julie
Bellec, Lune
Oliva, Aude
Roig, Gemma
Cichy, Radoslaw M
description There is growing symbiosis between artificial and biological intelligence sciences: neural principles inspire new intelligent machines, which are in turn used to advance our theoretical understanding of the brain. To promote further collaboration between biological and artificial intelligence researchers, we introduce the 2025 edition of the Algonauts Project challenge: How the Human Brain Makes Sense of Multimodal Movies (https://algonautsproject.com/). In collaboration with the Courtois Project on Neuronal Modelling (CNeuroMod), this edition aims to bring forth a new generation of brain encoding models that are multimodal and that generalize well beyond their training distribution, by training them on the largest dataset of fMRI responses to movie watching available to date. Open to all, the 2025 challenge provides transparent, directly comparable results through a public leaderboard that is updated automatically after each submission to facilitate rapid model assessment and guide development. The challenge will end with a session at the 2025 Cognitive Computational Neuroscience (CCN) conference that will feature winning models. We welcome researchers interested in collaborating with the Algonauts Project by contributing ideas and datasets for future challenges.
doi_str_mv 10.48550/arxiv.2501.00504
format Article
fullrecord <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2501_00504</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2501_00504</sourcerecordid><originalsourceid>FETCH-arxiv_primary_2501_005043</originalsourceid><addsrcrecordid>eNqFzrEOgjAUQNEuDkb9ACffD4gFaWLclGhYSExkciEv-oBqaU1bUP9eJe5Od7nDYWwa8iBeCcEXaJ-yCyLBw4BzweMhO-U1wUZVRmPrHRysudLZQ8QjAUmNSpGuaA2peYD_nGnboIatRakhwxs5OJJ2BKaErFVeNuaCCjLTSXJjNihROZr8OmKz_S5P0nmvKO5WNmhfxVdT9Jrl_-MNkqM-MA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies</title><source>arXiv.org</source><creator>Gifford, Alessandro T ; Bersch, Domenic ; St-Laurent, Marie ; Pinsard, Basile ; Boyle, Julie ; Bellec, Lune ; Oliva, Aude ; Roig, Gemma ; Cichy, Radoslaw M</creator><creatorcontrib>Gifford, Alessandro T ; Bersch, Domenic ; St-Laurent, Marie ; Pinsard, Basile ; Boyle, Julie ; Bellec, Lune ; Oliva, Aude ; Roig, Gemma ; Cichy, Radoslaw M</creatorcontrib><description>There is growing symbiosis between artificial and biological intelligence sciences: neural principles inspire new intelligent machines, which are in turn used to advance our theoretical understanding of the brain. To promote further collaboration between biological and artificial intelligence researchers, we introduce the 2025 edition of the Algonauts Project challenge: How the Human Brain Makes Sense of Multimodal Movies (https://algonautsproject.com/). In collaboration with the Courtois Project on Neuronal Modelling (CNeuroMod), this edition aims to bring forth a new generation of brain encoding models that are multimodal and that generalize well beyond their training distribution, by training them on the largest dataset of fMRI responses to movie watching available to date. Open to all, the 2025 challenge provides transparent, directly comparable results through a public leaderboard that is updated automatically after each submission to facilitate rapid model assessment and guide development. The challenge will end with a session at the 2025 Cognitive Computational Neuroscience (CCN) conference that will feature winning models. We welcome researchers interested in collaborating with the Algonauts Project by contributing ideas and datasets for future challenges.</description><identifier>DOI: 10.48550/arxiv.2501.00504</identifier><language>eng</language><subject>Quantitative Biology - Neurons and Cognition</subject><creationdate>2024-12</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2501.00504$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2501.00504$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Gifford, Alessandro T</creatorcontrib><creatorcontrib>Bersch, Domenic</creatorcontrib><creatorcontrib>St-Laurent, Marie</creatorcontrib><creatorcontrib>Pinsard, Basile</creatorcontrib><creatorcontrib>Boyle, Julie</creatorcontrib><creatorcontrib>Bellec, Lune</creatorcontrib><creatorcontrib>Oliva, Aude</creatorcontrib><creatorcontrib>Roig, Gemma</creatorcontrib><creatorcontrib>Cichy, Radoslaw M</creatorcontrib><title>The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies</title><description>There is growing symbiosis between artificial and biological intelligence sciences: neural principles inspire new intelligent machines, which are in turn used to advance our theoretical understanding of the brain. To promote further collaboration between biological and artificial intelligence researchers, we introduce the 2025 edition of the Algonauts Project challenge: How the Human Brain Makes Sense of Multimodal Movies (https://algonautsproject.com/). In collaboration with the Courtois Project on Neuronal Modelling (CNeuroMod), this edition aims to bring forth a new generation of brain encoding models that are multimodal and that generalize well beyond their training distribution, by training them on the largest dataset of fMRI responses to movie watching available to date. Open to all, the 2025 challenge provides transparent, directly comparable results through a public leaderboard that is updated automatically after each submission to facilitate rapid model assessment and guide development. The challenge will end with a session at the 2025 Cognitive Computational Neuroscience (CCN) conference that will feature winning models. We welcome researchers interested in collaborating with the Algonauts Project by contributing ideas and datasets for future challenges.</description><subject>Quantitative Biology - Neurons and Cognition</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNqFzrEOgjAUQNEuDkb9ACffD4gFaWLclGhYSExkciEv-oBqaU1bUP9eJe5Od7nDYWwa8iBeCcEXaJ-yCyLBw4BzweMhO-U1wUZVRmPrHRysudLZQ8QjAUmNSpGuaA2peYD_nGnboIatRakhwxs5OJJ2BKaErFVeNuaCCjLTSXJjNihROZr8OmKz_S5P0nmvKO5WNmhfxVdT9Jrl_-MNkqM-MA</recordid><startdate>20241231</startdate><enddate>20241231</enddate><creator>Gifford, Alessandro T</creator><creator>Bersch, Domenic</creator><creator>St-Laurent, Marie</creator><creator>Pinsard, Basile</creator><creator>Boyle, Julie</creator><creator>Bellec, Lune</creator><creator>Oliva, Aude</creator><creator>Roig, Gemma</creator><creator>Cichy, Radoslaw M</creator><scope>ALC</scope><scope>GOX</scope></search><sort><creationdate>20241231</creationdate><title>The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies</title><author>Gifford, Alessandro T ; Bersch, Domenic ; St-Laurent, Marie ; Pinsard, Basile ; Boyle, Julie ; Bellec, Lune ; Oliva, Aude ; Roig, Gemma ; Cichy, Radoslaw M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-arxiv_primary_2501_005043</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Quantitative Biology - Neurons and Cognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Gifford, Alessandro T</creatorcontrib><creatorcontrib>Bersch, Domenic</creatorcontrib><creatorcontrib>St-Laurent, Marie</creatorcontrib><creatorcontrib>Pinsard, Basile</creatorcontrib><creatorcontrib>Boyle, Julie</creatorcontrib><creatorcontrib>Bellec, Lune</creatorcontrib><creatorcontrib>Oliva, Aude</creatorcontrib><creatorcontrib>Roig, Gemma</creatorcontrib><creatorcontrib>Cichy, Radoslaw M</creatorcontrib><collection>arXiv Quantitative Biology</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Gifford, Alessandro T</au><au>Bersch, Domenic</au><au>St-Laurent, Marie</au><au>Pinsard, Basile</au><au>Boyle, Julie</au><au>Bellec, Lune</au><au>Oliva, Aude</au><au>Roig, Gemma</au><au>Cichy, Radoslaw M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies</atitle><date>2024-12-31</date><risdate>2024</risdate><abstract>There is growing symbiosis between artificial and biological intelligence sciences: neural principles inspire new intelligent machines, which are in turn used to advance our theoretical understanding of the brain. To promote further collaboration between biological and artificial intelligence researchers, we introduce the 2025 edition of the Algonauts Project challenge: How the Human Brain Makes Sense of Multimodal Movies (https://algonautsproject.com/). In collaboration with the Courtois Project on Neuronal Modelling (CNeuroMod), this edition aims to bring forth a new generation of brain encoding models that are multimodal and that generalize well beyond their training distribution, by training them on the largest dataset of fMRI responses to movie watching available to date. Open to all, the 2025 challenge provides transparent, directly comparable results through a public leaderboard that is updated automatically after each submission to facilitate rapid model assessment and guide development. The challenge will end with a session at the 2025 Cognitive Computational Neuroscience (CCN) conference that will feature winning models. We welcome researchers interested in collaborating with the Algonauts Project by contributing ideas and datasets for future challenges.</abstract><doi>10.48550/arxiv.2501.00504</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier DOI: 10.48550/arxiv.2501.00504
ispartof
issn
language eng
recordid cdi_arxiv_primary_2501_00504
source arXiv.org
subjects Quantitative Biology - Neurons and Cognition
title The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-10T03%3A56%3A26IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20Algonauts%20Project%202025%20Challenge:%20How%20the%20Human%20Brain%20Makes%20Sense%20of%20Multimodal%20Movies&rft.au=Gifford,%20Alessandro%20T&rft.date=2024-12-31&rft_id=info:doi/10.48550/arxiv.2501.00504&rft_dat=%3Carxiv_GOX%3E2501_00504%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true