View in EDS

Joint modelling of brain and behaviour dynamics with artificial intelligence.

Saved in:
Bibliographic Details
Title: Joint modelling of brain and behaviour dynamics with artificial intelligence.
Authors: Mathis, Mackenzie Weygandt (AUTHOR), Mathis, Alexander (AUTHOR)
Source: Nature Reviews Neuroscience. Feb2026, Vol. 27 Issue 2, p87-100. 14p.
Abstract: Artificial intelligence has created tremendous advances for many scientific and engineering applications. In this Review, we synthesize recent advances in joint brain–behaviour modelling of neural and behavioural data, with a focus on methodological innovations, scientific and technical motivations, and key areas for future innovation. We discuss how these tools reveal the shared structure between the brain and behaviour and how they can be used for both science and engineering aims. We highlight how three broad classes with differing aims — discriminative, generative and contrastive — are shaping joint modelling approaches. We also discuss recent advances in behavioural analysis approaches, including pose estimation, hierarchical behaviour analysis and multimodal-language models, which could influence the next generation of joint models. Finally, we argue that considering not only the performance of models but also their trustworthiness and interpretability metrics can help to advance the development of joint modelling approaches. Artificial intelligence is rapidly advancing our mechanistic understanding of the shared structure between the brain and higher-order behaviours. In this Review, Mathis and Mathis synthesize state-of-the-art methods in joint modelling of neural activity and behaviour, emphasizing both the technical innovations and the conceptual frameworks driving progress in this rapidly evolving field. [ABSTRACT FROM AUTHOR]
Copyright of Nature Reviews Neuroscience is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
FullText Text:
  Availability: 0
Header DbId: pbh
DbLabel: Psychology and Behavioral Sciences Collection
An: 191135165
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Joint modelling of brain and behaviour dynamics with artificial intelligence.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Mathis%2C+Mackenzie+Weygandt%22">Mathis, Mackenzie Weygandt</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mathis%2C+Alexander%22">Mathis, Alexander</searchLink> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Nature+Reviews+Neuroscience%22">Nature Reviews Neuroscience</searchLink>. Feb2026, Vol. 27 Issue 2, p87-100. 14p.
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Artificial intelligence has created tremendous advances for many scientific and engineering applications. In this Review, we synthesize recent advances in joint brain–behaviour modelling of neural and behavioural data, with a focus on methodological innovations, scientific and technical motivations, and key areas for future innovation. We discuss how these tools reveal the shared structure between the brain and behaviour and how they can be used for both science and engineering aims. We highlight how three broad classes with differing aims — discriminative, generative and contrastive — are shaping joint modelling approaches. We also discuss recent advances in behavioural analysis approaches, including pose estimation, hierarchical behaviour analysis and multimodal-language models, which could influence the next generation of joint models. Finally, we argue that considering not only the performance of models but also their trustworthiness and interpretability metrics can help to advance the development of joint modelling approaches. Artificial intelligence is rapidly advancing our mechanistic understanding of the shared structure between the brain and higher-order behaviours. In this Review, Mathis and Mathis synthesize state-of-the-art methods in joint modelling of neural activity and behaviour, emphasizing both the technical innovations and the conceptual frameworks driving progress in this rapidly evolving field. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Nature Reviews Neuroscience is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=191135165
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1038/s41583-025-00996-1
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 87
    Titles:
      – TitleFull: Joint modelling of brain and behaviour dynamics with artificial intelligence.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Mathis, Mackenzie Weygandt
      – PersonEntity:
          Name:
            NameFull: Mathis, Alexander
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 1471003X
          Numbering:
            – Type: volume
              Value: 27
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Nature Reviews Neuroscience
              Type: main
ResultId 1