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Constructing neural network models from brain data reveals representational transformations linked to adaptive behavior

Author

Listed:
  • Takuya Ito

    (Rutgers University
    Rutgers University
    Yale University School of Medicine)

  • Guangyu Robert Yang

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Columbia University)

  • Patryk Laurent

    (Independent Researcher)

  • Douglas H. Schultz

    (University of Nebraska-Lincoln)

  • Michael W. Cole

    (Rutgers University)

Abstract

The human ability to adaptively implement a wide variety of tasks is thought to emerge from the dynamic transformation of cognitive information. We hypothesized that these transformations are implemented via conjunctive activations in “conjunction hubs”—brain regions that selectively integrate sensory, cognitive, and motor activations. We used recent advances in using functional connectivity to map the flow of activity between brain regions to construct a task-performing neural network model from fMRI data during a cognitive control task. We verified the importance of conjunction hubs in cognitive computations by simulating neural activity flow over this empirically-estimated functional connectivity model. These empirically-specified simulations produced above-chance task performance (motor responses) by integrating sensory and task rule activations in conjunction hubs. These findings reveal the role of conjunction hubs in supporting flexible cognitive computations, while demonstrating the feasibility of using empirically-estimated neural network models to gain insight into cognitive computations in the human brain.

Suggested Citation

  • Takuya Ito & Guangyu Robert Yang & Patryk Laurent & Douglas H. Schultz & Michael W. Cole, 2022. "Constructing neural network models from brain data reveals representational transformations linked to adaptive behavior," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28323-7
    DOI: 10.1038/s41467-022-28323-7
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    References listed on IDEAS

    as
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    Cited by:

    1. Yue Liu & Xiao-Jing Wang, 2024. "Flexible gating between subspaces in a neural network model of internally guided task switching," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    2. Atsushi Kikumoto & Apoorva Bhandari & Kazuhisa Shibata & David Badre, 2024. "A transient high-dimensional geometry affords stable conjunctive subspaces for efficient action selection," Nature Communications, Nature, vol. 15(1), pages 1-16, December.

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