Constructing neural network models from brain data reveals representational transformations linked to adaptive behavior
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s41467-022-28323-7.pdf
Description
Published version
Size
2.54 MB
Format
Adobe PDF
Checksum (MD5)
923514492bf9d09e2616910ddd74740f
Author(s)
Ito, Takuya
Yang, Guangyu Robert
Laurent, Patryk
Schultz, Douglas H
Cole, Michael W
Date Issued
2022
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Ito, Takuya, Yang, Guangyu Robert, Laurent, Patryk, Schultz, Douglas H and Cole, Michael W. 2022. "Constructing neural network models from brain data reveals representational transformations linked to adaptive behavior." Nature Communications, 13 (1).
Version
Final published version
Abstract
AbstractThe 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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/S41467-022-28323-7
