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Network structure influences the strength of learned neural representations

Author

Listed:
  • Ari E. Kahn

    (Princeton University)

  • Karol Szymula

    (University of Rochester School of Medicine and Dentistry)

  • Sophie Loman

    (University of Pennsylvania)

  • Edda B. Haggerty

    (University of Pennsylvania)

  • Nathaniel Nyema

    (University of Pennsylvania)

  • Geoffrey K. Aguirre

    (University of Pennsylvania)

  • Dani S. Bassett

    (University of Pennsylvania
    University of Pennsylvania
    University of Pennsylvania
    University of Pennsylvania)

Abstract

From sequences of discrete events, humans build mental models of their world. Referred to as graph learning, the process produces a model encoding the graph of event-to-event transition probabilities. Recent evidence suggests that some networks are easier to learn than others, but the neural underpinnings of this effect remain unknown. Here we use fMRI to show that even over short timescales the network structure of a temporal sequence of stimuli determines the fidelity of event representations as well as the dimensionality of the space in which those representations are encoded: when the graph was modular as opposed to lattice-like, BOLD representations in visual areas better predicted trial identity and displayed higher intrinsic dimensionality. Broadly, our study shows that network context influences the strength of learned neural representations, motivating future work in the design, optimization, and adaptation of network contexts for distinct types of learning.

Suggested Citation

  • Ari E. Kahn & Karol Szymula & Sophie Loman & Edda B. Haggerty & Nathaniel Nyema & Geoffrey K. Aguirre & Dani S. Bassett, 2025. "Network structure influences the strength of learned neural representations," Nature Communications, Nature, vol. 16(1), pages 1-19, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-55459-5
    DOI: 10.1038/s41467-024-55459-5
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    References listed on IDEAS

    as
    1. Christopher W. Lynn & Ari E. Kahn & Nathaniel Nyema & Danielle S. Bassett, 2020. "Abstract representations of events arise from mental errors in learning and memory," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    2. Ari E. Kahn & Elisabeth A. Karuza & Jean M. Vettel & Danielle S. Bassett, 2018. "Network constraints on learnability of probabilistic motor sequences," Nature Human Behaviour, Nature, vol. 2(12), pages 936-947, December.
    3. Dion, Michelle L. & Sumner, Jane Lawrence & Mitchell, Sara McLaughlin, 2018. "Gendered Citation Patterns across Political Science and Social Science Methodology Fields," Political Analysis, Cambridge University Press, vol. 26(3), pages 312-327, July.
    4. Diego A. Gutnisky & Valentin Dragoi, 2008. "Adaptive coding of visual information in neural populations," Nature, Nature, vol. 452(7184), pages 220-224, March.
    5. Maliniak, Daniel & Powers, Ryan & Walter, Barbara F., 2013. "The Gender Citation Gap in International Relations," International Organization, Cambridge University Press, vol. 67(4), pages 889-922, October.
    6. Sangil Lee & Joseph W Kable, 2018. "Simple but robust improvement in multivoxel pattern classification," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-15, November.
    7. Hamed Nili & Cai Wingfield & Alexander Walther & Li Su & William Marslen-Wilson & Nikolaus Kriegeskorte, 2014. "A Toolbox for Representational Similarity Analysis," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-11, April.
    8. Arno Klein & Satrajit S Ghosh & Forrest S Bao & Joachim Giard & Yrjö Häme & Eliezer Stavsky & Noah Lee & Brian Rossa & Martin Reuter & Elias Chaibub Neto & Anisha Keshavan, 2017. "Mindboggling morphometry of human brains," PLOS Computational Biology, Public Library of Science, vol. 13(2), pages 1-40, February.
    9. Mattia Rigotti & Omri Barak & Melissa R. Warden & Xiao-Jing Wang & Nathaniel D. Daw & Earl K. Miller & Stefano Fusi, 2013. "The importance of mixed selectivity in complex cognitive tasks," Nature, Nature, vol. 497(7451), pages 585-590, May.
    10. Marcelo G. Mattar & Maria Olkkonen & Russell A. Epstein & Geoffrey K. Aguirre, 2018. "Adaptation decorrelates shape representations," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
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