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Dynamic representations in networked neural systems

Nature Neuroscience volume 23, pages 908–917 (2020)Cite this article

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Abstract

A group of neurons can generate patterns of activity that represent information about stimuli; subsequently, the group can transform and transmit activity patterns across synapses to spatially distributed areas. Recent studies in neuroscience have begun to independently address the two components of information processing: the representation of stimuli in neural activity and the transmission of information in networks that model neural interactions. Yet only recently are studies seeking to link these two types of approaches. Here we briefly review the two separate bodies of literature; we then review the recent strides made to address this gap. We continue with a discussion of how patterns of activity evolve from one representation to another, forming dynamic representations that unfold on the underlying network. Our goal is to offer a holistic framework for understanding and describing neural information representation and transmission while revealing exciting frontiers for future research.

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Fig. 1: Neural representations and tools to analyze them.
Fig. 2: Network models abstract neural systems.
Fig. 3: Integrating network models and neural representations.
Fig. 4: Dynamic representations in networked neural systems.
Fig. 5: Dynamic representations as trajectories in neural state space.

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Acknowledgements

H.J. and D.S.B. acknowledge support from the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, the ISI Foundation, the Paul Allen Foundation, the Army Research Laboratory (W911NF-10-2-0022), the Army Research Office (Bassett-W911NF-14-1-0679, Grafton-W911NF-16-1-0474, DCIST-W911NF-17-2-0181), the Office of Naval Research, the National Institute of Mental Health (2-R01-DC-00920911, R01-MH112847, R01-MH107235, R21-M MH-106799), the National Institute of Child Health and Human Development (1R01-HD086888-01), National Institute of Neurological Disorders and Stroke (R01-NS099348) and the National Science Foundation (BCS-1441502, BCS-1430087, NSF PHY-1554488 and BCS-1631550). We thank E.J. Cornblath, L. Papadopoulos, C.W. Lynn, D. Zhou and A. Sizemore Blevins for helpful feedback. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.

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Authors and Affiliations

  1. Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, PA, USA

    Harang Ju

  2. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

  3. Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

  4. Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

  5. Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

  6. Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

  7. Santa Fe Institute, Santa Fe, NM, USA

    Danielle S. Bassett

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  1. Harang Ju
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H.J. and D.S.B. contributed to the writing.

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Correspondence to Danielle S. Bassett.

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Peer review information Nature Neuroscience thanks Johan Carlin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Ju, H., Bassett, D.S. Dynamic representations in networked neural systems. Nat Neurosci 23, 908–917 (2020). https://doi.org/10.1038/s41593-020-0653-3

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