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    Reservoir computing model of prefrontal cortex creates novel combinations of previous navigation sequences from hippocampal place-cell replay with spatial reward propagation

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    journal.pcbi.1006624.pdf
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    Author
    Cazin, Nicolas
    Llofriu Alonso, Martin
    Scleidorovich Chiodi, Pablo
    Pelc, Tatiana
    Harland, Bruce
    Weitzenfeld, Alfredo
    Fellous, Jean-Marc
    Dominey, Peter Ford
    Affiliation
    Univ Arizona, Dept Psychol
    Issue Date
    2019-07-15
    
    Metadata
    Show full item record
    Publisher
    PUBLIC LIBRARY SCIENCE
    Citation
    Cazin N, Llofriu Alonso M, Scleidorovich Chiodi P, Pelc T, Harland B, Weitzenfeld A, et al. (2019) Reservoir computing model of prefrontal cortex creates novel combinations of previous navigation sequences from hippocampal place-cell replay with spatial reward propagation. PLoS Comput Biol 15(7): e1006624. https://doi.org/10.1371/journal.pcbi.1006624
    Journal
    PLOS COMPUTATIONAL BIOLOGY
    Rights
    Copyright © 2019 Cazin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    As rats learn to search for multiple sources of food or water in a complex environment, they generate increasingly efficient trajectories between reward sites. Such spatial navigation capacity involves the replay of hippocampal place-cells during awake states, generating small sequences of spatially related place-cell activity that we call "snippets". These snippets occur primarily during sharp-wave-ripples (SWRs). Here we focus on the role of such replay events, as the animal is learning a traveling salesperson task (TSP) across multiple trials. We hypothesize that snippet replay generates synthetic data that can substantially expand and restructure the experience available and make learning more optimal. We developed a model of snippet generation that is modulated by reward, propagated in the forward and reverse directions. This implements a form of spatial credit assignment for reinforcement learning. We use a biologically motivated computational framework known as 'reservoir computing' to model prefrontal cortex (PFC) in sequence learning, in which large pools of prewired neural elements process information dynamically through reverberations. This PFC model consolidates snippets into larger spatial sequences that may be later recalled by subsets of the original sequences. Our simulation experiments provide neurophysiological explanations for two pertinent observations related to navigation. Reward modulation allows the system to reject non-optimal segments of experienced trajectories, and reverse replay allows the system to "learn" trajectories that it has not physically experienced, both of which significantly contribute to the TSP behavior.
    Note
    Open access journal
    ISSN
    1553-7358
    PubMed ID
    31306421
    DOI
    10.1371/journal.pcbi.1006624
    Version
    Final published version
    Sponsors
    CRCNS NFS-ANR [1429929]
    ae974a485f413a2113503eed53cd6c53
    10.1371/journal.pcbi.1006624
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    UA Faculty Publications

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