Fast and flexible sequence induction in spiking neural networks via rapid excitability changes

  1. Rich Pang  Is a corresponding author
  2. Adrienne L Fairhall
  1. University of Washington, United States

Abstract

Cognitive flexibility likely depends on modulation of the dynamics underlying how biological neural networks process information. While dynamics can be reshaped by gradually modifying connectivity, less is known about mechanisms operating on faster timescales. A compelling entrypoint to this problem is the observation that exploratory behaviors can rapidly cause selective hippocampal sequences to 'replay' during rest. Using a spiking network model, we asked whether simplified replay could arise from three biological components: fixed recurrent connectivity; stochastic 'gating' inputs; and rapid gating input scaling via long-term potentiation of intrinsic excitability (LTP-IE). Indeed, these enabled both forward and reverse replay of recent sensorimotor-evoked sequences, despite unchanged recurrent weights. LTP-IE 'tags' specific neurons with increased spiking probability under gating input, and ordering is reconstructed from recurrent connectivity. We further show how LTP-IE can implement temporary stimulus-response mappings. This elucidates a novel combination of mechanisms that might play a role in rapid cognitive flexibility.

Article and author information

Author details

  1. Rich Pang

    Physiology and Biophysics Department, University of Washington, Seattle, United States
    For correspondence
    rpang@uw.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2644-6110
  2. Adrienne L Fairhall

    Physiology and Biophysics Department, University of Washington, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Institutes of Health (R01DC013693)

  • Adrienne L Fairhall

Simons Foundation (Collaboration for the Global Brain)

  • Adrienne L Fairhall

University of Washington (Computational Neuroscience Training Grant)

  • Rich Pang

Washington Research Foundation (UW Institute for Neuroengineering)

  • Adrienne L Fairhall

National Institutes of Health (NIH) (R01NS104925)

  • Adrienne L Fairhall

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Emilio Salinas, Wake Forest School of Medicine, United States

Version history

  1. Received: December 12, 2018
  2. Accepted: May 11, 2019
  3. Accepted Manuscript published: May 13, 2019 (version 1)
  4. Version of Record published: May 28, 2019 (version 2)

Copyright

© 2019, Pang & Fairhall

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Rich Pang
  2. Adrienne L Fairhall
(2019)
Fast and flexible sequence induction in spiking neural networks via rapid excitability changes
eLife 8:e44324.
https://doi.org/10.7554/eLife.44324

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https://doi.org/10.7554/eLife.44324

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