Recurrent network model for learning goal-directed sequences through reverse replay

  1. Tatsuya Haga  Is a corresponding author
  2. Tomoki Fukai  Is a corresponding author
  1. RIKEN, Japan

Abstract

Reverse replay of hippocampal place cells occurs frequently at rewarded locations, suggesting its contribution to goal-directed path learning. Symmetric spike-timing dependent plasticity (STDP) in CA3 likely potentiates recurrent synapses for both forward (start to goal) and reverse (goal to start) replays during sequential activation of place cells. However, how reverse replay selectively strengthens forward synaptic pathway is unclear. Here, we show computationally that firing sequences bias synaptic transmissions to the opposite direction of propagation under symmetric STDP in the co-presence of short-term synaptic depression or afterdepolarization. We demonstrate that significant biases are created in biologically realistic simulation settings, and this bias enables reverse replay to enhance goal-directed spatial memory on a W-maze. Further, we show that essentially the same mechanism works in a two-dimensional open field. Our model for the first time provides the mechanistic account for the way reverse replay contributes to hippocampal sequence learning for reward-seeking spatial navigation.

Data availability

Our study is based on computer simulations, and we are sharing codes in Github.

Article and author information

Author details

  1. Tatsuya Haga

    Center for Brain Science, RIKEN, Wako, Japan
    For correspondence
    tatsuya.haga@riken.jp
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3145-709X
  2. Tomoki Fukai

    Center for Brain Science, RIKEN, Wako, Japan
    For correspondence
    tfukai@riken.jp
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6977-5638

Funding

Ministry of Education, Culture, Sports, Science, and Technology (15H04265)

  • Tomoki Fukai

Core Research for Evolutional Science and Technology (JPMJCR13W1)

  • Tomoki Fukai

Ministry of Education, Culture, Sports, Science, and Technology (16H01289)

  • Tomoki Fukai

Ministry of Education, Culture, Sports, Science, and Technology (17H06036)

  • Tomoki Fukai

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

Copyright

© 2018, Haga & Fukai

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. Tatsuya Haga
  2. Tomoki Fukai
(2018)
Recurrent network model for learning goal-directed sequences through reverse replay
eLife 7:e34171.
https://doi.org/10.7554/eLife.34171

Share this article

https://doi.org/10.7554/eLife.34171

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