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.

Reviewing Editor

  1. Upinder Singh Bhalla, National Centre for Biological Sciences, Tata Institute of Fundamental Research, India

Version history

  1. Received: December 7, 2017
  2. Accepted: July 2, 2018
  3. Accepted Manuscript published: July 3, 2018 (version 1)
  4. Version of Record published: July 25, 2018 (version 2)

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.

Metrics

  • 2,741
    views
  • 459
    downloads
  • 35
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

Further reading

    1. Neuroscience
    Etienne Combrisson, Ruggero Basanisi ... Andrea Brovelli
    Research Article

    How human prefrontal and insular regions interact while maximizing rewards and minimizing punishments is unknown. Capitalizing on human intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning is better disentangled by interactions compared to local representations. Prefrontal and insular cortices display non-selective neural populations to rewards and punishments. Non-selective responses, however, give rise to context-specific interareal interactions. We identify a reward subsystem with redundant interactions between the orbitofrontal and ventromedial prefrontal cortices, with a driving role of the latter. In addition, we find a punishment subsystem with redundant interactions between the insular and dorsolateral cortices, with a driving role of the insula. Finally, switching between reward and punishment learning is mediated by synergistic interactions between the two subsystems. These results provide a unifying explanation of distributed cortical representations and interactions supporting reward and punishment learning.

    1. Neuroscience
    Laura Boi, Yvonne Johansson ... Gilad Silberberg
    Research Article

    Parkinson’s disease (PD) is characterized by motor impairments caused by degeneration of dopamine neurons in the substantia nigra pars compacta. In addition to these symptoms, PD patients often suffer from non-motor comorbidities including sleep and psychiatric disturbances, which are thought to depend on concomitant alterations of serotonergic and noradrenergic transmission. A primary locus of serotonergic neurons is the dorsal raphe nucleus (DRN), providing brain-wide serotonergic input. Here, we identified electrophysiological and morphological parameters to classify serotonergic and dopaminergic neurons in the murine DRN under control conditions and in a PD model, following striatal injection of the catecholamine toxin, 6-hydroxydopamine (6-OHDA). Electrical and morphological properties of both neuronal populations were altered by 6-OHDA. In serotonergic neurons, most changes were reversed when 6-OHDA was injected in combination with desipramine, a noradrenaline (NA) reuptake inhibitor, protecting the noradrenergic terminals. Our results show that the depletion of both NA and dopamine in the 6-OHDA mouse model causes changes in the DRN neural circuitry.