Dopamine role in learning and action inference

  1. Rafal Bogacz  Is a corresponding author
  1. University of Oxford, United Kingdom

Abstract

This paper describes a framework for modelling dopamine function in the mammalian brain. It proposes that both learning and action planning involve processes minimizing prediction errors encoded by dopaminergic neurons. In this framework, dopaminergic neurons projecting to different parts of the striatum encode errors in predictions made by the corresponding systems within the basal ganglia. The dopaminergic neurons encode differences between rewards and expectations in the goal-directed system, and differences between the chosen and habitual actions in the habit system. These prediction errors trigger learning about rewards and habit formation, respectively. Additionally, dopaminergic neurons in the goal-directed system play a key role in action planning: They compute the difference between an available reward and the reward expected from the current motor plan, and they facilitate action planning until this difference diminishes. Presented models account for dopaminergic responses during movements, effects of dopamine depletion on behaviour, and make several experimental predictions.

Data availability

Matlab codes for all simulations described in the paper are available at MRC Brain Network Dynamics Unit Data Sharing Platform(https://data.mrc.ox.ac.uk/data-set/simulations-action-inference).

The following data sets were generated

Article and author information

Author details

  1. Rafal Bogacz

    Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom
    For correspondence
    rafal.bogacz@ndcn.ox.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8994-1661

Funding

Medical Research Council (MC_UU_12024/5)

  • Rafal Bogacz

Medical Research Council (MC_UU_00003/1)

  • Rafal Bogacz

Biotechnology and Biological Sciences Research Council (BB/S006338/1)

  • Rafal Bogacz

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

Reviewing Editor

  1. Thorsten Kahnt, Northwestern University, United States

Version history

  1. Received: November 1, 2019
  2. Accepted: July 6, 2020
  3. Accepted Manuscript published: July 7, 2020 (version 1)
  4. Version of Record published: July 30, 2020 (version 2)

Copyright

© 2020, Bogacz

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

  • 8,536
    Page views
  • 848
    Downloads
  • 17
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

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. Rafal Bogacz
(2020)
Dopamine role in learning and action inference
eLife 9:e53262.
https://doi.org/10.7554/eLife.53262

Share this article

https://doi.org/10.7554/eLife.53262

Further reading

    1. Computational and Systems Biology
    James D Brunner, Nicholas Chia
    Research Article

    The microbial community composition in the human gut has a profound effect on human health. This observation has lead to extensive use of microbiome therapies, including over-the-counter 'probiotic' treatments intended to alter the composition of the microbiome. Despite so much promise and commercial interest, the factors that contribute to the success or failure of microbiome-targeted treatments remain unclear. We investigate the biotic interactions that lead to successful engraftment of a novel bacterial strain introduced to the microbiome as in probiotic treatments. We use pairwise genome-scale metabolic modeling with a generalized resource allocation constraint to build a network of interactions between taxa that appear in an experimental engraftment study. We create induced sub-graphs using the taxa present in individual samples and assess the likelihood of invader engraftment based on network structure. To do so, we use a generalized Lotka-Volterra model, which we show has strong ability to predict if a particular invader or probiotic will successfully engraft into an individual's microbiome. Furthermore, we show that the mechanistic nature of the model is useful for revealing which microbe-microbe interactions potentially drive engraftment.

    1. Computational and Systems Biology
    Tae-Yun Kang, Federico Bocci ... Andre Levchenko
    Research Article

    Angiogenesis is a morphogenic process resulting in the formation of new blood vessels from pre-existing ones, usually in hypoxic micro-environments. The initial steps of angiogenesis depend on robust differentiation of oligopotent endothelial cells into the Tip and Stalk phenotypic cell fates, controlled by NOTCH-dependent cell–cell communication. The dynamics of spatial patterning of this cell fate specification are only partially understood. Here, by combining a controlled experimental angiogenesis model with mathematical and computational analyses, we find that the regular spatial Tip–Stalk cell patterning can undergo an order–disorder transition at a relatively high input level of a pro-angiogenic factor VEGF. The resulting differentiation is robust but temporally unstable for most cells, with only a subset of presumptive Tip cells leading sprout extensions. We further find that sprouts form in a manner maximizing their mutual distance, consistent with a Turing-like model that may depend on local enrichment and depletion of fibronectin. Together, our data suggest that NOTCH signaling mediates a robust way of cell differentiation enabling but not instructing subsequent steps in angiogenic morphogenesis, which may require additional cues and self-organization mechanisms. This analysis can assist in further understanding of cell plasticity underlying angiogenesis and other complex morphogenic processes.