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BMC Neuroscience

Open Access

Stable reinforcement learning via temporal competition between LTP and LTD traces

  • Marco A Huertas1Email author,
  • Sarah Schwettmann1, 2,
  • Alfredo Kirkwood3 and
  • Harel Shouval1
BMC Neuroscience201415(Suppl 1):O12

https://doi.org/10.1186/1471-2202-15-S1-O12

Published: 21 July 2014

Neuronal systems that are involved in reinforcement learning must solve the temporal credit assignment problem, i.e., how is a stimulus associated with a reward that is delayed in time? Theoretical studies [13] have postulated that neural activity underlying learning ‘tags’ synapses with an ‘eligibility trace’, and that the subsequent arrival of a reward converts the eligibility traces into actual modification of synaptic efficacies. While eligibility traces provide one simple solution to the temporal credit assignment problem, they alone do not constitute a stable learning rule because there is no other mechanism indicating when learning should cease. In order to attain stability, rules involving eligibility traces often assume that once the association is learned, further learning is prevented via an inhibition of the reward stimulus [1, 3, 4].

Although synaptic plasticity is responsible for reinforcement learning in the brain, theories of reinforcement learning are generally abstract and involve neither neurons nor synapses. Furthermore, biophysical theories of synaptic plasticity typically model unsupervised learning and ignore the contribution of reinforcement. Here we describe a biophysically based theory of reinforcement-modulated synaptic plasticity and postulate the existence of two eligibility traces with different temporal profiles: one corresponding to the induction of LTP, and the other to the induction of LTD. The traces have different kinetics and their difference in magnitude at the time of reward determines if synaptic modification will correspond to LTP or LTD. Due to the difference in their decay rates, the LTP and LTD traces can exhibit temporal competition at the reward time and thus provides a mechanism for stable reinforcement learning without the need to inhibit reward. We test this novel reinforcement-learning rule on an experimentally motivated model of a recurrent cortical network [5], and compare the model results to experimental results at both the cellular and circuit levels. We further suggest that these eligibility traces are implemented via kinases and phosphatases, thus accounting for results at both the cellular and system levels.

Authors’ Affiliations

(1)
Dep. Neurobiology and Anatomy, University of Texas Medical School
(2)
Dep. Computational and Applied Mathematics, Rice University
(3)
Mind/Brain Institute, Johns Hopkins University

References

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Copyright

© Huertas et al; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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