Computational Modeling of the Basal Ganglia: Functional Pathways and Reinforcement Learning

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Berthet, Pierre,1982-Stockholms universitet,Numerisk analys och datalogi (NADA),Computational Biology (author)

Computational Modeling of the Basal Ganglia : Functional Pathways and Reinforcement Learning

BookEnglish2015

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Stockholm :Numerical Analysis and Computer Science (NADA), Stockholm University,2015
134 s.
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LIBRIS-ID:oai:DiVA.org:su-123747
ISBN:9789176491843
https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-123747URI

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Language:English
Summary in:English

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Subject category:dok swepub-publicationtype

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At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.
We perceive the environment via sensor arrays and interact with it through motor outputs. The work of this thesis concerns how the brain selects actions given the information about the perceived state of the world and how it learns and adapts these selections to changes in this environment. Reinforcement learning theories suggest that an action will be more or less likely to be selected if the outcome has been better or worse than expected. A group of subcortical structures, the basal ganglia (BG), is critically involved in both the selection and the reward prediction.We developed and investigated a computational model of the BG. We implemented a Bayesian-Hebbian learning rule, which computes the weights between two units based on the probability of their activations. We were able test how various configurations of the represented pathways impacted the performance in several reinforcement learning and conditioning tasks. Then, following the development of a more biologically plausible version with spiking neurons, we simulated lesions in the different pathways and assessed how they affected learning and selection.We observed that the evolution of the weights and the performance of the models resembled qualitatively experimental data. The absence of an unique best way to configure the model over all the learning paradigms tested indicates that an agent could dynamically configure its action selection mode, mainly by including or not the reward prediction values in the selection process. We present hypotheses on possible biological substrates for the reward prediction pathway. We base these on the functional requirements for successful learning and on an analysis of the experimental data. We further simulate a loss of dopaminergic neurons similar to that reported in Parkinson’s disease. We suggest that the associated motor symptoms are mostly causedby an impairment of the pathway promoting actions, while the pathway suppressing them seems to remain functional.

Subject headings and genre

NATURVETENSKAP Data- och informationsvetenskap Bioinformatik hsv//swe
NATURAL SCIENCES Computer and Information Sciences Bioinformatics hsv//eng
computational neuroscience
modelisation
reinforcement learning
basal ganglia
dopamine
datalogi
Computer Science

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Lansner, Anders,ProfessorStockholms universitet,Numerisk analys och datalogi (NADA) (thesis advisor)
Doya, Kenji,ProfessorOkinawa Institute of Science and Technology Graduate University, Japan (opponent)
Stockholms universitetNumerisk analys och datalogi (NADA) (creator_code:org_t)

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