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Functional relevance of different basal ganglia pathways investigated in a spiking model with reward dependent plasticity
Stockholm University, Faculty of Science, Numerical Analysis and Computer Science (NADA).ORCID iD: 0000-0002-6878-6842
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(English)Manuscript (preprint) (Other academic)
National Category
Bioinformatics (Computational Biology)
Research subject
Computer Science
URN: urn:nbn:se:su:diva-124355OAI: diva2:885518
Available from: 2015-12-18 Created: 2015-12-18 Last updated: 2015-12-22Bibliographically approved
In thesis
1. Computational Modeling of the Basal Ganglia: Functional Pathways and Reinforcement Learning
Open this publication in new window or tab >>Computational Modeling of the Basal Ganglia: Functional Pathways and Reinforcement Learning
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Stockholm: Numerical Analysis and Computer Science (NADA), Stockholm University, 2015. 134 p.
Trita-CSC-A, ISSN 1653-5723
computational neuroscience, modelisation, reinforcement learning, basal ganglia, dopamine
National Category
Bioinformatics (Computational Biology)
Research subject
Computer Science
urn:nbn:se:su:diva-123747 (URN)978-91-7649-184-3 (ISBN)
Public defence
2016-01-25, F3, Sing Sing, KTH Campus, Lindstedtsvägen 26, Stockholm, 14:00 (English)
EU, FP7, Seventh Framework Programme, FP7-237955

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.


Available from: 2015-12-29 Created: 2015-12-04 Last updated: 2016-01-25Bibliographically approved

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Berthet, PierreLansner, Anders
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