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The Functional Role of Striata Cholinergic Interneurons in Reinforcement Learning From Computational Perspective
Journal article   Open access   Peer reviewed

The Functional Role of Striata Cholinergic Interneurons in Reinforcement Learning From Computational Perspective

Taegyo Kim, Robert A. Capps, Khaldoun C. Hamade, William H. Barnett, Dmitrii Todorov, Elizaveta M. Latash, Sergey N. Markin, Ilya A. Rybak and Yaroslav Molkov
Frontiers in neural circuits, v 13
21 Feb 2019
PMID: 30846930
url
https://doi.org/10.3389/fncir.2019.00010View
Published, Version of Record (VoR)CC BY V4.0 Open

Abstract

Life Sciences & Biomedicine Neurosciences Neurosciences & Neurology Science & Technology
In this study, we explore the functional role of striatal cholinergic interneurons, hereinafter referred to as tonically active neurons (TANs), via computational modeling; specifically, we investigate the mechanistic relationship between TAN activity and dopamine variations and how changes in this relationship affect reinforcement learning in the striatum. TANs pause their tonic firing activity after excitatory stimuli from thalamic and cortical neurons in response to a sensory event or reward information. During the pause striatal dopamine concentration excursions are observed. However, functional interactions between the TAN pause and striatal dopamine release are poorly understood. Here we propose a TAN activity-dopamine relationship model and demonstrate that the TAN pause is likely a time window to gate phasic dopamine release and dopamine variations reciprocally modulate the TAN pause duration. Furthermore, this model is integrated into our previously published model of reward-based motor adaptation to demonstrate how phasic dopamine release is gated by the TAN pause to deliver reward information for reinforcement learning in a timely manner. We also show how TAN-dopamine interactions are affected by striatal dopamine deficiency to produce poor performance of motor adaptation.

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Collaboration types
Domestic collaboration
Web of Science research areas
Neurosciences
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