Abstract

It is widely accepted that the basal ganglia (BG) play a key role in action selection and reinforcement learning. However, despite considerable number of studies, the BG architecture and function are not completely understood. Action selection and reinforcement learning are facilitated by the activity of dopaminergic neurons, which encode reward prediction errors when reward outcomes are higher or lower than expected. The BG are thought to select proper motor responses by gating appropriate actions, and suppressing inappropriate ones. The direct striato-nigral (GO) and the indirect striato-pallidal (NOGO) pathways have been suggested to provide the functions of BG in the two-pathway concept. Previous models confirmed the idea that these two pathways can mediate the behavioral choice, but only for a relatively small number of potential behaviors. Recent studies have provided new evidence of BG involvement in motor adaptation tasks, in which adaptation occurs in a non-error-based manner. In such tasks, there is a continuum of possible actions, each represented by a complex neuronal activity pattern. We extended the classical concept of the two-pathway BG by creating a model of BG interacting with a movement execution system, which allows for an arbitrary number of possible actions. The model includes sensory and premotor cortices, BG, a spinal cord network, and a virtual mechanical arm performing 2D reaching movements. The arm is composed of 2 joints (shoulder and elbow) controlled by 6 muscles (4 mono-articular and 2 bi-articular). The spinal cord network contains motoneurons, controlling the muscles, and sensory interneurons that receive afferent feedback and mediate basic reflexes. Given a specific goal-oriented motor task, the BG network through reinforcement learning constructs a behavior from an arbitrary number of basic actions represented by cortical activity patterns. Our study confirms that, with slight modifications, the classical two-pathway BG concept is consistent with results of previous studies, including non-error based motor adaptation experiments, pharmacological manipulations with BG nuclei, and functional deficits observed in BG-related motor disorders.

Highlights

  • The basal ganglia (BG), located in the deep forebrain of most vertebrates, have a complex structure with multiple connected sub-nuclei: the striatum, the globus pallidus (GP), the subthalamic nucleus (STN), and the substantia nigra

  • These projections are plastic; their efficacy changes depending on dopaminergic inputs from Substantia Nigra pars compacta (SNc), which code the reward prediction error and underlie positive or negative reinforcement of the action (Frank, 2005)

  • For the Parkinson’s Diseases (PD) condition, which manifests itself by dopamine deficiency, we reduced the learning rates λ1, λ2 in D1 and D2 medium spiny neurons (MSN) by 90%

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Summary

Introduction

The basal ganglia (BG), located in the deep forebrain of most vertebrates, have a complex structure with multiple connected sub-nuclei: the striatum, the globus pallidus (GP), the subthalamic nucleus (STN), and the substantia nigra. The GABAergic internal GP (GPi) and substantia nigra pars reticulata (SNr) serve as the output nuclei of BG which send inhibitory projections to the thalamus and to other various brain regions (Hikosaka et al, 1993; Weyand and Gafka, 1998; Takakusaki et al, 2003, 2004; Liu and Basso, 2008). The output of the BG network depends on synaptic projections from the cortex to medium spiny neurons (MSN) in the striatum. These projections are plastic; their efficacy changes depending on dopaminergic inputs from Substantia Nigra pars compacta (SNc), which code the reward prediction error and underlie positive or negative reinforcement of the action (Frank, 2005)

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