Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Actor-critic models of the basal ganglia: new anatomical and computational perspectives
Neural Networks - Computational models of neuromodulation
An integrative theory of the phasic and tonic modes of dopamine modulation in the prefrontal cortex
Neural Networks - Computational models of neuromodulation
Modeling the basal ganglia in the control of arm movements
Modeling the basal ganglia in the control of arm movements
A neural network model of Parkinson's disease bradykinesia
Neural Networks
What do the basal ganglia do? A modeling perspective
Biological Cybernetics
| Hi-index | 0.00 |
We present a computational model that highlights the role of basal ganglia (BG) in generating simple reaching movements. The model is cast within the reinforcement learning (RL) framework with correspondence between RL components and neuroanatomy as follows: dopamine signal of substantia nigra pars compacta as the temporal difference error, striatum as the substrate for the critic, and the motor cortex as the actor. A key feature of this neurobiological interpretation is our hypothesis that the indirect pathway is the explorer. Chaotic activity, originating from the indirect pathway part of the model, drives the wandering, exploratory movements of the arm. Thus, the direct pathway subserves exploitation, while the indirect pathway subserves exploration. The motor cortex becomes more and more independent of the corrective influence of BG as training progresses. Reaching trajectories show diminishing variability with training. Reaching movements associated with Parkinson's disease (PD) are simulated by reducing dopamine and degrading the complexity of indirect pathway dynamics by switching it from chaotic to periodic behavior. Under the simulated PD conditions, the arm exhibits PD motor symptoms like tremor, bradykinesia and undershooting. The model echoes the notion that PD is a dynamical disease.