Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Reinforcement learning models of the dopamine system and their behavioral implications
Reinforcement learning models of the dopamine system and their behavioral implications
An oscillatory neuromotor model of handwriting generation
International Journal on Document Analysis and Recognition
A model of basal ganglia in saccade generation
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Modeling basal ganglia for understanding parkinsonian reaching movements
Neural Computation
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Handwriting in Parkinson's disease (PD) is typically characterized by micrographia, jagged line contour, and unusual fluctuations in pen tip velocity. Although PD handwriting features have been used for diagnostics, they are not based on a signaling model of basal ganglia (BG). In this letter, we present a computational model of handwriting generation that highlights the role of BG. When PD conditions like reduced dopamine and altered dynamics of the subthalamic nucleus and globus pallidus externa subsystems are simulated, the handwriting produced by the model manifested characteristic PD handwriting distortions like micrographia and velocity fluctuations. Our approach to PD modeling is in tune with the perspective that PD is a dynamic disease.