Saccades are rapid, frequent eye movements that shift the fovea onto objects of interest. Several areas of the brain, including the frontal cortical areas, Lateral Intraparietal (LIP) cortex, Basal Ganglia (BG), Superior Colliculus (SC) and the brainstem reticular formation are believed to be involved in saccade generation. Models of saccade generation, however, tend to focus heavily on the determination of saccadic saliency in the Superior Colliculus and eye movement dynamics thereafter. The aim of this paper is to model the role played by the BG, particularly the switching function of the SubThalamic Nucleus (STN), in switching from a pattern of automated saccadic responses, to voluntary control of saccade generation. © 2010 IEEE.

Srinivasa Chakravarthy

Department Of Biotechnology

Maithreye Rengaswamy

BASAL GANGLIA

BRAINSTEM

computational model

CORTICAL AREAS

SUBTHALAMIC NUCLEUS

SUPERIOR COLLICULUS

VOLUNTARY CONTROLS

Boolean functions

Brain

Neural Networks

Switching functions

Eye movements

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IIT Madras

A study of the switching function of the Subthalamic Nucleus in saccade generation using a computational model of Basal Ganglia

Proceedings of the International Joint Conference on Neural Networks

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. © 2008 Massachusetts Institute of Technology.

Fulltext

Neural Computation

Understanding parkinsonian handwriting through a computational model of basal ganglia

We present a computational model of basal ganglia as a key player in exploratory behavior. The model describes exploration of a virtual rat in a simulated water pool experiment. The virtual rat is trained using a reward-based or reinforcement learning paradigm which requires units with stochastic behavior for exploration of the system's state space. We model the Subthalamic Nucleus-Globus Pallidus externa (STN-GPe) segment of the basal ganglia as a pair of neuronal layers with oscillatory dynamics, exhibiting a variety of dynamic regimes such as chaos, traveling waves and clustering. Invoking the property of chaotic systems to explore state-space, we suggest that the complex exploratory dynamics of STN-GPe system in conjunction with dopamine-based reward signaling from the Substantia, Nigra pars compacta (SNc) present the two key ingredients of a reinforcement learning system. © World Scientific Publishing Company.

International Journal of Neural Systems

The role of the basal ganglia in exploration in A neural model based on reinforcement learning

Journal of Neuroscience

Role for Subthalamic Nucleus Neurons in Switching from Automatic to Controlled Eye Movement

Modelling divided visual attention with a winner-take-all network

Journal of Neurophysiology

Predictive Reward Signal of Dopamine Neurons

A study of the switching function of the subthalamic nucleus in saccade generation using a computational model of basal ganglia

Journal	Proceedings of the International Joint Conference on Neural Networks
Open Access	No