Models of basal ganglia and cerebellum for sensorimotor integration and predictive control

Marwan A. Jabri; Jerry Huang; Olivier J.-M. D. Coenen; Terrence J. Sejnowski

doi:10.1117/12.403711

16 October 2000 Models of basal ganglia and cerebellum for sensorimotor integration and predictive control

Marwan A. Jabri, Jerry Huang, Olivier J.-M. D. Coenen, Terrence J. Sejnowski

Proceedings Volume 4196, Sensor Fusion and Decentralized Control in Robotic Systems III; (2000) https://doi.org/10.1117/12.403711
Event: Intelligent Systems and Smart Manufacturing, 2000, Boston, MA, United States

Abstract

This paper presents a sensorimotor architecture integrating computational models of a cerebellum and a basal ganglia and operating on a microrobot. The computational models enable a microrobot to learn to track a moving object and anticipate future positions using a CCD camera. The architecture features pre-processing modules for coordinate transformation and instantaneous orientation extraction. Learning of motor control is implemented using predictive Hebbian reinforcement-learning algorithm in the basal ganglia model. Learning of sensory predictions makes use of a combination of long-term depression (LTD) and long-term potentiation (LTP) adaptation rules within the cerebellum model. The basal ganglia model uses the visual inputs to develop sensorimotor mapping for motor control, while the cerebellum module uses robot orientation and world- coordinate transformed inputs to predict the location of the moving object in a robot centered coordinate system. We propose several hypotheses about the functional role of cell populations in the cerebellum and argue that mossy fiber projections to the deep cerebellar nucleus (DCN) could play a coordinate transformation role and act as gain fields. We propose that such transformation could be learnt early in the brain development stages and could be guided by the activity of the climbing fibers. Proprioceptor mossy fibers projecting to the DCN and providing robot orientation with respect to a reference system could be involved in this case. Other mossy fibers carrying visual sensory input provide visual patterns to the granule cells. The combined activities of the granule and the Purkinje cells store spatial representations of the target patterns. The combinations of mossy and Purkinje projections to the DCN provide a prediction of the location of the moving target taking into consideration the robot orientation. Results of lesion simulations based on our model show degradations similar to those reported in cerebellar lesion studies on monkeys.

Citation Download Citation

Marwan A. Jabri, Jerry Huang, Olivier J.-M. D. Coenen, and Terrence J. Sejnowski "Models of basal ganglia and cerebellum for sensorimotor integration and predictive control", Proc. SPIE 4196, Sensor Fusion and Decentralized Control in Robotic Systems III, (16 October 2000); https://doi.org/10.1117/12.403711

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available