Share Email Print
cover

Proceedings Paper

Models of basal ganglia and cerebellum for sensorimotor integration and predictive control
Author(s): Marwan A. Jabri; Jerry Huang; Olivier J.-M. D. Coenen; Terrence J. Sejnowski
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 16 October 2000
PDF: 13 pages
Proc. SPIE 4196, Sensor Fusion and Decentralized Control in Robotic Systems III, (16 October 2000); doi: 10.1117/12.403711
Show Author Affiliations
Marwan A. Jabri, Oregon Graduate Institute of Science and Technology and Univ. of Sydney (United States)
Jerry Huang, Univ. of Sydney (Australia)
Olivier J.-M. D. Coenen, Children's Hospital Research Ctr. and Univ. of California/San Diego (United States)
Terrence J. Sejnowski, Salk Institute and Univ. of California/San Diego (United States)


Published in SPIE Proceedings Vol. 4196:
Sensor Fusion and Decentralized Control in Robotic Systems III
Gerard T. McKee; Paul S. Schenker, Editor(s)

© SPIE. Terms of Use
Back to Top