Accession Number : ADA304256

Title :   Connectionist Modeling of Basal Ganglia Motor Circuitry.

Descriptive Note : Final rept. 1 Dec 91-30 Sep 95,

Corporate Author : EMORY UNIV ATLANTA GA SCHOOL OF MEDICINE

Personal Author(s) : Alexander, Garrett E.

PDF Url : ADA304256

Report Date : 31 JAN 1996

Pagination or Media Count : 9

Abstract : Using a self-organizing, topology-preserving, sensorimotor architecture, we developed two types of neural networks that were capable of learning, without supervision, to control a simulated, three-segment robot arm with variable degrees of freedom (3,4 or 6 df). One type was an endpoint or posture-controlling network, and the other was a trajectory controller. The hidden layers in these networks consisted of both 2D and 3D lattices comprising from 729 to 1728 neurons. Through process of trial and error, all networks learned to control the positioning of th end of the robot arm within a 3D workspace. The workspace was either a hemisphere or a cube centered at the origin of the stimulated limb. When tested after training that ranged from 2000 to 12000 trials, both networks achieved relatively uniform placement accuracy throughout the workspace, the level of accuracy varying directly with the number of processing elements and asymptotically with the duration of training. The number of trials required to achieve maximum accuracy was approximately 5 times the number of neurons in the hidden layers.

Descriptors :   *NEURAL NETS, *HUMAN FACTORS ENGINEERING, *NERVE CELLS, *MOTORS, *MOTOR NEURONS, *GANGLIA, CONTROL, STIMULATION(GENERAL), TRAINING, ROBOTS, ACCURACY, PROCESSING EQUIPMENT, TIME, VARIABLES, DEGREES OF FREEDOM, EMPLACEMENT, CIRCUITS, TRAJECTORIES, SUPERVISION, EXTREMITIES.

Subject Categories : Anatomy and Physiology
      Human Factors Engineering & Man Machine System

Distribution Statement : APPROVED FOR PUBLIC RELEASE