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[Lina Massone]

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		    TECHNICAL REPORT AVAILABLE



	A NEURAL NETWORK MODEL FOR LIMB TRAJECTORY FORMATION

		Lina Massone and Emilio Bizzi
	    Dept. of Brain and Cognitive Sciences
	    Massachusetts Institute of Technology


  This paper deals with the problem of representing and generating
  unconstrained aiming movements of a limb by means of a neural network
  architecture. The network produced a time trajectory of a limb from a
  starting posture toward a target specified by a sensory stimulus. Thus 
  the network performed a sensory-motor transformation. The experimenters
  imposed a bell-shaped velocity profile on the trajectory. This type of
  profile is characteristic of most movements performed by biological systems.
  We investigated the generalization capabilities of the network as well as
  its internal organization. Experiments performed during learning and on
  the trained network showed that: (i) the task could be learned by a
  three-layer sequential network; (ii) the network successfully generalized
  in trajectory space and adjusted the velocity profiles properly; (iii) the
  same task could not be learned by a linear network; (iv) after learning,
  the internal connections became organized into inhibitory and excitatory
  zones and encoded the main features of the training set; (v) the model was
  robust to noise on the input signals; (vi) the network exhibited
  attractor-dynamics properties; (vii) the network was able to solve
  the motor-equivalence problem.
  A key feature of this work is the fact that the neural network was coupled
  to a mechanical model of a limb in which muscles are represented as springs. 
  With this representation the model solved the problem of motor redundancy.



A short version of this paper covering only part of the
described research was mailed in February to IJCNN.
The full report has been submitted to Biological Cybernetics.

All requests should be addressed to: lina at wheaties.ai.mit.edu