Two papers available on arm movements

[Lina Massone]

The following two papers are available from the neuroprose archive. The
papers are currently Technical Reports of the Neural Information Processing
Laboratory of Northwestern University and have been submitted for
publication.

ftp-host:archive.cis.ohio-state.edu
ftp-file: massone.arm_model.ps.Z


	A Neural Network Model of an Anthropomorphic Arm

	    Lina L.E. Massone and Jennifer D. Myers



			   Abstract

This paper introduces a neural network model of a planar redundant arm
whose structure and operation principles were inspired by those of
the human arm. We developed the model for two purposes.
One purpose was to study the relative role of control strategies and
plant properties in trajectory formation, namely which features of
simple arm movements can be attributed to the properties of the plant
alone. We address this matter in a companion paper [the next paper].
The second purpose was a motor-learning one: to design an arm model that,
because of its neural-network quality, can be eventually
incorporated in a parallel distributed learning scheme for
the arm controller. We modeled the arm with two joints (shoulder
and elbow) and six muscle-like actuators: a pair of
antagonist shoulder muscles, a pair of antagonist elbow muscles and a
pair of antagonist double-joint muscles. The arm was allowed to move in the
horizontal plane subject to the action of gravity. The model computes
the transformation between the control signals that activate the muscle-like 
actuators and the coordinates of the arm endpoint. This transformation
comprises four interacting stages (muscle dynamics, joint geometry,
forward arm dynamics, forward arm kinematics) that we modeled with a number
of feedforward and recurrent neural networks. In this paper we
introduce and describe in detail the modeling methods, that are
efficient, highly flexible (some of the resulting networks can be easily
modified to accommodate different parametric choices and temporal scales),
and quite general and hence applicable to a number of different
scientific domains.

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ftp-host: archive.cis.ohio-state.edu
ftp-file: massone.plant_properties.ps.Z


	     A Study of the Role of Plant Properties
	     		     in
		    Arm Trajectory Formation

 	    Lina L.E. Massone and Jennifer D. Myers


			Abstract

This paper describes the response of a neural-network model of
an anthropomorphic arm to various patterns
of activation of the arm muscles.  The arm model was introduced and
described in detail in [the previous paper].  The purpose of the
simulation experiments presented here is to study the relative role
of control strategies and plant properties in trajectory formation,
namely which features of simple arm movements can be attributed to
the properties of the plant alone -- a study that might provide some
guidelines for the design of artificial arms.  Our simulations
demonstrate the performance of the model at steady-state, what
movements the model produces in response to various activations of
its muscles, and the generalization abilities of the recurrent
neural network that implements the forward dynamic transformation.
The results of our simulations emphasize the role of the intrinsic
properties of the plant in generating movements with anthropomorphic
qualities such as smoothness and unimodal velocity profiles and
demonstrate that the task of an eventual controller for such an arm
could be simply that of programming the amplitudes and durations of
steps of neural input without considering additional motor details.
Our findings are relevant to the design of artificial arms and, with
some caveats, to the study of brain strategies in the arm motor system.

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