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Chapter 10
Neural Network Modeling of Voluntary
Single-Joint Movement Organization I. Normal
Conditions
Vassilis Cutsuridis
Abstract
Motor learning and motor control have been the focus of intense study
by researchers from various disciplines. The neural network model approach has
been very successful in providing theoretical frameworks on motor learning and
motor control by modeling neural and psychophysical data from multiple levels of
biological complexity. Two neural network models of voluntary single-joint move-
ment organization under normal conditions are summarized here. The models seek
to explain detailed electromyographic data of rapid single-joint arm movement and
identify their neural substrates. The models are successful in predicting several char-
acteristics of voluntary movement.
10.1 Introduction
Voluntary movements are goal-directed movements triggered either by internal or
external cues. Voluntary movements can be improved with practice as one learns
to anticipate and correct for environmental obstacles that perturb the body. Single-
joint rapid (ballistic) movements are goal-directed movements performed in a single
action, without the need for corrective adjustments during its course. They are char-
acterized by a symmetric bell-shaped velocity curve, where the acceleration (the
time from the start to the peak velocity) and deceleration (the time from the peak
velocity to the end of movement) times are equal [3]. Similar velocity profiles have
also been observed in multi-joint movements [11].
The electromyographic (EMG) pattern of single-joint rapid voluntary movements
in normal subjects is also very characteristic. It is characterized by alternating bursts
of agonist and antagonist muscles [28]. The first agonist burst provides the impulsive
force for the movement, whereas the antagonist activity provides the braking force
