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to halt the limb. Sometimes a second agonist burst is needed to bring the limb to
the final position [1, 4, 5, 6, 23, 24, 25, 26, 27, 36]. The combination of the agonist-
antagonist-agonist bursts is known as the triphasic pattern of muscle activation [28].
An excellent review on the properties of the triphasic pattern of muscle activation
and the produced movement under different experimental conditions can be found
in Berardelli and colleagues [2].
The origin of the triphasic pattern and whether it is controlled by the nervous sys-
tem has been long debated [33]. In a review paper by Berardelli and colleagues [2],
three conclusions were made: (1) the basal ganglia output plays a role in the scaling
of the first agonist burst size, (2) the corticospinal tract has a role in determining
spatial and temporal recruitment of motor units, and (3) the proprioceptive feedback
is not necessary to the production of the triphasic pattern, but it contributes to the
accuracy of both the trajectory and the end point of ballistic movements. That means
that the origin of the triphasic pattern of muscle activation
may
be a central one, but
afferent inputs can also modulate the voluntary activity.
10.2 Models and Theories of Motor Control
Motor learning and motor control have been the focus of intense study by researchers
from various disciplines. The experimental researchers interested in motor learning
investigate how practice facilitates skill acquisition and improvement. The theoret-
ical/computational researchers interested in motor control have investigated which
movement variables are controlled during movement from the nervous system [33].
Many computational models of motor control have been advanced over the years
[14]. These models include the equilibrium point hypothesis [20], dynamical sys-
tem theory [32], the pulse-step model [22], the impulse-timing model [35], the dual-
strategy hypothesis [14], models about minimizing movement variables [34], and
neural network models [8, 9, 10, 13, 17, 15, 16, 18].
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. In partic-
ular, the vector integration to endpoint (VITE) and factorization of muscle length
and muscle tension (FLETE) neural network models of Bullock, Grossberg, and
colleagues [7,8,9,10,13] have provided qualitative answers to questions such as how
can a limb be rotated to and stabilized at a desired angle? How can movement speed
from an initial to a desired final angle be controlled under conditions of low joint
stiffness? How can launching and braking forces be generated to compensate from
inertial loads? The VITE model was capable of generating single-joint arm move-
ments, whereas the FLETE model afforded independent voluntary control of joint
stiffness and joint position, and incorporated second-order dynamics, which played
a large role in realistic limb movements. Variants of the FLETE model [9] have been
successful in producing realistic transient muscle activations, such as the triphasic
pattern of muscle activation observed during rapid, self-terminated movements.
