Chapter 2. Prehension

98 THE PHASES OF PREHENSION
were in closer correspondence to the actual muscle settings. Thus, the
actual arm muscle settings are acting as a teacher. And while there is
no external teacher to make this correlation, there had to be some
outside entity that pointed the eye at a given location. In order to
accomplish this, he used a high contrast mechanism: the eyes looked at
the highest contrast place in space, which in this case, was a marker
on the wrist.
Kuperstein’s model demonstrates how a computation might be
done in the brain to relate visual locations in space to the muscle activ-
ity needed to produce a goal arm configuration. Note that he did not
actually use the network to place the arm at any one location.
Evidence for such visually guided behavior emerging from the rela-
tionship of visual stimulation to self-produced movements has been
demonstrated in cats (Hein, 1974). One problem with his model is in
the nature of the problem he is trying to solve. Relating goal locations
to muscle commands requires an inverse kinematic computation. The
inverse problem is ill-posed because there are many solutions for arm
configurations at a given goal location; his robot will only learn one of
these configurations.
Other styles of neural networks are possible. Iberall(1987a) pre-
sented a computational model for the transport component using a
non-adaptive neural network, called the Approach Vector Selector
model. To compute the wrist location goal, a pair of two-dimensional
neural networks interacted with two one-dimensional layers, following
AmadArbib cooperative/competitive models (Amari & Arbib, 1977).
As seen in Figure 4.14, Iberall used a pair of two-dimensional
excitatory networks to represent the space around the object in polar
coordinates (distance r, orientation f) relative to the object’s opposition
vector. Each node in the excitatory neural networks was a location in
this task space. These nodes competed with each other in order to at-
tract the wrist to a good location for contacting the dowel by two vir-
tual fingers. The analysis for VF1 and VF2 was separated, that is, one
network represents the perspective of VF1, the other represents that
for VF2. Two inhibitory processes interact with these (not shown),
allowing gradual build up of an agreed upon solution between the
nets. As a ‘winner take all’ network, possible wrist location solutions
compete, with distances and orientations outside the range of the
opposition space dying out, and VF configurations that correspond to
an effective posture for the given opposition vector reinforcing each
other. If the parameters are properly chosen, the AmWArbib model
predicts that the field wil reach a state of equilibrium, where excitation
is maintained at only one location.