It is shown how
`static' neural approaches to adaptive target detection
can be replaced by a more efficient and more
sequential alternative.
The latter is inspired by the
observation that biological systems employ sequential eye-movements for
pattern recognition.
A system is described which builds an adaptive model
of the time-varying inputs of an artificial fovea controlled by
an adaptive neural controller. The controller
uses the adaptive model
for
learning the sequential
generation of fovea trajectories causing
the fovea to move to a target in a visual scene.
The system also learns to track moving targets.
No teacher provides the desired activations of `eye-muscles'
at various times. The only goal information is the shape of the target.
Since the
task is a `reward-only-at-goal' task , it involves a
complex temporal credit assignment problem.
Some implications for adaptive
attentive systems in general are discussed.