DESIRED RESPONSES DO NOT CORRESPOND TO GOOD TEACHING INPUTS
                      IN ECOLOGICAL NEURAL NETWORKS

In this paper  we analyze  how supervised learning occurs in ecological neural
networks, i.e. networks that interact with an autonomous external environment.
Using an evolutionary method  for selecting  good  teaching inputs we show how
the  learning  process  interacts  with  the  capability  of  such networks to
partially determine  the  next  input  stimuli  with  their  motor outputs. We
surprisingly  find that  to  obtain  a  desired output X it is better to use a
teaching  input  different from X. To explain this fact we claim that teaching
inputs  in  ecological networks have  two different effects: (a) to reduce the
discrepancy between  the  actual output of the network and the teaching input,
(b)  to  modify  the  network  behavior  and,  as a consequence, the network's
learning experiences. Evolved teaching inputs appear to represent a compromise
between these two needs. We finally show that evolved teaching inputs that are
allowed  to  change  during  the  learning  process  function  differently  at
different stages  of  learning, first giving more weight to (b) and, later on,
to (a).