Aside from the previous question of deciding on network structure,
there are several other questions that remain to be studied in learning
networks.
There is the question of requiring more than a single output from a
network. If, say, two outputs are required for a given input, one
+1 and the other -1, this runs into conflict with the incrementing
process. Changes that aid one output may act against the other.
Apparently the searching process depicted before with a varying bias
must be considerably refined to find weight changes which act on
all the outputs in the required way. This is far from an academic
question because there will undoubtedly be numerous cases in which
the greatest part of the input-output computation will have shared
features for all output variables. Only at later levels do they need to
be differentiated. Hence it is necessary to envision a single network
producing multiple outputs rather than a separate network for each
output variable if full efficiency is to be achieved.
Another related question is that of using input variables that are
either many-, or continuous-, valued rather than two-valued. No
fundamental difficulties are discernible in this case, but the matter
deserves some considerable study and experimentation.
Another important question involves the use of a succession of inputs
for producing an output. That is, it may be useful to allow time to
enter into the network’s logical action, thus giving it a “dynamic” as
well as “static” capability.