This paper is concerned with the problem of designing a network of
linear threshold elements capable of efficiently adapting its various
sets of weights so as to produce a prescribed input-output relation.
It is to accomplish this adaptation by being repetitively presented
with the various inputs along with the corresponding desired outputs.
We will not be concerned here with the further requirement of various
kinds of ability to “generalize”—_i.e._, to tend to give correct
outputs for inputs that have not previously occurred when they are
similar in some transformed sense to other inputs that have occurred.
In putting forth a model for such an adapting or “learning” network, a
requirement is laid down that the complexity of the adaption process
in terms of interconnections among elements needed for producing
appropriate weight changes, should not greatly exceed that already
required to produce outputs from inputs with a static set of weights.
In fact, it has been found possible to use the output-from-input
computing capacity of the network to help choose proper weight changes
by observing the effect on the output of a variety of possible weight
changes.
No attempt is made here to defend the proposed network model on
theoretical grounds since no effective theory is known at present.
Instead, the plausibility of the various aspects of the network model,
combined with empirical results must suffice.