No subject

memories.  But neither of these viewpoints constitutes AI in any real
sense.  There is a famous saying by Nietzsche that might be adapted to
describe the current status of neural networks: Machines "do not
become thinkers simply because their memories are too good."

Yet there are other aspects of neural networks that have been
extremely important.  Thus the structural paradigm is of obvious value
to the neurophysiologist, the cognitive scientist, and the vision
researcher.  It would be of value to the computer science community if
Information Sciences were to review and critique the original promise
of neurocomputing in the light of developments in the past few years.

The Special Issue of Information Sciences will do just this.  It will
provide reviews of this link between neural networks and AI.  In other
words, the scope of this \fIIssue\fR is much broader than that of the
most commonly encountered applications of associative memories or
mapping networks.  The application areas that the \fIIssue\fR will
deal with include neural logic programming, feature detection,
knowledge representation, search techniques, and learning.  The
connectionist approach to AI will be contrasted from the traditional
symbolic techniques.

Deadline for Submissions:  September 30, 1991

Papers may be sent to:

Subhash Kak
Guest Editor, Information Sciences
Department of Electrical and Computer Engineering
Louisiana State University
Baton Rouge, LA 70803-5901, USA

Tel: (504) 388-5552
E-mail: kak at max.ee.lsu.edu