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:  October 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