Dynamic Binding

[john kolen]

As Graham Smith writes:

> It strikes me that an obvious solution to the binding problem has been
> overlooked in our rush to study phase locking in oscillatory networks. ;-)

He's right.

> I hope to publish the above-mentioned work but before doing so I shall be
> grateful for some feedback either to reassure myself that there is
> something here worth publishing or to spare my blushes with a wider
> audience.

A wider audience?  Those who read connectionists (1000+ ??, only DT knows
for sure) are pretty much the ones who really count.

Before you do publish this result, check the proceedings of the last CogSci
Society meeting.  Janet Wiles presented a similar variable binding method
using autoassociative (aa) encoders.  She found that strict aa mappings are
too difficult to learn for very large networks.  In response, the strict aa
mapping was replaced by a mapping of 1-in-n slots to n/2-in-n slots.  The
representational shift helped learning tremendously, as the std encoder
network can easily produce this mapping.

The citation for this paper is:

Wiles, J., (1993) "Representation of variables and their values in neural
networks", In Proceedings of the Fifteenth Annual Meeting of the Cognitive
Science Society. June 18-21, 1993. Boulder, CO. Lawrence Erlbaum
Associates, Hillsdale, NJ.

I've sent this to connectionists, rather than individually to Graham Smith,
because I think the Wiles paper was perhaps the best all-around neural
network paper at CogSci this year.

John