Connectionist symbol processing: any progress?

[Jacques Sougne]

Hi,

I am working on modeling deductive reasoning using a distributed network of
spiking nodes. Variable binding is achieved by temporal synchrony while it
is not a new technique, the use of distributed representation, and the way
it solves the problem of multiple instantiation is new.
I called the model INFERNET

I obtained good results with conditional reasoning even with negated
conditional. all these forms:
	A=>B, A; A=>B, ~A; A=>B, B; A=>B, ~B
	A=>~B, A; A=>~B, ~A; A=>~B, ~B; A=>~B, B
	~A=>B, ~A; ~A=>B, A; ~A=>B, B; ~A=>B, ~B
	~A=>~B, ~A; ~A=>~B, A; ~A=>~B, ~B; ~A=>~B, B
The INFERNET performance fit human data which are sensitive to negation.
The effect of negation is often referred as negative conclusion bias.

I also worked on problem requiring multiple instantiations (see Sougne,
1998a; Sougne, 1998b). In INFERNET multiple instantiation is achieved by
using the neurobiological phenomena of period doubling. Nodes pertaining to
a doubly instantiated concept will sustain two oscillation.  This means
that these nodes will be able to synchronize with two different set of
nodes.
The INFERNET performance seems to fit human data for problems requiring
multiple instantiation like:
	Mark loves Helen and Helen loves John. Who is jealous of whom?

Due to distributed representation I also found an effect of similarity of
the concepts used in deductive tasks which are confirmed by empirical
evidences.

I also found an interesting effect of noise. When white noise is added in
the system (and if it is not too important) the performance of the system
is improved. This phenomenon is known as Stochastic resonance (see Levin &
Miller 1996, Sougne, 1998b).


Description of my work can be found in:

Sougne, J. (1996). A Connectionist Model of Reflective Reasoning Using
Temporal Properties of Node Firing. Proceedings of the Eighteenth Annual
Conference of the Cognitive Science Society. (pp. 666-671) Mahwah, NJ:
Lawrence Erlbaum Associates.

Sougn, J. (1998). Connectionism and the problem of multiple instantiation.
Trends in Cognitive Sciences, 2, 183-189.

Sougn, J. (1998). Period Doubling as a Means of Representing Multiply
Instantiated Entities. Proceedings of the twentieth Annual Conference of
the Cognitive Science Society. (pp. 1007-1012) Mahwah, NJ: Lawrence Erlbaum
Associates.

Sougne, J. and French, R. M. (1997). A Neurobiologically Inspired Model of
Working Memory Based on Neuronal Synchrony and Rythmicity. In J. A.
Bullinaria, D. W Glasspool, and G. Houghton (Eds.) Proceedings of the
Fourth Neural Computation and Psychology Workshop:  Connectionist
Representations.  London:  Springer-Verlag.

some preliminary versions are available at:
http://www.fapse.ulg.ac.be/Lab/Trav/jsougne.html

Some of the recently collected data are not yet published, if you are
interested, you can contact me. j.sougne at ulg.ac.be

References

Sougn, J. (1998a). Connectionism and the problem of multiple
instantiation. Trends in Cognitive Sciences, 2, 183-189.

Sougn, J. (1998b). Period Doubling as a Means of Representing Multiply
Instantiated Entities. Proceedings of the twentieth Annual Conference of
the Cognitive Science Society. (pp. 1007-1012) Mahwah, NJ: Lawrence Erlbaum
Associates.

Levin, J. E. and Miller, J. P. (1996). Broadband neural encoding in the
cricket cercal sensory system enhanced by stochastic resonance. Nature,
380, 165-168.


Jacques