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Symbolic functions from neural computation.

Paul Smolensky¹

¹Cognitive Science Department, Johns Hopkins University, Baltimore, MD 21218, USA. smolensky@jhu.edu

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences

|June 20, 2012

Summary

This summary is machine-generated.

This research proposes a new computational approach to cognition, computing symbolic functions without directly processing symbols. This method uses numerical activation patterns in abstract neurons for a subsymbolic approach to cognitive science.

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Area of Science:

Cognitive Science
Computational Neuroscience
Artificial Intelligence

Background:

Traditional cognitive science models computation over symbols.
This approach faces limitations in explaining complex cognitive functions.

Purpose of the Study:

To develop a computational framework for cognition that bypasses direct symbol manipulation.
To explore subsymbolic computation for modeling cognitive processes.

Main Methods:

Encoding symbols as patterns of numerical activation across abstract neurons.
Performing massively parallel numerical computation within a continuous medium.
Developing an axiomatic framework with formal results.

Main Results:

Demonstrated computation of recursive symbolic functions using subsymbolic methods.
Specified formal languages via symbolic rewrite rules.
Achieved symbolic outputs reflecting both grammatical competence and statistical performance in language.

Conclusions:

Subsymbolic computation offers a viable alternative for modeling cognition.
This framework can account for both the structured and statistical aspects of human language.
The approach provides a new perspective on the computational theory of mind.