Connectionist Learning/Representation: BBS Call for Commentators

[Stevan Harnad]

Below is the abstract of a forthcoming target article to appear in
Behavioral and Brain Sciences (BBS), an international,
interdisciplinary journal that provides Open Peer Commentary on important
and controversial current research in the biobehavioral and cognitive
sciences. Commentators must be current BBS Associates or nominated by a 
current BBS Associate. To be considered as a commentator on this article,
to suggest other appropriate commentators, or for information about how
to become a BBS Associate, please send email to:
    harnad at confidence.princeton.edu   harnad at pucc.bitnet     or write to:
BBS, 20 Nassau Street, #240, Princeton NJ 08542  [tel: 609-921-7771]
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WHAT CONNECTIONIST MODELS LEARN:
LEARNING AND REPRESENTATION IN CONNECTIONIST NETWORKS

Stephen J Hanson
Learning and Knowledge Acquisition Group
Siemens Research Center
Princeton NJ 08540

and

David J Burr
Artificial Intelligence and 
Communications Research Group
Bellcore
Morristown NJ 07960

Connectionist models provide a promising alternative to the traditional
computational approach that has for several decades dominated cognitive
science and artificial intelligence, although the nature of
connectionist models and their relation to symbol processing remains
controversial. Connectionist models can be characterized by three
general computational features: distinct layers of interconnected
units, recursive rules for updating the strengths of the connections
during learning, and "simple" homogeneous computing elements. Using just
these three features one can construct surprisingly elegant and
powerful models of memory, perception, motor control, categorization
and reasoning. What makes the connectionist approach unique is not its
variety of representational possibilities (including "distributed
representations") or its departure from explicit rule-based models,
or even its preoccupation with the brain metaphor. Rather, it is that
connectionist models can be used to explore systematically the complex
interaction between learning and representation, as we try to
demonstrate through the analysis of several large networks.