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Title

Can neural models of cognition benefit from the advantages of connectionism?

Authors

Sommer, Friedrich T.; Kanerva, Pentti

Abstract

Cognitive function certainly poses the biggest challenge for computational neuroscience. As we argue, past efforts to build neural models of cognition (the target article included) had too narrow a focus on implementing rule-based language processing. The problem with these models is that they sacrifice the advantages of connectionism rather than building on them. Recent and more promising approaches for modeling cognition build on the mathematical properties of distributed neural representations. These approaches truly exploit the key advantages of connectionism, that is, the high representational power of distributed neural codes and similarity-based pattern recognition. The architectures for cognitive computing that emerge from these approaches are neural associative memories endowed with additional mapping operations to handle invariances and to form reduced! representations of combinatorial structures.

Subjects

COGNITION; CONNECTIONISM; COMPUTATIONAL neuroscience; PATTERN perception; RESEMBLANCE (Philosophy); BRAIN mapping

Publication

Behavioral & Brain Sciences, 2006, Vol 29, Issue 1, p86

ISSN

0140-525X

Publication type

Academic Journal

DOI

10.1017/S0140525X06379022

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