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On Neural Networks, Connectionism and Brain-Like Learning

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From Synapses to Rules
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Abstract

This paper argues that some of the ideas of connectionism are not only logically flawed, but that they are also inconsistent with some commonly observed human learning behavior. In addition, this paper attempts to define some common, externally observed, properties of the human learning process, properties that are common to all types of human learning. It is expected that any theory of learning should account for these common properties. Characterization of an autonomous learning system such as the brain has been one of the “large” missing pieces in connectionism and other brain-related sciences. The external characteristics of learning algorithms have never been defined in these fields. They largely pursued algorithm development from an “internal mechanisms” point of view. This paper is an attempt to rectify that situation.

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© 2002 Springer Science+Business Media New York

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Roy, A. (2002). On Neural Networks, Connectionism and Brain-Like Learning. In: Apolloni, B., Kurfess, F. (eds) From Synapses to Rules. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0705-5_16

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  • DOI: https://doi.org/10.1007/978-1-4615-0705-5_16

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-5204-4

  • Online ISBN: 978-1-4615-0705-5

  • eBook Packages: Springer Book Archive

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