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The implementation of propositional logic in random neural networks

  • Computational Models of Neurons and Neural Nets
  • Conference paper
  • First Online:
From Natural to Artificial Neural Computation (IWANN 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 930))

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Abstract

Describing the evolution of cognition as an informational process, we want to compare the complexity and computational power of connectionist models of some cognitive functions to the maximal information transmitted in brain relevant genes during neocortex evolution.

In this paper we implement propositional logic as Boolean functions in neural networks and investigate what types of functions emerge in nets with specified architecture (feedforward vs. fully backcoupled) and random weights. For N=2,3 arguments the relative portions of Boolean functions are given as results of a Monte Carlo simulation.

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Author information

Authors and Affiliations

  1. Institut f. Theoret. Chemie, Technische Universität München, 85 747, Garching, Germany

    Marc-Denis Weitze & G. L. Hofacker

Authors
  1. Marc-Denis Weitze
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  2. G. L. Hofacker
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Editor information

José Mira Francisco Sandoval

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© 1995 Springer-Verlag Berlin Heidelberg

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Weitze, MD., Hofacker, G.L. (1995). The implementation of propositional logic in random neural networks. In: Mira, J., Sandoval, F. (eds) From Natural to Artificial Neural Computation. IWANN 1995. Lecture Notes in Computer Science, vol 930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59497-3_173

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  • DOI: https://doi.org/10.1007/3-540-59497-3_173

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-59497-0

  • Online ISBN: 978-3-540-49288-7

  • eBook Packages: Springer Book Archive

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