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Learning and Generalization in Random Automata Networks

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Bio-Inspired Models of Network, Information, and Computing Systems (BIONETICS 2010)

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Abstract

It has been shown [7,6] that feedforward Boolean networks can learn to perform specific simple tasks and generalize well if only a subset of the learning examples is provided for learning. Here, we extend this body of work and show experimentally that random Boolean networks (RBNs), where both the interconnections and the Boolean transfer functions are chosen at random initially, can be evolved by using a state-topology evolution to solve simple tasks. We measure the learning and generalization performance, investigate the influence of the average node connectivity K, the system size N, and introduce a new measure that allows to better describe the network’s learning and generalization behavior. Our results show that networks with higher average connectivity K (supercritical) achieve higher memorization and partial generalization. However, near critical connectivity, the networks show a higher perfect generalization on the even-odd task.

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Author information

Authors and Affiliations

  1. Computer Science and Systems Science Department, Portland State University (PSU), Portland, OR, USA

    Alireza Goudarzi

  2. Department of Electrical and Computer Engineering, Portland State University (PSU), Portland, OR, USA

    Christof Teuscher

  3. Department of Cellular and Molecular Pharmacology, University of California, San Francisco (UCSF), CA, USA

    Natali Gulbahce

Authors
  1. Alireza Goudarzi
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  2. Christof Teuscher
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  3. Natali Gulbahce
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Massachusetts, 100 Morrissey Blvd, 02125, Boston, MA, USA

    Junichi Suzuki

  2. Graduate School of Engineering, 2-1 Yamada-oka, Suita, 565-0871, Osaka, Japan

    Tadashi Nakano

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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering

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Goudarzi, A., Teuscher, C., Gulbahce, N. (2012). Learning and Generalization in Random Automata Networks. In: Suzuki, J., Nakano, T. (eds) Bio-Inspired Models of Network, Information, and Computing Systems. BIONETICS 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32615-8_19

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  • DOI: https://doi.org/10.1007/978-3-642-32615-8_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32614-1

  • Online ISBN: 978-3-642-32615-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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