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Modeling Biological Neural Networks

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Abstract

In recent years, many new experimental studies on emerging phenomena in neural systems have been reported. The high efficiency of living neural systems to encode, process, and learn information has stimulated an increased interest among theoreticians in developing mathematical approaches and tools to model biological neural networks. In this chapter we review some of the most popular models of neurons and neural networks that help us understand how living systems perform information processing. Beyond the fundamental goal of understanding the function of the nervous system, the lessons learned from these models can also be used to build bio-inspired paradigms of artificial intelligence and robotics.

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Authors and Affiliations

  1. Institute “Carlos I” for Theoretical and Computational Physics and Department of Electromagnetism and Matter Physics, Facultad de Ciencias, Universidad de Granada, Granada, Spain

    Joaquin J. Torres

  2. Departamento de Ingeniería Informática, Universidad Autónoma de Madrid, Madrid, Spain

    Pablo Varona

Authors
  1. Joaquin J. Torres
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  2. Pablo Varona
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Editors and Affiliations

  1. LIACS, Leiden University, Leiden, The Netherlands

    Grzegorz Rozenberg

  2. Computer Science Department, University of Colorado, Boulder, USA

    Grzegorz Rozenberg

  3. LIACS, Leiden University, Leiden, The Netherlands

    Thomas Bäck

  4. LIACS, Leiden University, Leiden, The Netherlands

    Joost N. Kok

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Torres, J.J., Varona, P. (2012). Modeling Biological Neural Networks. In: Rozenberg, G., Bäck, T., Kok, J.N. (eds) Handbook of Natural Computing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92910-9_17

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