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International Conference on Membrane Computing

CMC 2010: Membrane Computing pp 348–360Cite as

Polynomial Complexity Classes in Spiking Neural P Systems

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6501))

Abstract

We study the computational potential of spiking neural (SN) P systems. several intractable problems have been proven to be solvable by these systems in polynomial or even constant time. We study first their formal aspects such as the input encoding, halting versus spiking, and descriptional complexity. Then we establish a formal platform for complexity classes of uniform families of confluent recognizer SN P systems. Finally, we present results characterizing the computational power of several variants of confluent SN P systems, characterized by classes ranging from P to PSPACE.

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Author information

Authors and Affiliations

  1. Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Campus de Montegancedo s/n, Boadilla del Monte, Madrid, 28660, Spain

    Petr Sosík & Alfonso Rodríguez-Patón

  2. Institute of Computer Science, Faculty of Philosophy and Science, Silesian University in Opava, 74601, Opava, Czech Republic

    Petr Sosík & Lucie Ciencialová

Authors
  1. Petr Sosík
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  2. Alfonso Rodríguez-Patón
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  3. Lucie Ciencialová
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Sheffield, Regent Court, Portobello Street, S1 4DP, Sheffield, UK

    Marian Gheorghe

  2. Department of Bioinformatics, School of Biology and Pharmacy, Friedrich Schiller University, Ernst-Abbe-Platz 1–4, 07743, Jena, Germany

    Thomas Hinze

  3. Institute of Mathematics of the Romanian Academy, P.O. Box 1-764, 014700, Bucureşti, Romania

    Gheorghe Păun

  4. Leiden Center of Advanced Computer Science (LIACS), Universiteit Leiden, Niels Bohrweg 1, 2333, CA Leiden, The Netherlands

    Grzegorz Rozenberg

  5. Turku Centre for Computer Science (TUCS), Leminkäisenkatu 14, 20520, Turku, Finland

    Arto Salomaa

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Sosík, P., Rodríguez-Patón, A., Ciencialová, L. (2010). Polynomial Complexity Classes in Spiking Neural P Systems. In: Gheorghe, M., Hinze, T., Păun, G., Rozenberg, G., Salomaa, A. (eds) Membrane Computing. CMC 2010. Lecture Notes in Computer Science, vol 6501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18123-8_27

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  • DOI: https://doi.org/10.1007/978-3-642-18123-8_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-18122-1

  • Online ISBN: 978-3-642-18123-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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