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Polynomial Complexity Classes in Spiking Neural P Systems

  • Petr Sosík
  • Alfonso Rodríguez-Patón
  • Lucie Ciencialová
Part of the Lecture Notes in Computer Science book series (LNCS, 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Petr Sosík
    • 1
    • 2
  • Alfonso Rodríguez-Patón
    • 1
  • Lucie Ciencialová
    • 2
  1. 1.Departamento de Inteligencia Artificial, Facultad de InformáticaUniversidad Politécnica de MadridBoadilla del MonteSpain
  2. 2.Institute of Computer Science, Faculty of Philosophy and ScienceSilesian University in OpavaOpavaCzech Republic

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