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Solving NP-Complete Problems by Spiking Neural P Systems with Budding Rules

  • Tseren-Onolt Ishdorj
  • Alberto Leporati
  • Linqiang Pan
  • Jun Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5957)

Abstract

Inspired by the growth of dendritic trees in biological neurons, we introduce spiking neural P systems with budding rules. By applying these rules in a maximally parallel way, a spiking neural P system can exponentially increase the size of its synapse graph in a polynomial number of computation steps. Such a possibility can be exploited to efficiently solve computationally difficult problems in deterministic polynomial time, as it is shown in this paper for the NP-complete decision problem sat.

Keywords

Polynomial Time Leaf Node Output Neuron Computation Step Boolean Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tseren-Onolt Ishdorj
    • 1
  • Alberto Leporati
    • 2
  • Linqiang Pan
    • 3
    • 4
  • Jun Wang
    • 3
  1. 1.Department of Information TechnologiesComputational Biomodelling Laboratory, Åbo Akademi UniversityTurkuFinland
  2. 2.Dipartimento di Informatica, Sistemistica e ComunicazioneUniversità degli Studi di Milano – BicoccaMilanoItaly
  3. 3.Key Laboratory of Image Processing and Intelligent Control, Department of Control Science and EngineeringHuazhong University of Science and TechnologyHubeiPeople’s Republic of China
  4. 4.Research Group on Natural Computing, Department of CS and AIUniversity of SevillaSevillaSpain

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