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Local Homogeneous Weighted Spiking Neural P Systems

  • Mengmeng Liu
  • Feng QiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10745)

Abstract

Homogeneous Spiking Neural P Systems (HSN P systems, for short) are a class of neural-like computing models in membrane computing, which are inspired by neurons that they are “designed” by nature to have the same “set of rules”, “working” in a uniform way to transform input into output. HSN P systems can be converted to weighted homogeneous SNP systems. In this work, based on the above two known systems, we consider a restricted variant of SN P systems called local homogeneous weighted SN P systems (LHWSN P systems, for short), where neurons in same module have the same set of rules. As a result, we prove that such systems can achieve Turing completeness. Specifically, it is proved that using only standard spiking rules is sufficient to compute and accept the family of sets of Turing computable natural numbers, moreover local homogeneity reduces the time required for the execution of the system.

Keywords

Spiking Neural P system Weight Local homogeneous 

Notes

Acknowledgment

This work was supported by the Natural Science Foundation of China (No. 61502283). Natural Science Foundation of China (No. 61472231). Natural Science Foundation of China (No. 61640201).

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.School of Management Science and EngineeringShandong Normal UniversityJinanChina

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