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Local Homogeneous Weighted Spiking Neural P Systems

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Human Centered Computing (HCC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10745))

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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.

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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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Authors and Affiliations

  1. School of Management Science and Engineering, Shandong Normal University, No. 88 East of Wenhua Road, Jinan, 250014, Shandong, China

    Mengmeng Liu & Feng Qi

Authors
  1. Mengmeng Liu
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  2. Feng Qi
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Corresponding author

Correspondence to Feng Qi .

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Editors and Affiliations

  1. Wuhan University of Technology, Wuhan, China

    Qiaohong Zu

  2. Fujitsu Laboratories of Europe Ltd., Hayes, United Kingdom

    Bo Hu

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Liu, M., Qi, F. (2018). Local Homogeneous Weighted Spiking Neural P Systems. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2017. Lecture Notes in Computer Science(), vol 10745. Springer, Cham. https://doi.org/10.1007/978-3-319-74521-3_5

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  • DOI: https://doi.org/10.1007/978-3-319-74521-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74520-6

  • Online ISBN: 978-3-319-74521-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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