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Matrix Representation of Spiking Neural P Systems

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Membrane Computing (CMC 2010)

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

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Abstract

Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes. In this work, a discrete structure representation of SN P systems with extended rules and without delay is proposed. Specifically, matrices are used to represent SN P systems. In order to represent the computations of SN P systems by matrices, configuration vectors are defined to monitor the number of spikes in each neuron at any given configuration; transition net gain vectors are also introduced to quantify the total amount of spikes consumed and produced after the chosen rules are applied. Nondeterminism of the systems is assured by a set of spiking transition vectors that could be used at any given time during the computation. With such matrix representation, it is quite convenient to determine the next configuration from a given configuration, since it involves only multiplication and addition of matrices after deciding the spiking transition vector.

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References

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Author information

Authors and Affiliations

  1. Image Processing and Intelligent Control Key Laboratory of Education Ministry, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China

    Xiangxiang Zeng & Linqiang Pan

  2. Department of Computer Science, (Algorithms and Complexity), University of the Philippines, Diliman, 1101, Quezon City, Philippines

    Henry Adorna

  3. Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, University of Sevilla, Avda. Reina Mercedes s/n, 41012, Sevilla, Spain

    Miguel Ángel Martínez-del-Amor & Mario J. Pérez-Jiménez

Authors
  1. Xiangxiang Zeng
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  2. Henry Adorna
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  3. Miguel Ángel Martínez-del-Amor
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  4. Linqiang Pan
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  5. Mario J. Pérez-Jiménez
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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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© 2010 Springer-Verlag Berlin Heidelberg

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Zeng, X., Adorna, H., Martínez-del-Amor, M.Á., Pan, L., Pérez-Jiménez, M.J. (2010). Matrix Representation of 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_29

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  • DOI: https://doi.org/10.1007/978-3-642-18123-8_29

  • 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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