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An improved universal spiking neural P system with generalized use of rules

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Abstract

Taken inspiration from biological phenomenon that neurons communicate via spikes, spiking neural P systems (SN P systems, for short) are a class of distributed and parallel computing devices. So far firing rules in most of the SN P systems usually work in a sequential way or in an exhaustive way. Recently, a combination of the two ways mentioned above is considered in SN P systems. This new strategy of using rules, which is called a generalized way of using rules, is applicable for both firing rules and forgetting rules. In SN P systems with generalized use of rules (SNGR P systems, for short), if a rule is used at some step, it can be applied any possible number of times, nondeterministically chosen. In this work, the computational completeness of SNGR P systems is investigated. Specifically, a universal SNGR P system is constructed, where each neuron contains at most 5 rules, and for each time each firing rule consumes at most 6 spikes and each forgetting rule removes at most 4 spikes. This result makes an improvement regarding to these related parameters, thus provides an answer to the open problem mentioned in original work. Moreover, with this improvement we can use less resources (neurons and spikes involved in the evolution of system) to construct universal SNGR P systems.

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Funding

This work was supported by National Natural Science Foundation of China (61502063 and 61502004), Natural Science Foundation Project of CQ CSTC (cstc2018jcyjAX0057), Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN201800814), and Chongqing Social Science Planning Project (2017YBGL142).

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Authors and Affiliations

  1. Chongqing Engineering Laboratory for Detection, Control and Integrated Systems, Chongqing Technology and Business University, Chongqing, 400067, China

    Yun Jiang

  2. School of Artificial Intelligence, Chongqing Technology and Business University, Chongqing, 400067, China

    Yun Jiang & Fen Luo

  3. Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, 230039, China

    Yansen Su

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  1. Yun Jiang
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  2. Yansen Su
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  3. Fen Luo
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Correspondence to Yun Jiang.

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Jiang, Y., Su, Y. & Luo, F. An improved universal spiking neural P system with generalized use of rules. J Membr Comput 1, 270–278 (2019). https://doi.org/10.1007/s41965-019-00025-y

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  • DOI: https://doi.org/10.1007/s41965-019-00025-y

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