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Sequential dynamic threshold neural P systems

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Abstract

Dynamic threshold neural P systems (DTNP systems, for short) are a kind of distributed parallel computing systems abstracted from the spiking and dynamic threshold mechanisms of neurons. A DTNP system consists of several dynamic threshold neurons, and each neuron has a data unit and a threshold unit. The computational completeness of DTNP systems has been investigated. DTNP systems are synchronous systems, and a global clock is assumed to synchronize all threshold neurons. However, the assumption is biologically non-realistic. In this paper, we discuss DTNP systems working in sequential mode, i.e., sequential DTNP systems (SDTNP systems, in short). Based on the number of spikes of active neurons and the rule-application strategy, four sequentiality strategies are considered. It is proven that SDTP systems working in four sequentiality strategies are Turing universal number generating/accepting devices.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (No. 62076206), and Research Foundation of the Education Department of Sichuan province (No. 17TD0034), China.

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Authors and Affiliations

  1. School of Computer and Software Engineering, Xihua University, Chengdu, Sichuan, 610039, China

    Tingting Bao, Nan Zhou, Zeqiong Lv & Hong Peng

  2. School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, 610039, China

    Jun Wang

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  1. Tingting Bao
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  2. Nan Zhou
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  4. Hong Peng
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Correspondence to Hong Peng.

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Bao, T., Zhou, N., Lv, Z. et al. Sequential dynamic threshold neural P systems. J Membr Comput 2, 255–268 (2020). https://doi.org/10.1007/s41965-020-00060-0

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