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Numerical spiking neural P systems with weights

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Abstract

Numerical spiking neural (NSN) P systems are a novel type of distributed parallel membrane computing models, where numerical variables and continuous production functions are employed instead of spikes and evolution rules in traditional spiking neural (SN) P systems. The values of numerical variables are used to encode information, continuous production functions are used to describe the procedure of processing information. In this work, an improvement is made to NSN P systems, a weight is placed on the synapse connecting two neurons, a modified computing model of numerical spiking neural P systems is constructed, called numerical spiking neural P systems with weights (NSNW P systems, for short). We show that the introduction of weights not only makes NSNW P systems be still Turing universal, but also makes the computing process more simple, that is the computational power of NSNW P systems is investigated by using fewer neurons. Especially, the weights used as restrictive conditions only is 1 or \(-1\), in this case, NSNW P systems as number generating devices and number accepting devices are still universal.

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Acknowledgements

The work of S. Jiang was supported by National Natural Science Foundation of China (61902360), and the Foundation of Young Key Teachers from University of Henan Province (2019GGJS131).

Funding

This work was supported by National Natural Science Foundation of China (61902360), the Foundation of Young Key Teachers from University of Henan Province (2019GGJS131), and the Joint Funds of the National Natural Science Foundation of China (U1804262).

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    Authors and Affiliations

    1. School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, Henan, China

      Bowen Xu, Suxia Jiang, Zhichao Shen, Xiaoliang Zhu & Tao Liang

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    1. Bowen Xu
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    Contributions

    All authors contributed to the study conception and design. The conception and design of the work were performed by SJ and BX. Material preparation, data collection and analysis were performed by TL, ZS. The first draft of the manuscript was written by BX. Yanfeng Wang revised this work for important intellectual content.

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    Correspondence to Suxia Jiang.

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    Xu, B., Jiang, S., Shen, Z. et al. Numerical spiking neural P systems with weights. J Membr Comput 5, 12–24 (2023). https://doi.org/10.1007/s41965-022-00116-3

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