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Homogenous Spiking Neural P Systems with Inhibitory Synapses

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Abstract

Spiking neural P systems with inhibitory synapses (ISN P systems, for short) are a class of discrete neural-like computing models, which are inspired by the way of biological neurons storing and processing information and communication by means of excited and inhibitory impulses. In this work, we prove that ISN P systems can compute and accept any set of Turing computable numbers by using one type of neurons, thus can achieve Turing universality. Such systems are called homogenous ISN P systems. The results give a positive answer to an open problem left in (Pan and Păun, Int J Comput Commun 4(3):273–282, 2009) that “whether the number of types of neurons in universal SN P systems can be decreased by using inhibitory synapses”. The obtained result is optimal in the sense of having minimal number of types of neurons in Turing universal SN P systems.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (61033003, 91130034, 61100145, 61202011 and 61272071), Ph.D. Programs Foundation of Ministry of Education of China (20100142110072 and 2012014213 008), and Natural Science Foundation of Hubei Province (2011CDA027).

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Authors and Affiliations

  1. School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China

    Tao Song

  2. Graduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki , 3050006, Japan

    Xun Wang

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  1. Tao Song
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  2. Xun Wang
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Correspondence to Xun Wang.

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Song, T., Wang, X. Homogenous Spiking Neural P Systems with Inhibitory Synapses. Neural Process Lett 42, 199–214 (2015). https://doi.org/10.1007/s11063-014-9352-y

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