Spiking neural P systems with anti-spikes and without annihilating priority working in a 'flip-flop' way

Authors:
Gangjun Tan;Tao Song;Zhihua Chen;Xiangxiang Zeng
Affiliations:
Key Laboratory of Image Processing and Intelligent Control, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China;Key Laboratory of Image Processing and Intelligent Control, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China;Key Laboratory of Image Processing and Intelligent Control, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China;Key Laboratory of Image Processing and Intelligent Control, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
Venue:
International Journal of Computing Science and Mathematics
Year:
2013

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Abstract

Spiking neural P systems with anti-spikes ASN P systems, for short are a variant of spiking neural P systems, which are inspired by inhibitory impulses/spikes in biological neural systems. In general ASN P systems, spikes and anti-spikes can annihilate with each other when they meet in a neuron. The annihilation has priority to using spiking and forgetting rules, and takes no time to finish. In this work, we consider ASN P systems without annihilating priority with neurons working in a 'flip-flop' way, that is each neuron can only produce spikes by anti-spikes or produce anti-spikes from spikes. As results, such systems achieve the Turing completeness as number generator. This gives a positive answer to an open problem left in IJCCC 2009, Vol. IV, No. 3, pp.273-282.