Spiking neural P systems with rules on synapses

Authors:
Tao Song;Linqiang Pan;Gheorghe Pun
Affiliations:
Key Laboratory of Image Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China;Key Laboratory of Image Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China;Institute of Mathematics of the Romanian Academy, P.O. Box 1-764, 014700 Bucureşti, Romania
Venue:
Theoretical Computer Science
Year:
2014

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Abstract

Spiking neural P systems (SN P systems, for short) are a class of membrane systems inspired from the way the neurons process information and communicate by means of spikes. In this paper, we introduce and investigate a new class of SN P systems, with spiking rules placed on synapses. The computational completeness is first proved, then two small universal SN P systems with rules on synapses for computing functions are constructed. Specifically, when using standard spiking rules, we obtain a universal system with 39 neurons, while when using extended spiking rules on synapses, a universal SN P system with 30 neurons is constructed.