Affiliations: [a] Shanghai Institute of Science & Technology Management, Shanghai 201800, Shanghai, China | [b] Key Laboratory of Image Information Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China. tfwu@hust.edu.cn | [c] School of Electric and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China | [d] School of Computer Science, Wuhan University of Science and Technology, Wuhan 420081, Hubei, China
Correspondence:
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Address for correspondence: Key Laboratory of Image Information Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.
Note: [†] Also affiliated at: Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430081, Hubei, China
Abstract: Spiking neural (SN, for short) P systems are a class of computation models inspired from the way in which neurons communicate by exchanging spikes. SN P systems with homogenous neurons and synapses are a new variant of SN P systems, where the spiking and forgetting rules are placed on synapses instead of in neurons and each synapse has the same set of spiking and forgetting rules. Recent studies illustrated that this variant of SN P systems is Turing universal as both number generating and accepting devices. In this note, we prove that SN P systems with homogenous neurons and synapses without the feature of delay are also Turing universal. This result gives a positive answer to an open problem formulated in [K. Jiang, et al. Neurocomputing 171(2016) 1548-1555] “whether SN P systems with homogenous neurons and synapses are Turing universal when the feature of delay is not used”.
Keywords: Bio-inspired computing, Membrane computing, Spiking neural P system, Small universal system, Turing completeness
