Research ArticlesNo Access

Spiking Neural P Systems with Communication on Request

Key Laboratory of Image Information Processing, and Intelligent Control of Education Ministry of China, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, P. R. China

and Zhengzhou University of Light Industry, Zhengzhou 450002, Henan, P. R. China

E-mail Address: lqpan@mail.hust.edu.cn

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Gheorghe Păun

Institute of Mathematics of the Romanian Academy, P. O. Box 1-764, RO-014700 Bucharest, Romania

E-mail Address: gpaun@us.es

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Gexiang Zhang

Robotics Research Center, Xihua University, Chengdu 610039, P. R. China

Key Laboratory of Fluid and Power Machinery (Xihua University), Ministry of Education, Chengdu 610039, P. R. China

School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, P. R. China

E-mail Address: zhgxdylan@126.com

Corresponding author.

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, and

Ferrante Neri

Centre for Computational Intelligence, School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester LE1 9BH, England, UK

E-mail Address: fneri@dmu.ac.uk

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https://doi.org/10.1142/S0129065717500423Cited by:167 (Source: Crossref)

Abstract

Spiking Neural $P$ Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural $P$ systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these $P$ systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron.

In the present work, a novel communication strategy among neurons of Spiking Neural $P$ Systems is proposed. In the resulting models, called Spiking Neural $P$ Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural $P$ systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron).

The Spiking Neural $P$ Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems.

Keywords:

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