Title: A normal form of spiking neural P systems with structural plasticity

Authors: Tao Song; Linqiang Pan

Addresses: Key Laboratory of Image Information Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China ' Key Laboratory of Image Information Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China

Abstract: Spiking neural P systems (SN P systems, in short) are a class of distributed and parallel neural-like computing models, which are inspired by the way of biological neurons processing information and communication by means of impulses (or spikes). SN P systems with structural plasticity are a new variant of SN P systems, which have plasticity to the topological structure in the sense that connections among neurons can be reformed by creating and deleting synapses during the computation. In this work, we give a normal form of universal SN P systems with structural plasticity. Specifically, we prove that SN P systems with structural plasticity can achieve Turing universality with the restriction that any neuron can either create or delete synapses, instead of both, in a computation step. This result gives an answer to an open problem formulated in Francis et al. (2013).

Keywords: bio-inspired computing; membrane computing; spiking neural P systems; SN P systems; Turing universality; structural plasticity.

DOI: 10.1504/IJSI.2015.072889

International Journal of Swarm Intelligence, 2015 Vol.1 No.4, pp.344 - 357

Available online: 06 Nov 2015 *

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