Evolutionary membrane computing: A comprehensive survey and new results

Spiking neural P systems (SN P systems) are a class of distributed and parallel neural-like computing models, inspired from the way neurons communicate by means of spikes. In this paper, a new variant of the systems, called SN P systems with learning functions, is introduced. Such systems can dynamically strengthen and weaken connections among neurons during the computation. A class of specific SN P systems with simple Hebbian learning function is constructed to recognize English letters. The experimental results show that the SN P systems achieve average accuracy rate 98.76% in the test case without noise. In the test cases with low, medium, and high noises, the SN P systems outperform back propagation neural networks and probabilistic neural networks. Moreover, comparing with spiking neural networks, SN P systems perform a little better in recognizing letters with noise. The result of this paper is promising in terms of the fact that it is the first attempt to use SN P systems in pattern recognition after many theoretical advancements of SN P systems, and SN P systems exhibit the feasibility for tackling pattern recognition problems.

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Spiking Neural P Systems With Learning Functions

Neural-like computing models are versatile computing mechanisms in the field of artificial intelligence. Spiking neural P systems (SN P systems for short) are one of the recently developed spiking neural network models inspired by the way neurons communicate. The communications among neurons are essentially achieved by spikes, i. e. short electrical pulses. In terms of motivation, SN P systems fall into the third generation of neural network models. In this study, a novel variant of SN P systems, namely SN P systems with self-organization, is introduced, and the computational power of the system is investigated and evaluated. It is proved that SN P systems with self-organization are capable of computing and accept the family of sets of Turing computable natural numbers. Moreover, with 87 neurons the system can compute any Turing computable recursive function, thus achieves Turing universality. These results demonstrate promising initiatives to solve an open problem arisen by Gh Paun.

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On the Computational Power of Spiking Neural P Systems with Self-Organization.

An unsupervised learning algorithm for membrane computing

This paper presents the newly introduced class of simple kernel P systems s kP systems and investigates through a 3-colouring problem case study the expressive power and efficiency of kernel P systems. It describes two skP systems that model the problem and analyses them in terms of efficiency and complexity. The skP models prove to be more succinct in terms of number of rules, objects, number of cells and execution steps than the corresponding tissue P system, available in the literature, that solves the same problem, at the expense of a greater length of the rules.

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3-Col problem modelling using simple kernel P systems

This is a list of open problems and research topics collected after the Twelfth Conference on Membrane Computing, CMC 2012 (Fontainebleau, France (23 26 August 2011), meant initially to be a working material for Tenth Brainstorming Week on Membrane Computing, Sevilla, Spain (January 30 February 3, 2012). The result was circulated in several versions before the brainstorming and then modified according to the discussions held in Sevilla and according to the progresses made during the meeting. In the present form, the list gives an image about key research directions currently active in membrane computing.

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Frontiers of Membrane Computing: Open Problems and Research Topics

1 Department of Computer Science University of Sheffield Portobello Street, Regent Court, Sheffield, S1 4DP, UK {m.gheorghe, c.dragomir}@sheffield.ac.uk 2 Department of Computer Science University of Bucarest Str Academiei, 14, Bucarest, Romania florentin.ipate@ifsoft.ro, laurentiu.mierla@gmail.com 3 Research Group on Natural Computing Department of Computer Science and Artificial Intelligence University of Seville Avda. Reina Mercedes s/n, 41012 Seville, Spain {lvalencia, mgarciaquismondo, marper}@us.es

Kernel P Systems - Version 1

This paper presents an integrated approach for model simulation, property extraction and formal verification of P systems, illustrated on a tissue P system with active membranes solving the 3-colouring problem. The paper focuses on this problem and reports the invariants and the properties extracted and verified using a series of tools (Daikon, MeCoSim, Maple, Spin, ProB) and languages (P–Lingua, Promela, Event-B). Appropriate tools and integration plugins, which facilitate and even automate the steps involved in the aforementioned approach, have also been developed. The case study chosen is complex (it involves an exponential growth of the number of states through the use of membrane division rules) and the properties obtained are non-trivial.

Towards an Integrated Approach for Model Simulation, Property Extraction and Veri cation of P Systems

A basic P system, called kernel P system (kP system for short), covering features of different P systems introduced and studied so far is defined and discussed. It is a relatively low level specification system aiming to cover features exhibited by most of the problems modelled so far using P system formalisms. A small set of rules and specific strategies to run the system step by step are presented. Some preliminary results regarding the relationships between kP systems and other classes of P systems, like neural-like P systems and P systems with active membranes, are presented. Examples illustrating the behaviour of kP systems or showing how a sorting algorithm is modelled with various classes of P systems are provided. Further developments of this class of P systems are finally briefly discussed.

A Kernel P System

P systems are the computational models introduced in the context of membrane computing, a computational paradigm within the more general area of unconventional computing. Kernel P (kP) systems are defined to unify the specification of different variants of P systems, motivated by challenging theoretical aspects and the need to model different problems. In this paper, we present kPWorkbench, a software framework developed to support kP systems. kPWorkbench integrates several simulation and verification tools and methods, and provides a software suit for the modelling and analysis of membrane systems.

Marian Gheorgue

Papers

Frontiers of Membrane Computing: Open Problems and Research Topics

Kernel P Systems - Version 1

Towards an Integrated Approach for Model Simulation, Property Extraction and Veri cation of P Systems

A Kernel P System

kPWorkbench: A Software Framework for Kernel P Systems