Polynomial complexity classes in spiking neural P systems

Authors:
Petr Sosík;Alfonso Rodríguez-Patón;Lucie Ciencialová
Affiliations:
Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain and Institute of Computer Science, Faculty of Philosophy and Science, S ...;Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain;Institute of Computer Science, Faculty of Philosophy and Science, Silesian University in Opava, Opava, Czech Republic
Venue:
CMC'10 Proceedings of the 11th international conference on Membrane computing
Year:
2010

Citing 8
Cited 1

A new algorithm for regularizing one-letter context-free grammars

Theoretical Computer Science
Spiking Neural P Systems

Fundamenta Informaticae
On the Computational Complexity of Spiking Neural P Systems

UC '08 Proceedings of the 7th international conference on Unconventional Computing
Uniform solutions to SAT and Subset Sum by spiking neural P systems

Natural Computing: an international journal
Deterministic solutions to QSAT and Q3SAT by spiking neural P systems with pre-computed resources

Theoretical Computer Science
Solving numerical NP-complete problems with spiking neural P systems

WMC'07 Proceedings of the 8th international conference on Membrane computing
A polynomial complexity class in P systems using membrane division

Journal of Automata, Languages and Combinatorics
A computational complexity theory in membrane computing

WMC'09 Proceedings of the 10th international conference on Membrane Computing

Selected topics in computational complexity of membrane systems

Computation, cooperation, and life

Quantified Score

Hi-index	0.00

Visualization

Abstract

We study the computational potential of spiking neural (SN) P systems. several intractable problems have been proven to be solvable by these systems in polynomial or even constant time. We study first their formal aspects such as the input encoding, halting versus spiking, and descriptional complexity. Then we establish a formal platform for complexity classes of uniform families of confluent recognizer SN P systems. Finally, we present results characterizing the computational power of several variants of confluent SN P systems, characterized by classes ranging from P to PSPACE.