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. © 2010 Springer-Verlag.
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CITATION STYLE
Sosík, P., Rodríguez-Patón, A., & Ciencialová, L. (2010). Polynomial complexity classes in spiking neural P systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6501 LNCS, pp. 348–360). Springer Verlag. https://doi.org/10.1007/978-3-642-18123-8_27
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