Name
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-18123-8_27 | Int. Conf. on Membrane Computing |
Keywords | Field | DocType |
confluent sn p system,polynomial complexity class,formal aspect,descriptional complexity,constant time,computational potential,neural p system,spiking neural,complexity class,p system,formal platform,computational power | Quantum complexity theory,PH,Complexity class,Discrete mathematics,Structural complexity theory,Sparse language,Polynomial,P,PSPACE,Mathematics | Conference |
Volume | ISSN | ISBN |
6501 | 0302-9743 | 3-642-18122-8 |
Citations | PageRank | References |
2 | 0.38 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Petr Sosík | 1 | 479 | 68.66 |
| Alfonso Rodríguez-Patón | 2 | 435 | 51.44 |
| Lucie Ciencialová | 3 | 45 | 11.98 |