Asynchronous spiking neural P systems: decidability and undecidability

  • Authors:

  • Matteo Cavaliere;Omer Egecioglu;Oscar H. Ibarra;Mihai Ionescu;Gheorghe Păun;Sara Woodworth

  • Affiliations:

  • Microsoft Research, University of Trento, CoSBi, Italy;Dept. of Computer Science, University of California, Santa Barbara;Dept. of Computer Science, University of California, Santa Barbara;Research Group on Mathematical Linguistics, Universitat Rovira i Virgili, Tarragona, Spain;Institute of Mathematics of the Romanian Academy, Bucharest, Romania;Dept. of Computer Science, University of California, Santa Barbara

  • Venue:
  • DNA13'07 Proceedings of the 13th international conference on DNA computing
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

In search for "realistic" bio-inspired computing models, we consider asynchronous spiking neural P systems, in the hope to get a class of computing devices with decidable properties. However, although the non-synchronization is known in general to decrease the computing power, in the case of using extended rules (several spikes can be produced by a rule) we obtain again the equivalence with Turing machines (interpreted as generators of sets of vectors of numbers). The problem remains open for the case of restricted spiking neural P systems, whose rules can only produce one spike. On the other hand, we prove that asynchronous spiking neural P systems, with a specific way of halting, using extended rules and where each neuron is either bounded or unbounded, are equivalent to partially blind counter machines and, therefore, have many decidable properties.