M.Sc. C.S. Thesis Defense: Korsie J. Ballesteros (Numerical Spiking Neural P systems: Representation, Simulations and Solutions)
July 4, 2022
Meeting ID: 945 7767 7254
Meeting password: 11384834
- Richelle Ann B. Juayong, Ph.D., Chair
- Francis George Cabarle, Ph.D., Adviser
- Johnrob Y. Bantang, Ph.D., Reader
Spiking Neural P systems (SNP systems) are biologically inspired models of computation based on the firing behavior of neurons. Variations of these systems have been proposed to solve more specific problems. A more recent variation called the Numerical Spiking Neural P systems(NSNP systems) combines concepts from SNP systems and Numerical P systems to create a new variant of SNP systems. This variant allows continuous production functions with real valued variables and in effect, allows for faster resolution of rules within neurons when compared to the traditional regular expression matching mechanism used by more classical variants of SNP systems. This work develops a matrix representation for the representation of NSNP systems and its interactions together with a simulation algorithm that uses this representation to generate computation graphs of NSNP systems. This research also provides a NSNP system based non-deterministic solution for the Subset sum problem and comparisons of said solution with some existing P system solutions to the Subset Sum Problem.