Nondeterminism in Spiking Neural P Systems: Algorithms and Simulations

Abstract

Spiking Neural P system (or SN P system) is a computing model based on the neurons in a living being. It is composed of neurons containing spikes interconnected by synapses. Each neuron contain a set of rules which will determine how the spikes are passed in the system. It is a non-deterministic and parallel system which makes GPU a good candidate for simulating this computing model. A matrix representation for system without delay was previously developed and an algorithm for simulating deterministic systems with delay was also presented. In this work, an algorithm for simulating non-deterministic Spiking Neural P System was presented. To accelerate simulations of Spiking Neural P Systems, this algorithm was then implemented and used to simulate nonuniform and uniform solution to the subset sum problem as a case study. Time and space resources in the GPU of such simulations are then compared and analyzed