Weighted Spiking Neural P Systems with Structural Plasticity Working in Maximum Spiking Strategy

Abstract

Spiking neural P systems(in short, SNP systems) are parallel and distributed computing devices inspired by the function and structure of spiking neurons. Recently, a new variant of SNP systems, called SNP systems with structural plasticity(in short, SNPSP systems) was introduced. In SNPSP systems, neuron can use plasticity rules to create and delete synapses. In this work, we consider many restrictions sequentiality on SNPSP systems:(i)we use the weighted synapses,(ii)neuron with the maximum number of spikes is chosen to fire. Specifically, We investigate the computational power of weighted SNPSP systems working in maximum spiking strategy(in short, WSNPSPM systems) and we proved that such SNPSP systems are universal as generating devices and accepting devices.