Generating context-free languages using spiking neural P systems with structural plasticity

Abstract

Spiking neural P system (SNP system) is a model of computation inspired by networks of spiking neurons. An SNP system is a network of neurons that can send an object, known as a spike, to each other. Spiking neural P system with structural plasticity (SNPSP system) is a variant of the classical SNP system. SNPSP system that incorporates the ideas of synaptogenesis (creating new synapses) and synaptic pruning (deletion of existing synapses), collectively known as structural plasticity, as features of the model. This gives SNPSP systems the ability to change their own structure/topology. In this work, we use SNPSP systems to generate context-free languages. We create a procedure for constructing an SNPSP system given a context-free grammar in Greibach normal form (GNF). The resulting SNPSP system essentially simulates the way in which a context-free grammar in GNF is used to generate languages. We use modules known as arithmetic-memory modules, also created using SNPSP systems, to perform arithmetic operations which are needed for the simulation.