Spiking Neural P Systems With White Hole Neurons

Abstract

Spiking neural P systems (SN P systems) are a class of parallel and distributed spiking neural network models, which are inspired from the way biological neurons spiking and communicating by means of spikes. White hole rules, abstracted from the biological observation of neural information rejection, were recently introduced into SN P systems, by which a neuron consumes its complete contents when it fires. In this work, SN P systems with white hole neurons are proposed, in which each neuron has only white hole rules. The computational power of general and bounded SN P systems with white hole neurons are obtained. Specifically, it is achieved in a constructive way that i) general SN P systems (having both bounded and unbounded) white hole neurons are Turing universal as number generators; ii) bounded SN P systems with white hole neurons can only characterize semi-linear sets of numbers. These results show that "information storage capacity" of certain key neurons provides some "programming capacity" useful for SN P systems achieving a desired computation power.