Spiking Neural P Systems With White Hole Neurons

Area of Science:

• Computational neuroscience
• Theoretical computer science
• Biologically inspired computing

Background:

• Spiking neural P systems (SN P systems) are parallel, distributed models inspired by biological neuron communication via spikes.
• White hole rules, representing neural information rejection, were recently integrated into SN P systems, causing neurons to consume their contents upon firing.

Purpose of the Study:

• To introduce and analyze SN P systems exclusively utilizing white hole rules.
• To determine the computational power of both general and bounded SN P systems with white hole neurons.

Main Methods:

• Proposing SN P systems where each neuron exclusively employs white hole rules.
• Constructively demonstrating the computational capabilities of these systems as number generators and set characterizers.

Main Results:

• General SN P systems with white hole neurons exhibit Turing universality in number generation.
• Bounded SN P systems with white hole neurons are capable of characterizing semi-linear sets of numbers.

Conclusions:

• The information storage capacity of specific neurons in SN P systems contributes to their computational power.
• These findings advance the understanding of SN P systems' capabilities and their potential applications in computation.