Spiking Neural P Systems With Scheduled Synapses.

Area of Science:

• Computational Neuroscience
• Theoretical Computer Science
• Biologically Inspired Computing

Background:

• Spiking Neural P systems (SN P systems) model computation using biological spiking neurons and static synapse graphs.
• Existing SN P systems lack dynamic synapse properties found in biological systems.

Purpose of the Study:

• Introduce a novel variant, SN P systems with scheduled synapses (SSN P systems).
• Incorporate structural dynamism of biological synapses into SN P systems.
• Address the limitation of static synapses in traditional SN P systems.

Main Methods:

• Developed SSN P systems inspired by dynamic graphs and biological synapse behavior.
• Introduced local and global synapse schedule types.
• Analyzed computational universality under a normal form.

Main Results:

• SSN P systems demonstrate computational universality as number generators and acceptors.
• Both local and global schedule types were proven universal.
• Synapse scheduling enhances system programmability, even with normal form restrictions.

Conclusions:

• SSN P systems effectively model dynamic synapse behavior, expanding the capabilities of SN P systems.
• The computational universality of SSN P systems is established for both schedule types.
• Scheduled synapses offer practical advantages for programming these computational models.