Spiking neural P systems with rules on synapses working in maximum spikes consumption strategy

Area of Science:

• Theoretical Computer Science
• Computational Neuroscience
• Biologically Inspired Computing

Background:

• Spiking neural P systems (SN P systems) are parallel and distributed computation models.
• These models are inspired by neuronal information processing via spikes.

Purpose of the Study:

• To introduce and analyze a novel variant of SN P systems.
• To investigate the computational power of this new variant.

Main Methods:

• A new variant of SN P systems is proposed where spiking rules are associated with synapses instead of neurons.
• Neurons contain spikes, and rules on synapses are enabled by the number of spikes in a neuron.
• At most one rule is applied per synapse per step, with spikes consumed based on the maximum number used by applied rules.

Main Results:

• The computational power of the proposed SN P system variant is investigated.
• It is proven that these systems can generate or accept any set of Turing-computable natural numbers.

Conclusions:

• The introduced SN P system variant possesses significant computational power.
• This work resolves an open problem in the field of SN P systems, enhancing understanding of their capabilities.