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Sequential spiking neural P systems with exhaustive use of rules
Xingyi Zhang1, Bin Luo, Xianyong Fang
1Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, China. xyzhanghust@gmail.com
Bio Systems
|February 7, 2012
Summary
Sequential spiking neural P systems (SN P systems) with exhaustive rules exhibit Turing computability and generate semilinear sets. Their computational power is linked to neuron spiking rule types.
Area of Science:
- Computational intelligence
- Theoretical computer science
- Biologically inspired computing
Background:
- Spiking neural P systems (SN P systems) are parallel computing devices modeling neuronal communication via spikes.
- Neurons operate in parallel, but sequentially apply rules at each step.
- This study focuses on a specific variant: sequential SN P systems with exhaustive rule application.
Purpose of the Study:
- Investigate the computational power of sequential SN P systems with exhaustive rule use.
- Characterize their relationship with Turing computability and semilinear sets.
- Explore the generation of sets beyond semilinear ones.
Main Methods:
- Utilizing sequential SN P systems with the restriction of one neuron firing per step.
- Implementing exhaustive rule application within each neuron.
- Analyzing the resulting computational capabilities and generated sets of numbers.
Main Results:
- Established characterizations of Turing computability for these systems.
- Demonstrated the generation of semilinear sets of numbers.
- Identified a strict superclass of semilinear sets.
Conclusions:
- The computational power of sequential SN P systems with exhaustive rules is closely tied to the specific types of spiking rules employed.
- These systems offer a novel framework for exploring computational complexity and biologically inspired computing models.
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