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On some classes of sequential spiking neural p systems
Xingyi Zhang1, Xiangxiang Zeng, Bin Luo
1Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei 230039, China xyzhanghust@gmail.com.
Neural Computation
|February 22, 2014
Summary
This study introduces novel Spiking Neural P systems (SN P systems) with sequential or pseudo-sequential firing modes. These systems demonstrate Turing universality, showing sequential restrictions minimally impact computational power.
Area of Science:
- Theoretical Computer Science
- Computational Neuroscience
- Biologically Inspired Computing
Background:
- Spiking Neural P systems (SN P systems) are parallel computing devices modeling neuronal communication via spikes.
- Neurons operate sequentially within a step, but multiple neurons can fire in parallel.
- Existing models explore parallel computation inspired by neural networks.
Purpose of the Study:
- To investigate novel SN P systems with biologically inspired sequential and pseudo-sequential firing modes.
- To analyze the computational power of these restricted SN P systems.
- To explore the impact of sequentiality restrictions on the universality of SN P systems.
Main Methods:
- Introduced four types of SN P systems: maximum/minimum spike number induced sequential/pseudo-sequential systems.
- Implemented exhaustive rule application within neurons (local parallelism).
- Analyzed the systems as number-generating computation devices.
Main Results:
- All four types of SN P systems were proven to be Turing universal.
- Demonstrated that sequential or pseudo-sequential modes do not significantly reduce computational power.
- Showcased the potential of restricted neural-inspired computing models.
Conclusions:
- The studied SN P systems, despite sequential restrictions, achieve Turing universality.
- Biologically inspired constraints on neuronal firing (sequential/pseudo-sequential) do not inherently limit computational capacity.
- These findings contribute to understanding the computational power of restricted parallel systems.
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