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On String Languages Generated by Spiking Neural P Systems With Structural Plasticity.
IEEE Transactions on Nanobioscience
|November 8, 2018
Summary
Spiking neural P systems with structural plasticity (SNPSP) can compute Chomsky hierarchy languages. Synapse plasticity offers advantages for neural-inspired computation and language generation.
Area of Science:
- Computational neuroscience
- Theoretical computer science
- Formal languages
Background:
- Spiking neural P systems (SNP) are brain-inspired computational models.
- Structural plasticity in neural systems allows synapse modification, crucial for learning.
- SNP systems with structural plasticity (SNPSP) incorporate this feature.
Purpose of the Study:
- To investigate the computational power of SNPSP systems.
- To compute classes of languages within the Chomsky hierarchy (FIN, REG, RE).
- To analyze the role of synapse plasticity in language generation.
Main Methods:
- Utilizing SNPSP systems for language computation.
- Comparing SNPSP systems with existing SNP system features.
- Analyzing advantages and disadvantages of synapse plasticity.
Main Results:
- SNPSP systems demonstrate capability in computing FIN, REG, and RE languages.
- Synapse plasticity offers specific benefits for language generation tasks.
- The study extends previous research on SNP systems and formal languages.
Conclusions:
- SNPSP systems are computationally universal and capable of generating complex language classes.
- Synapse plasticity is a key feature enhancing the computational power of neural-inspired systems.
- This research contributes to understanding brain-like computation and formal language theory.
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