4.7 Article

Asynchronous spiking neural P systems with rules on synapses and coupled neurons

期刊

KNOWLEDGE-BASED SYSTEMS
卷 257, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.knosys.2022.109896

关键词

Bio-inspired computing; Spiking neural P system; Coupled neuron; Computational power

资金

  1. National Key R&D Program of China for International S&T Cooperation Projects [2021YFE0102 100]
  2. National Natural Science Foundation of China [62072201]
  3. Provincial Key R&D Program of Hubei [2021BAA168]
  4. Fundamental Research Funds for the Central Universities [HUST:2019 kfyXMBZ056]

向作者/读者索取更多资源

The paper introduces the asynchronous spiking neural P system with rules on synapses (ASNPR system) and presents the ASNPRC system with coupled neurons. The computational power of the ASNPRC systems is investigated and it is proven that they are Turing universal function-computing devices. The results highlight the importance of coupled neurons in the computation power of ASNPR systems.
The asynchronous spiking neural P system with rules on synapses (ASNPR system) is a type of distributive and non-deterministic computing model inspired by neural activities in biology. In this work, the synchronous excitation/inhibition phenomenon of coupled neurons is abstracted as the all -or-none behavior of coupled neurons: spikes in the coupled neurons are supplemented/consumed if and only if all the coupled neurons in the same set synchronously receive/consume spikes. Based on all-or-none behaviors, the ASNPR system with coupled neurons (ASNPRC system) is introduced, where coupled neurons are grouped into several sets, and the changes in the content of coupled neurons in the same set are positively correlated. The computational power of the ASNPRC systems is investigated. It has been proven that ASNPRC systems using standard rules are Turing universal function-computing devices. Moreover, a universal ASNPRC system consisting of four neurons is constructed to compute functions. The results show that coupled neuronsis an efficient ingredient for the computation power of ASNPR systems in the sense that ASNPR systems using relatively few neurons achieve their universality with the help of couple neurons.(c) 2022 Elsevier B.V. All rights reserved.

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