Sequential Spiking Neural P Systems with Local Scheduled Synapses without Delay
Spiking neural P systems with scheduled synapses are a class of distributed and parallel computational models motivated by the structural dynamism of biological synapses by incorporating ideas from nonstatic (i.e., dynamic) graphs and networks. In this work, we consider the family of spiking neural...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/7313414 |
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| author | Alia Bibi Fei Xu Henry N. Adorna Francis George C. Cabarle |
| author_facet | Alia Bibi Fei Xu Henry N. Adorna Francis George C. Cabarle |
| author_sort | Alia Bibi |
| collection | DOAJ |
| description | Spiking neural P systems with scheduled synapses are a class of distributed and parallel computational models motivated by the structural dynamism of biological synapses by incorporating ideas from nonstatic (i.e., dynamic) graphs and networks. In this work, we consider the family of spiking neural P systems with scheduled synapses working in the sequential mode: at each step the neuron(s) with the maximum/minimum number of spikes among the neurons that can spike will fire. The computational power of spiking neural P systems with scheduled synapses working in the sequential mode is investigated. Specifically, the universality (Turing equivalence) of such systems is obtained. |
| format | Article |
| id | doaj-art-facf66ecf1f0481281b688d9c41e742d |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-facf66ecf1f0481281b688d9c41e742d2025-02-03T05:57:27ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/73134147313414Sequential Spiking Neural P Systems with Local Scheduled Synapses without DelayAlia Bibi0Fei Xu1Henry N. Adorna2Francis George C. Cabarle3Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, ChinaKey Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, ChinaDepartment of Computer Science, University of the Philippines Diliman, Quezon City, PhilippinesDepartment of Computer Science, University of the Philippines Diliman, Quezon City, PhilippinesSpiking neural P systems with scheduled synapses are a class of distributed and parallel computational models motivated by the structural dynamism of biological synapses by incorporating ideas from nonstatic (i.e., dynamic) graphs and networks. In this work, we consider the family of spiking neural P systems with scheduled synapses working in the sequential mode: at each step the neuron(s) with the maximum/minimum number of spikes among the neurons that can spike will fire. The computational power of spiking neural P systems with scheduled synapses working in the sequential mode is investigated. Specifically, the universality (Turing equivalence) of such systems is obtained.http://dx.doi.org/10.1155/2019/7313414 |
| spellingShingle | Alia Bibi Fei Xu Henry N. Adorna Francis George C. Cabarle Sequential Spiking Neural P Systems with Local Scheduled Synapses without Delay Complexity |
| title | Sequential Spiking Neural P Systems with Local Scheduled Synapses without Delay |
| title_full | Sequential Spiking Neural P Systems with Local Scheduled Synapses without Delay |
| title_fullStr | Sequential Spiking Neural P Systems with Local Scheduled Synapses without Delay |
| title_full_unstemmed | Sequential Spiking Neural P Systems with Local Scheduled Synapses without Delay |
| title_short | Sequential Spiking Neural P Systems with Local Scheduled Synapses without Delay |
| title_sort | sequential spiking neural p systems with local scheduled synapses without delay |
| url | http://dx.doi.org/10.1155/2019/7313414 |
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