Delayed Spiking Neural P Systems with Scheduled Rules
Due to the inevitable delay phenomenon in the process of signal conversion and transmission, time delay is bound to occur between neurons. Therefore, it is necessary to introduce the concept of time delay into the membrane computing models. Spiking neural P systems (SN P systems), as an attractive t...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6817636 |
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| _version_ | 1832547704208621568 |
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| author | Qianqian Ren Xiyu Liu |
| author_facet | Qianqian Ren Xiyu Liu |
| author_sort | Qianqian Ren |
| collection | DOAJ |
| description | Due to the inevitable delay phenomenon in the process of signal conversion and transmission, time delay is bound to occur between neurons. Therefore, it is necessary to introduce the concept of time delay into the membrane computing models. Spiking neural P systems (SN P systems), as an attractive type of neural-like P systems in membrane computing, are widely followed. Inspired by the phenomenon of time delay, in our work, a new variant of spiking neural P systems called delayed spiking neural P systems (DSN P systems) is proposed. Compared with normal spiking neural P systems, the proposed systems achieve time control by setting the schedule on spiking rules and forgetting rules, and the schedule is also used to realize the system delay. A schedule indicates the time difference between receiving and outputting spikes, and it also makes the system work in a certain time, which means that a rule can only be used within a specified time range. We specify that each rule is performed only in the continuous schedule, during which the neuron is locked and cannot send or receive spikes. If the neuron is not available at a given time, it will not receive or send spikes due to the lack of a schedule for this period of time. Moreover, the universality of DSN P systems in both generating and accepting modes is proved. And a universal DSN P system having 81 neurons for computing functions is also proved. |
| format | Article |
| id | doaj-art-5d2c203336b542b4bee53329636446c2 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-5d2c203336b542b4bee53329636446c22025-02-03T06:43:46ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/68176366817636Delayed Spiking Neural P Systems with Scheduled RulesQianqian Ren0Xiyu Liu1Academy of Management Science, Business School, Shandong Normal University, Jinan, ChinaAcademy of Management Science, Business School, Shandong Normal University, Jinan, ChinaDue to the inevitable delay phenomenon in the process of signal conversion and transmission, time delay is bound to occur between neurons. Therefore, it is necessary to introduce the concept of time delay into the membrane computing models. Spiking neural P systems (SN P systems), as an attractive type of neural-like P systems in membrane computing, are widely followed. Inspired by the phenomenon of time delay, in our work, a new variant of spiking neural P systems called delayed spiking neural P systems (DSN P systems) is proposed. Compared with normal spiking neural P systems, the proposed systems achieve time control by setting the schedule on spiking rules and forgetting rules, and the schedule is also used to realize the system delay. A schedule indicates the time difference between receiving and outputting spikes, and it also makes the system work in a certain time, which means that a rule can only be used within a specified time range. We specify that each rule is performed only in the continuous schedule, during which the neuron is locked and cannot send or receive spikes. If the neuron is not available at a given time, it will not receive or send spikes due to the lack of a schedule for this period of time. Moreover, the universality of DSN P systems in both generating and accepting modes is proved. And a universal DSN P system having 81 neurons for computing functions is also proved.http://dx.doi.org/10.1155/2021/6817636 |
| spellingShingle | Qianqian Ren Xiyu Liu Delayed Spiking Neural P Systems with Scheduled Rules Complexity |
| title | Delayed Spiking Neural P Systems with Scheduled Rules |
| title_full | Delayed Spiking Neural P Systems with Scheduled Rules |
| title_fullStr | Delayed Spiking Neural P Systems with Scheduled Rules |
| title_full_unstemmed | Delayed Spiking Neural P Systems with Scheduled Rules |
| title_short | Delayed Spiking Neural P Systems with Scheduled Rules |
| title_sort | delayed spiking neural p systems with scheduled rules |
| url | http://dx.doi.org/10.1155/2021/6817636 |
| work_keys_str_mv | AT qianqianren delayedspikingneuralpsystemswithscheduledrules AT xiyuliu delayedspikingneuralpsystemswithscheduledrules |