Abstract
Spiking neural P systems simulate the biological phenomena that the neurons cooperate to deal with spikes via synapses. In order to make the system more controllable, we introduce a new class of SNP systems, namely SNP systems with time delay (in short, TDSNP systems). In this systems, we set an initial time and a delay time for each rule. By this new way, we can use less neurons to construct each module of our system. Seen as number computing devices and number accepting devices respectively, TDSNP systems are shown to be computationally complete, both in the generating mode and the accepting mode.
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Acknowledgement
Project supported by National Natural Science Foundation of China (61472231, 61502283, 61876101, 61802234, 61806114), Ministry of Education of Humanities and Social Science Research Project, China (12YJA630152), Social Science Fund Project of Shandong Province, China (16BGLJ06, 11CGLJ22), China Postdoctoral Project (2017M612339).
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Ma, Z., Liu, X. (2019). Spiking Neural P Systems with Time Delay. In: Tang, Y., Zu, Q., RodrÃguez GarcÃa, J. (eds) Human Centered Computing. HCC 2018. Lecture Notes in Computer Science(), vol 11354. Springer, Cham. https://doi.org/10.1007/978-3-030-15127-0_21
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