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A Note on Spiking Neural P Systems with Homogenous Neurons and Synapses

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Języki publikacji
EN
Abstrakty
EN
Spiking neural (SN, for short) P systems are a class of computation models inspired from the way in which neurons communicate by exchanging spikes. SN P systems with homogenous neurons and synapses are a new variant of SN P systems, where the spiking and forgetting rules are placed on synapses instead of in neurons and each synapse has the same set of spiking and forgetting rules. Recent studies illustrated that this variant of SN P systems is Turing universal as both number generating and accepting devices. In this note, we prove that SN P systems with homogenous neurons and synapses without the feature of delay are also Turing universal. This result gives a positive answer to an open problem formulated in [K. Jiang, et al. Neurocomputing 171(2016) 1548-1555] “whether SN P systems with homogenous neurons and synapses are Turing universal when the feature of delay is not used”.
Wydawca
Rocznik
Strony
231--240
Opis fizyczny
Bibliogr. 27 poz., rys.
Twórcy
autor
  • Shanghai Institute of Science & Technology Management, Shanghai 201800, Shanghai, China
autor
  • Key Laboratory of Image Information Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
autor
  • Key Laboratory of Image Information Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
autor
  • School of Electric and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
autor
  • School of Computer Science, Wuhan University of Science and Technology, Wuhan 420081, Hubei, China
Bibliografia
  • [1] Ionescu M, Păun G, Yokomori T. Spiking neural P systems. Fundamenta Informaticae, 2006;71 (2-3): 279-308. URL http://dl.acm.org/citation.cfm?id=1227505.1227513.
  • [2] Păun G, Rozenberg G, Salomaa A (eds.). The Oxford Handbook of Membrane Computing. Oxford University Press, New York, 2010. ISBN: 0199556679.
  • [3] Zeng X, Zhang X, Song T, Pan L. Spiking neural P systems with thresholds. Neural Computation, 2014; 26 (7): 1340-1361. doi: 10.1162/NECO$\_$a$\_$00605.
  • [4] Zhang X, Pan L, Paun A. On the universality of axon P systems. IEEE Transactions on Neural Networks and Learning Systems, 2015; 26 (11): 2816-2829. doi: 10.1109/TNNLS.2015.2396940.
  • [5] Cabarle FGC, Adorna HN, Pérez-Iiménez MJ, Song T. Spiking neural P systems with structural plasticity. Neural Computing and Applications, 2015; 26 (8): 1905-1917. doi: 10.1007/s00521-015-1857-4.
  • [6] Wu T, Zhang Z, Păun G, Pan L. Cell-like spiking neural P systems. Theoretical Computer Science, 2016: 623: 180-189. doi: 10.1016/j.tcs.2015.12.038.
  • [7] Spiking neural P systems with request rules. Neurocomputing. 2016; 193 (C): 193-200, doi: 10.1016/j.neucom.2016.02.023.
  • [8] Cavaliere M, Ibarra OH, Păun G, Egecioglu O, Ionescu M, Woodworth S. Asynchronous spiking neural P systems. Theoretical Computer Science, 2009; 410 (24): 2352-2364. doi: 10.1016/j.tcs.2009.02.031.
  • [9] Song T, Pan L, Păun G. Asynchronous spiking neural P systems with local synchronization. Information Sciences, 2013; 219: 197-207. doi: 10.1016/j.ins.2012.07.023.
  • [10] Ibarra OH, Păun A, Rodríguez-Patón A. Sequential SNP systems based on min/max spike number. Theoretical Computer Science, 2009; 410 (30): 2982-2991. doi: 10.1016/j.tcs.2009.03.004.
  • [11] Jiang K, Song T. Pan L. Universality of sequential spiking neural P systems based on minimum spike number. Theoretical Computer Science, 2013; 499: 88-97. doi: 10.1016/j.tcs.2013.07.006.
  • [12] Ionescu M, Păun G, Yokomori T. Spiking neural P systems with an exhaustive use of rules. International Journal of Unconventional Computing, 2007; 3 (2): 135-153. URL http://direct.bl.uk/bld/PlaceOrder.do?UIN=210412711&ETOC=RN&from=searchengine.
  • [13] Zhang X, Wang B, Pan L. Spiking neural P systems with a generalized use of rules. Neural Computation, 2014; 26 (12): 2925-2943. doi: 10.1162/NECO$\_$a$\_$00665.
  • [14] Leporati A, Mauri G, Zandron C, Păun G, Pérez-Jiménez MJ. Uniform solutions to SAT and Subset Sum by spiking neural P systems. Natural Computing, 2008; 8 (4): 681-702. doi: 10.1007/s11047-008-9091-y.
  • [15] Pan L, Păun G, Pérez-Jiménez MJ. Spiking neural P systems with neuron division and budding. Science China Information Sciences, 2011; 54 (8): 1596-1607. doi: 10.1007/s11432-011-4303-y.
  • [16] Ishdorj TO, Leporati A, Pan L, Zeng X, Zhang X. Deterministic solutions to QSAT and Q3SAT by spiking neural P systems with pre-computed resources. Theoretical Computer Science, 2010; 411 (25): 2345—2358. doi: 10.1016/j.tcs.2010.01.019.
  • [17] Díaz-Pernil D, Peña-Cantillana F, Gutiérrez-Naranjo MA. A parallel algorithm for skeletonizing images by using spiking neural P systems. Neurocomputing, 2013; 115: 81-91. doi: 10.1016/j.neucom.2012.12.032.
  • [18] Peng H, Wang J, Pérez-Jiménez MJ, Wang H, Shao J, Wang T. Fuzzy reasoning spiking neural P system for fault diagnosis. Information Sciences, 2013; 235: 106-116. doi: 10.1016/j.ins.2012.07.015.
  • [19] Wang T, Zhang G. Zhao J, He Z, Wang J, Pérez-Jiménez MJ. Fault diagnosis of electric power systems based on fuzzy reasoning spiking neural P systems. IEEE Transactions on Power Systems, 2015; 30 (3): 1182-1194. doi: 10.1109/TPWRS.2014.2347699.
  • [20] Wang J, Shi P, Peng H, Pérez-Jiménez MJ, Wang T. Weighted fuzzy spiking neural P systems. IEEE Transactions on Fuzzy Systems, 2013; 21 (2): 209-220. doi: 10.1109/TFUZZ.2012.2208974.
  • [21] Macías-Ramos LF, Pérez-Hurtado I, García-Quismondo M, Valencia-Cabrera L, Pérez-Jiménez MJ, Riscos-Núñez A. P-Lingua based simulator for spiking neural P systems. In: Proceedings of the 12th International Conference on Membrane Computing, August 23-26, 2011, Fontainebleau, France, M. Gheorghe et al. (eds.), volume 7184. LNCS, Springer, 2012 pp. 257-281. doi: 10.1007/978-3-642-28024-5$\_$18.
  • [22] Macías-Ramos LF, Pérez-Jiménez MJ, Song T, Pan L. Extending Simulation of Asynchronous Spiking Neural P Systems in P-Lingua. Fundamenta Informaticae, 2015; 136 (3): 253-267. doi: 10.3233/FI-2015-1156.
  • [23] Cabarle FG, Adorna H, Martínez-del Amor MA, Pérez-Jiménez MJ. Spiking neural P system simulations on a high performance GPU platform. In: Algorithms and Architectures for Parallel Processing, Y. Xiang et al. (eds.). volume 7017. LNCS, Springer. 2011 pp. 99-108. doi:10.1007/978-3-642-24669-2$\_$10.
  • [24] Cabarle FGC, Adorna H, Martínez-del Amor MA. A spiking neural P system simulator based on CUDA. In: Proceedings of the 12th International Conference on Membrane Computing, August 23-26, 2011, Fontainebleau, France, M. Gheorghe et al. (eds.), volume 7184. LNCS, Springer, 2012 pp. 87-103. doi: 10.1007/978-3-642-28024-5$\_$18.
  • [25] Song T, Pan L, Păun G. Spiking neural P systems with rules on synapses. Theoretical Computer Science, 2014; 529: 82-95. doi: 10.1016/j.tcs.2014.01.001.
  • [26] Jiang K, Chen W, Zhang Y, Pan L. Spiking neural P systems with homogeneous neurons and synapses. Neurocomputing, 2016; 171: 1548-1555. doi: 10.1016/j.neucom.2015.07.097.
  • [27] Minsky M. Computation: Finite and Infinite Machines. Prentice-Hall, Englewood Cliffs, N.J., 1967. ISBN-13: 978-0131655638.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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