基于合作规则的脉冲神经膜系统的小通用性

俞洋, 吴庭芳, 贺娟娟

系统工程理论与实践 ›› 2017, Vol. 37 ›› Issue (9) : 2465-2473.

PDF(572 KB)
中国科学院数学与系统科学研究院期刊网
PDF(572 KB)
系统工程理论与实践 ›› 2017, Vol. 37 ›› Issue (9) : 2465-2473. DOI: 10.12011/1000-6788(2017)09-2465-09
论文

基于合作规则的脉冲神经膜系统的小通用性

    俞洋1, 吴庭芳2, 贺娟娟3
作者信息 +

Smaller universal spiking neural P systems with cooperating rules

    YU Yang1, WU Tingfang2, HE Juanjuan3
Author information +
文章历史 +

摘要

基于合作规则的脉冲神经膜系统是一类受神经元利用脉冲进行信息处理与通讯的生物功能启发得到的分布式并行计算模型的变体.在这类系统中，每个神经元具有相同有限数量的规则集合，且所有神经元中的集合用相同的标签进行标记，规则的集合称为组件.文中研究了基于合作规则的脉冲神经膜系统的小通用性：作为产生数的装置，构造了一个需要6个神经元的通用脉冲神经膜系统，这个结果回答了Metta等提出的一个公开问题.

Abstract

Spiking neural P systems are a class of distributed parallel computing models inspired from the way neurons process and communicate information by means of spikes, spiking neural P systems with cooperating rules are a new variant of spiking neural P systems, where each neuron has the same finite number of sets of rules, labelled identically, each set is called a component. In this work, we continue the study of small SN P systems with cooperating rules and we improve in the number of neurons to 6. Specifically, we construct a Turing universal SN P system having 6 neurons, which can generate any set of Turing computable natural numbers. This result answers to an open problem formulated by Metta, et al.

关键词

生物启发的计算 / 膜计算 / 脉冲神经膜系统 / 合作规则 / 通用性

Key words

bio-inspired computing / membrane computing / spiking neural P system / cooperating rule / universality

引用本文

EndNote

Ris (Procite)

Bibtex

导出引用
俞洋 , 吴庭芳 , 贺娟娟. 基于合作规则的脉冲神经膜系统的小通用性. 系统工程理论与实践, 2017, 37(9): 2465-2473 https://doi.org/10.12011/1000-6788(2017)09-2465-09
YU Yang , WU Tingfang , HE Juanjuan. Smaller universal spiking neural P systems with cooperating rules. Systems Engineering - Theory & Practice, 2017, 37(9): 2465-2473 https://doi.org/10.12011/1000-6788(2017)09-2465-09
中图分类号： TP301   

参考文献

[1] Pǎun G. Membrane computing-An introduction[M]. Berlin:Springer-Verlag, 2002.
[2] Pǎun G, Rozenberg G, Salomaa A. The Oxford handbook of membrane computing[M]. Oxford University Press, 2010.
[3] Ionescu M, Pǎun G, Yokomori T. Spiking neural P systems[J]. Fundamenta Informaticae, 2006, 71(2):279-308.
[4] 张葛祥, 潘林强. 自然计算的新分支-膜计算[J]. 计算机学报, 2010, 33(2):208-214. Zhang G X, Pan L Q. A survey of membrane computing as a new branch of natural computing[J]. Chinese Journal of Computers, 2010, 33(2):208-214.
[5] Chen H M, Freund R, Ionescu M, et al. On string languages generated by spiking neural P systems[J]. Fundamenta Informaticae, 2007, 75(1):141-162.
[6] Chen H M, Ionescu M, Ishdorj T O, et al. Spiking neural P systems with extended rules, universality and languages[J]. Natural Computing, 2008, 7(2):147-166.
[7] Pǎun A, Pǎun G. Small universal spiking neural P systems[J]. Biosystems, 2007, 90(1):48-60.
[8] Zhang X Y, Zeng X X, Pan L Q. Smaller universal spiking neural P systems[J]. Fundamenta Informaticae, 2008, 87(1):117-136.
[9] Neary T. A universal spiking neural P system with 11 neurons[C]//Proceedings of the 11th International Conference on Membrane Computing, Germany, 2010:327-346.
[10] Pan L Q, Pǎun G. Spiking neural P systems with anti-spikes[J]. Internationanl Journal of Computers, Communications & Control, 2009, IV(3):273-282.
[11] Song T, Pan L Q, Wang J, et al. Normal forms of spiking neural P systems with anti-spikes[J]. IEEE Transactions on NanoBioscience, 2012, 11(4):352-359.
[12] Cavaliere M, Ibarra O H, Pǎun G, et al. Asynchronous spiking neural P systems[J]. Theoretical Computer Science, 2009, 410(24):2352-2364.
[13] Song T, Pan L Q, Pǎun G. Asynchronous spiking neural P systems with local synchronization[J]. Information Sciences, 2013, 219:197-207.
[14] 谭钢军, 宋勃升, 陈智华, 等. 单边插入-删除(膜)系统计算能力[J]. 系统工程理论与实践, 2013, 33(9):2362-2370. Tan G J, Song B S, Chen Z H, et al. Computation power of one-sided insertion-deletion (P) systems[J]. Systems Engineering-Theory & Practice, 2013, 33(9):2362-2370.
[15] Song T, Pan L Q, Pǎun G. Spiking neural P systems with rules on synapses[J]. Theoretical Computer Science, 2014, 529:82-95.
[16] Song T, Pan L Q. Spiking neural P systems with rules on synapses working in maximum spikes consumption strategy[J]. IEEE Transactions on Nanobioscience, 2014, 14(1):38-44.
[17] Zeng X X, Zhang X Y, Pan L Q. Homogeneous spiking neural P systems[J]. Fundamenta Informaticae, 2009, 97:1-20.
[18] Song T, Wang X, Zhang Z J, et al, Homogenous spiking neural P systems with anti-spikes[J]. Neural Computing and Applications, 2014, 24:1833-1841.
[19] Zhang X Y, Pǎun A, Pan L Q. On the universality of Axon P systems[J]. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(11):2816-2829.
[20] Wu T F, Zhang Z Q, Pǎun G, et al. Cell-like spiking neural P systems[J]. Theoretical Computer Science, 2016, 623:180-189.
[21] Leporati A, Mauri G, Zandron C, et al. Uniform solutions to SAT and Subset Sum by spiking neural P systems[J]. Natural Computing, 2009, 8(4):681-702.
[22] Pan L Q, Pǎun G, Pérez-Jiménez M J. Spiking neural P systems with neuron division and budding[J]. Science China Information Sciences, 2011, 54(8):1596-1607.
[23] Zhang G X, Rong H N, Neri F, et al. An optimization spiking neural P system for approximately solving combinatorial optimization problems[J]. International Journal of Neural Systems, 2014, 24(5):1440006.
[24] Wang T, Zhang G X, Zhao J B, et al. Fault diagnosis of electric power systems based on fuzzy reasoning spiking neural P systems[J]. IEEE Transactions on Power Systems, 2015, 30(3):1182-1194.
[25] Peng H, Wang J, Pérez-Jiménez M J, et al. Fuzzy reasoning spiking neural P systems for fault diagnosis[J]. Information Sciences, 2013, 235:106-116.
[26] 张葛祥, 程吉祥, 王涛, 等. 膜计算:理论与应用[M]. 北京:科学出版社, 2015. Zhang G X, Cheng J X, Wang T, et al. Membrane computing:Theory and applications[M]. Beijing:Science Press, 2015.
[27] Yurii R. Small universal Turing machines[J]. Theoretical Computer Science, 1996, 168:215-240.
[28] Korec I. Small universal register machines[J]. Theoretical Computer Science, 1996, 168(2):267-301.
[29] Neary T. A boundary between universality and non-universality in extended spiking neural P systems[C]//4th International Conference Language and Automata Theory and Applications, 2010:475-487.
[30] Pan L Q, Zeng X X. A note on small universal spiking neural P systems[J]. Lecture Notes in Computer Science, 2010, 5957:436-447.
[31] Song T, Jiang Y, Shi X L, et al. Small universal spiking neural P systems with anti-spikes[J]. Journal of Computational and Theoretical Nanoscience, 2013, 10(4):999-1006.
[32] Pan L Q, Zeng X X, Zhang X Y. Small universal asynchronous spiking neural P systems[C]//Proceedings of IEEE Fifth International Conference on Bio-Inspired Computing:Theories and Applications, 2010:622-630.
[33] Pǎun A, Sidorff M. Sequentiality induced by spike number in SNP systems:Small universal machines[J]. Lecture Notes in Computer Science, 2012, 7184:333-345.
[34] Song T, Xu J B, Pan L Q. On the universality and non-universality of spiking neural P system with rules on synapses[J]. IEEE Transactions on Nanobioscience, 2015, 14(8):960-966.
[35] Meersman R, Rozenberg G. Cooperating grammar systems[M]. Springer:Heidelberg, 1978.
[36] Metta V P, Raghuraman S, Krithivasan K. Spiking neural P systems with cooperating rules[C]//15th International Conference on Membrane Computing, 2014:267-282.
[37] Metta V P, Raghuraman S, Krithivasan K. Small universal spiking neural P systems with cooperating rules as function computing devices[C]//15th International Conference on Membrane Computing, 2014:300-313.
[38] Rozenberg G, Salomaa A. Handbook of formal languages[M]. Berlin:Springer-Verlag, 1997.
[39] Song T, Pan L Q. A small universal spiking neural P system with cooperating rules[J]. Romanian Journal of Information Science and Technology, 2014, 17(2):177-189.
[40] Minsky M. Computation-Finite and infinite machines[M]. Englewood Cliffs:Prentice.Hall, 1967.

基金

国家自然科学基金（61320106005，61472293，91130034）；湖北省自然科学基金（2015CFB335）
PDF(572 KB)

368

Accesses

0

Citation

Detail
段落导航
相关文章

/

〈 〉