Homogenous Spiking Neural P Systems with Inhibitory Synapses

Neural Processing Letters

Volumn 42, Issue 1, 2015, Pages 199-214

  Song, Tao ^a   Wang, Xun ^b  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b UNIVERSITY OF TSUKUBA   (Japan)

Author keywords

Anti spike; Membrane computing; P system; Spiking neural P system; Turing completeness

Indexed keywords

BIOINFORMATICS; NEURONS;

ANTI-SPIKE; MEMBRANE COMPUTING; P SYSTEMS; SPIKING NEURAL P SYSTEMS; TURING COMPLETENESS;

COMPUTABILITY AND DECIDABILITY;

EID: 84937629335     PISSN: 13704621     EISSN: 1573773X     Source Type: Journal    
DOI: 10.1007/s11063-014-9352-y     Document Type: Article

References (44)

1. Rozenberg G, Bck T, Kok JN (2011) Handbook of natural computing. Springer, Berlin
2. Păun G (2000) Computing with membranes. J Comput Syst Sci 61(1):108–143
3. Freund R, Păun G, Pérez-Jiménez MJ (2005) Tissue-like P systems with channel-states. Theor Comput Sci 330:101–116
4. Ionescu M, Păun G, Yokomori T (2006) Spiking neural P systems. Fund Inf 71(2–3):279–308
5. Nishida TY (2004) An approximate algorithm for NP-complete optimization problems exploiting P systems. In Proceedings of Brainstorming Workshop on Uncertainty in Membrane, Computing, 185–192
6. Zhang G, Cheng J, Gheorghe M, Meng Q (2013) A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems. Appl Soft Comput 13(3):1528–1542
7. Păun G, Rozenberg G, Salomaa A (2010) The Oxford handbook of membrane computing. Oxford University Press, Oxford
8. Siegelmann HT, Sontag ED (1995) On the computational power of neural nets. J Comput System Sci 50(1):132–150
9. Pan L, Zeng X (2010) A note on small universal spiking neural P systems. LNCS 5957:436–447
10. Pollack JB (1991) The induction of dynamical recognizers. Mach Learn 7(2–3):227–252
11. Giles CL, Omlin CW (1992), Inserting rules into recurrent neural networks. Neural Networks for Signal Processing II. In Proceedings of the Signal Processing, Workshop, 13–22
12. Hyoetyniemi H (1996) Turing machines are recurrent neural networks. In Proceedings of Genes, Nets And Symbols. Helsinki, Finland: Finnish Artificial Intelligence Society, 13–24
13. Moore C (1998) Dynamical recognizers: real-time language recognition by analog computers. Theor Comput Sci 201:99–136
14. Maass W (1997) Networks of spiking neurons: the third generation of neural network models. Neural Netw 10:1659–1671
15. Maass W, Schmitt M (1999) On the complexity of learning for spiking neurons with temporal coding. Inf Comput 153(1):26–46
16. Natschläger T, Maass W (2002) Spiking neurons and the induction of finite state machines. Theor Comput Sci 287(1):251–265
17. Ghosh DS, Adeli H (2009) Spiking neural networks. Int J Neural Syst 19(4):295–308
18. Pan L, Păun G (2010) Spiking neural P systems: an improved normal form. Theor Comput Sci 411(6):906–918
19. Zeng X, Pan L (2009) Homogeneous spiking neural P systems. Fund Inf 97:275–294
20. Chen H, Freund R, Ionescu M et al (2007) On string languages generated by spiking neural P systems. Fund Inf 75(1–4):141–162
21. Zhang X, Zeng X, Pan L (2008) On string languages generated by SN P systems with exhaustive use of rules. Nat Comput 7(4):535–549
22. Pan L, Zeng X (2011) Small universal spiking neural P systems working in exhaustive mode. IEEE Trans Nanobiosci 10(2):99–105
23. Păun A, Păun G (2007) Small universal spiking neural P systems. Biosystems 90:48–60
24. Ishdorj TO, Leporati A, Pan L et al (2010) Deterministic solutions to QSAT and Q3SAT by spiking neural P systems with pre-computed resources. Theor Comput Sci 411(25):2345–2358
25. Pan L, Păun G, Pérez-Jiménez MJ (2011) Spiking neural P systems with neuron division and budding. Sci China Inform Sci 54(8):1596–1607
26. Ionescu M, Sburlan D (2007) Some applications of spiking neural P systems. In Proceedings of the Eighth Workshop on Membrane Computing, Thessaloniki, 383–394
27. Cavaliere M, Ibarra OH, Păun G (2009) Asynchronous spiking neural P systems. Theor Comput Sci 410:2352–2364
28. Song T, Pan L, Păun G (2013) Asynchronous spiking neural P systems with local synchronization. Inf Sci 219:197–207
29. Pan L, Wang J, Hoogeboom HJ (2012) Spiking neural P systems with astrocytes. Neural Comput 24:1–24
30. Macías-Ramos LF, Pérez-Jiménez MJ (2013) Spiking neural P systems with functional astrocytes. LNCS 7762:228–242
31. Păun G (2007) Spiking neural P systems with astrocyte-like control. J Univers Comput Sci 13(11):1707–1721
32. Song T, Pan L (2014) Spiking neural P systems with rules on synapses. Theor Comput Sci 529:82–95
33. Pan L, Păun G (2009) Spiking neural P systems with anti-spikes. Int J Comput Commun 4(3):273–282
34. Song T, Pan L, Wang J et al (2012) Normal forms of spiking neural P systems with anti-spikes. IEEE Trans Nanobiosci 4(11):352–359
35. Rozenberg G, Salomaa A (1997) Handbook of formal languages. Springer, Berlin
36. Minsky M (1967) Computation: finite and infinite machines. Prentice Hall, Upper Saddle River
37. Păun G (2002) Membrane computing: an introduction. Springer, Berlin
38. Cavaliere M, Egecioglu O, Ibarra OH et al (2009) Asynchronous spiking neural P systems. Theor Comput Sci 410:2352–2364
39. Song T, Wang X, Zhang Z et al (2013) Homogenous spiking neural P systems with anti-spikes. Neural Comput Appl. doi:10.1007/s00521-013-1397-8
40. Nishida TY (2006) Membrane algorithms: approximate algorithms for np-complete optimization problems. Applications of membrane computing. Springer, Berlin
41. Zhang G, Gheorghe M, Li Y (2012) A membrane algorithm with quantum-inspired subalgorithms and its application to image processing. Nat Comput 11(4):701–717
42. Zhao J, Wang N (2011) Hybrid optimization method based on membrane computing. Ind Eng Chem Res 50(3):1691–1704
43. Wang K, Wang N (2011) A protein inspired RNA genetic algorithm for parameter estimation in hydrocracking of heavy oil. Chem Eng J 167(1):228–239
44. Xiao J, Zhang X, Xu J (2012) A membrane evolutionary algorithm for DNA sequence design in DNA computing. Chin Sci Bull 57(1):698–706