Skip to main content
Log in

Asynchronous Spiking Neural P Systems with Anti-Spikes

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Spiking neural P systems with anti-spikes (ASN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes and inhibitory spikes. ASN P systems working in the synchronous manner with standard spiking rules have been proved to be Turing completeness, do what Turing machine can do. In this work, we consider the computing power of ASN P systems working in the asynchronous manner with standard rules. As expected, the non-synchronization will decrease the computability of the systems. Specifically, asynchronous ASN P systems with standard rules can only characterize the semilinear sets of natural numbers. But, by using weighted synapses, asynchronous ASN P systems can achieve the equivalence with Turing machine again. It implies that weighted synapses has some “programming capacity” in the sense of achieving computing power. The obtained results have a nice interpretation: the loss in power entailed by removing the synchronization from ASN P systems can be compensated by using weighted synapses among connected neurons.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Cavaliere M, Ibarra OH, Păun G (2009) Asynchronous spiking neural P systems. Theor Comput Sci 410:2352–2364

    Article  MATH  Google Scholar 

  2. Chen H, Freund R, Ionescu M (2007) On string languages generated by spiking neural P systems. Fundamenta Informaticae 75(1–4):141–162

    MathSciNet  MATH  Google Scholar 

  3. Cheng L, Hou Z, Lin Y, Tan M, Zhang W, Wu F (2011) Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks. IEEE Trans Neural Netw 22:714–726

    Article  Google Scholar 

  4. Cheng L, Hou Z, Tan M (2009) Solving linear variational inequalities by projection neural network with time-varying delays. Phys Lett A 373:1739–1743

    Article  MATH  Google Scholar 

  5. Cheng L, Lin Y, Hou Z, Tan M, Huang J, Zhang W (2011) Adaptive tracking control of hybrid machines: a closed-chain five-bar mechanism case. IEEE/ASME Trans Mechatron 16:1155–1163

    Article  Google Scholar 

  6. Gerstner W, Kistler W (2002) Spiking Neuron Models. Single Neurons. Populations, Plasticity. Cambridge University Press, Cambridge

    Book  Google Scholar 

  7. Harju T, Ibarra OH, Karhamaki J (2002) Some decision problems concerning semilinearity and communtation. J Comput Syst Sci 65:278–294

    Article  MATH  Google Scholar 

  8. Ionescu M, Păun G, Yokomori T (2006) Spiking neural P systems. Fundamenta Informaticae 71(2–3):279–308

    MathSciNet  MATH  Google Scholar 

  9. Maass W (2002) Computing with spikes. Spec Issue Found Inf Process TELEMATIK. 8(1):32–36

    Google Scholar 

  10. Minsky M (1967) Computation - finite and infinite machines. Prentice Hall, New Jersey

    MATH  Google Scholar 

  11. Niu Y, Pan L, Pérez-Jiménez MJ (2011) A tissue systems based uniform solution to tripartite matching problem. Fundamenta Informaticae 109:1–10

    MathSciNet  Google Scholar 

  12. Pan L, Păun G (2009) Spiking neural P systems with anti-spikes. Intern J Comput, Commun Control 4(3):273–282

    Google Scholar 

  13. Pan L, Păun G (2010) Spiking neural P systems: an improved normal form. Theor Comput Sci 411(6):906–918

    Article  MATH  Google Scholar 

  14. Pan L, Păun G, Pérez-Jiménez MJ (2011) Spiking neural P systems with neuron division and budding. Sci Chi Inf Sci 54(8):1596–1607

    Article  MATH  Google Scholar 

  15. Pan L, Wang J, Hoogeboom HJ (2012) Spiking neural P systems with astrocytes. Neural Comput 24:1–24

    Article  MathSciNet  Google Scholar 

  16. Pan L, Zeng X, Zhang X, Jiang Y (2012) Spiking neural P systems with weighted synapses. Neural Process Lett 35(1):13–27

    Article  Google Scholar 

  17. Păun A, Păun G (2007) Small universal spiking neural P systems. BioSystems 90:48–60

    Article  Google Scholar 

  18. Păun G (2000) Computing with membranes. J Comput Syst Sci 61(1):108–143

    Article  MathSciNet  MATH  Google Scholar 

  19. Rozenberg G, Salomaa A (1997) Handbook of formal languages. Springer-Verlag, Berlin

    Book  MATH  Google Scholar 

  20. Song T, Wang X (2014) Homogeneous spiking neural P systems with inhibitory synapses. Neural Process Lett. doi:10.1007/s11063-014-9352-y

  21. Wang J, Hoogeboom HJ, Pan L, Păun G, Pérez-Jiménez MJ (2010) Spiking neural P systems with weights. Neural Comput 22(10):2615–2646

    Article  MathSciNet  MATH  Google Scholar 

  22. Wang X, Hou Z, Zou A, Tan M, Cheng L (2008) A behavior controller based on spiking neural networks for mobile robots. Neurocomputing 71:655–666

    Article  Google Scholar 

  23. Zhang G, Rong H, Neri F, Pérez-Jiménez MJ (2014) An optimization spiking neural P system for approximately solving combinatorial optimization problems. Intern J Neural Syst 24(5):1–16

    Article  Google Scholar 

  24. Zhang X, Jiang Y, Pan L (2010) Small universal spiking neural P systems with exhaustive use of rules. J Comput Theor Nanosci 7(5):1–10

    Google Scholar 

  25. Zhang X, Wang J, Pan L (2009) A note on the generative power of axon systems. Intern J Comput, Commun Control 4(1):92–98

    Google Scholar 

  26. Zhang X, Wang S, Niu Y, Pan L (2011) Tissue P systems with cell separation: attacking the partition problem. Sci Chin 54(2):293–304

    MATH  Google Scholar 

Download references

Acknowledgments

This work was supported by National Natural Science Foundation of China (61202011, 61033003, 91130034, 61100145, 61272071,61320106005), China Postdoctoral Science Foundation funded project (2014M550389), Base Research Project of Shenzhen Bureau of Science, Technology, and Information (JC201006030858A), and Natural Science Foundation of Hubei Province (2011CDA027).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiangxiang Zeng.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, T., Liu, X. & Zeng, X. Asynchronous Spiking Neural P Systems with Anti-Spikes. Neural Process Lett 42, 633–647 (2015). https://doi.org/10.1007/s11063-014-9378-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-014-9378-1

Keywords

Navigation