Skip to main content
SpringerLink
Log in

Neural information coding on small-world spiking neuronal networks modulated by spike-timing-dependent plasticity under external noise stimulation

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Neural information coding is the fundamental of information processing mechanism in biological neural network. The study of neural information coding can help to understand the function of information processing in biological neural network and lay the theoretical foundation for improving bionic ability. As the abstract of a large number of real complex systems, small-world networks have the properties of biological neural networks. However, the neural information coding based on the small-world topology is rarely studied and the information transmission mechanism among the neurons is mostly excitatory regulation mechanism of spike-timing-dependent plasticity (STDP). In this paper, the small-world network is constructed and its properties are analyzed; the small-world spiking neural network based on the more complete STDP including excitatory synapse and inhibitory synapse is constructed; from the angle of firing rate of neurons and the temporal structure of the spike train, the properties of information coding on the small-world spiking neural network under the stimulations of white Gauss noise and impulse noise are analyzed respectively. Our experimental results indicate that under the same stimulation, the responses of the mean rate coding and ISI coding of the small-world network are both enhanced with the increase of stimulation intensity; under different stimulations, the mean rate coding and ISI coding of the small-world network show respective specificity.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Yu, K., Wang, J., Deng, B., Wei, X.L.: Synchronization of neuron population subject to steady DC electric field induced by magnetic stimulation. Cogn. Neurodyn. 7, 237–252 (2013)

    Article  Google Scholar 

  2. Laurent, G., Wehr, M., Davidowitz, H.: Temporal representations of odors in an olfactory network. J. Neurosci. 16, 3837 (2001)

    Article  Google Scholar 

  3. Panzeri, S., Harvey, C.D., Piasini, E.: Cracking the neural code for sensory perception by combining statistics, intervention, and behavior. Neuron 93, 491 (2017)

    Article  Google Scholar 

  4. Parker, D.: Neuronal network analyses: premises, promises and uncertainties. Philos. Trans. R. Soc. Lond. 365, 2315–28 (2010)

    Article  Google Scholar 

  5. Zemanová, L., Zamora-López, G., Zhou, C.: Complex brain networks: from topological communities to clustered dynamics. Pramana 70, 1087–1097 (2008)

    Article  Google Scholar 

  6. van Rossum, M.C.W., Turrigiano, G.G., Nelson, S.B.: Fast propagation of firing rates through layered networks of noisy neurons. J. Neurosci. 22, 1956–1966 (2002)

    Article  Google Scholar 

  7. Yu, H., Guo, X., Wang, J.: Spike coherence and synchronization on Newman–Watts small-world neuronal networks modulated by spike-timing-dependent plasticity. Physica A 419, 307–317 (2015)

    Article  MathSciNet  Google Scholar 

  8. Haibo, Q.U., Su, L.U., Zhang, W.: Analysis of the characteristics of infantile small world neural network node properties correlated with the influencing factors. J. Biomed. Eng. 33, 931–938 (2016)

    Google Scholar 

  9. Litwinkumar, A., Doiron, B.: Formation and maintenance of neuronal assemblies through synaptic plasticity. Nat. Commun. 5, 5319 (2013)

    Article  Google Scholar 

  10. Rumbell, T., Denham, S.L., Wennekers, T.: A spiking self-organizing map combing STDP, oscillations and continuous learning. IEEE Trans. Neural. Netw. Learn. Syst. 25, 894–907 (2014)

    Article  Google Scholar 

  11. Wei, Y., Koulakov, A.A.: Long-term memory stabilized by noise-induced rehearsal. J. Neurosci. 34, 15804–15815 (2014)

    Article  Google Scholar 

  12. Goudar, V., Buonomano, D.V.: A model of order-selectivity based on dynamic changes in the balance of excitation and inhibition produced by short-term synaptic plasticity. J. Neurophysiol. 113, 509–523 (2015)

    Article  Google Scholar 

  13. Wang, R., Wu, Y., Wang, L.: Structure and dynamics of self-organized neuronal network with an improved STDP rule. Nonlinear Dynam. 1, 1–14 (2017)

    Article  MathSciNet  Google Scholar 

  14. Effenberger, F., Jost, J., Levina, A.: Self-organization in balanced state networks by STDP and homeostatic plasticity. PloS Comput. Biol. 11, e1004420 (2015)

    Article  Google Scholar 

  15. Rumbell, T., Denham, S.L., Wennekers, T.: A spiking self-organizing map combining STDP, oscillations, and continuous learning. IEEE Trans. Neural Netw. Learn. Syst. 25, 894–907 (2014)

    Article  Google Scholar 

  16. Wang, M.L., Wang, J.S.: Dynamical balance between excitation and inhibition of feedback neural circuit via inhibitory synaptic plasticity. Acta Phys. Sin. 64, 416–423 (2015)

    Google Scholar 

  17. Yu, H.T., Wang, J., Liu, Q.X., Deng, B., Wei, X.L.: Delayed feedback control of bursting synchronization in small-world neuronal networks. Neurocomputing 99, 178–187 (2013)

    Article  Google Scholar 

  18. Mikkelsen, K., Imparato, A., Torcini, A.: Emergence of slow oscillations in neural networks with spike-timing dependent plasticity. Phys. Rev. Lett. 110, 208101 (2013)

    Article  Google Scholar 

  19. Guo, L., Wang, Y., Yu, H.: Study of brain functional network based on sample entropy of EEG under magnetic stimulation at PC6 acupoint. Bio-med. Mater. Eng. 24, 1063–1069 (2014)

    Google Scholar 

  20. Guo, L., Wang, Y., Yu, H.: Brain functional network based on approximate entropy of EEG under magnetic stimulation at acupuncture point. Trans. CES 30, 31–38 (2015)

    Google Scholar 

  21. Izhikevich, E.M.: Simple model of spiking neurons. IEEE Trans. Neural Netw. 14, 1569 (2003)

    Article  Google Scholar 

  22. Srinivasa, N., Cho, Y.: Unsupervised discrimination of patterns in spiking neural networks with excitatory and inhibitory synaptic plasticity. Front. Comput. Neurosci. 8, 159 (2014)

    Article  Google Scholar 

  23. Kleberg, F.I., Fukai, T., Gilson, M.: Excitatory and inhibitory STDP jointly tune feedforward neural circuits to selectively propagate correlated spiking activity. Front. Comput. Neurosci. 8, 53 (2014)

    Article  Google Scholar 

  24. Vogels, T.P., Sprekeler, H., Zenke, F.: Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks. Science 334, 1569–1573 (2011)

    Article  Google Scholar 

  25. Song, S., Miller, K.D., Abbott, L.F.: Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3, 919–926 (2010)

    Article  Google Scholar 

  26. Perrinet, L., Delorme, A., Samuelides, M.: Networks of integrate-and-fire neuron using rank order coding A: how to implement spike time dependent Hebbian plasticity. Neurocomputing 38–40, 817–822 (2001)

    Article  Google Scholar 

  27. Tsubo, Y., Isomura, Y., Fukai, T.: Power-law inter-spike interval distributions infer a conditional maximization of entropy in cortical neurons. PloS Comput. B 8, e1002461 (2012)

    Article  MathSciNet  Google Scholar 

  28. George, D., Sommer, F.T.: Computing with inter-spike interval codes in networks of integrate and fire neurons. Neurocomputings 65–66, 415–420 (2005)

    Article  Google Scholar 

  29. Yang, Y., Ramamurthy, B., Neef, A.: Low somatic sodium conductance enhances action potential precision in time-coding auditory neuron. J. Neurosci. 36(47), 11999–12009 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Nos. 61571180, 31400844) and Natural Science Foundation of Hebei Province (No. E2016202128).

Author information

Authors and Affiliations

  1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, 300130, People’s Republic of China

    Lei Guo, Wei Zhang & Jialei Zhang

Authors
  1. Lei Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jialei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lei Guo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, L., Zhang, W. & Zhang, J. Neural information coding on small-world spiking neuronal networks modulated by spike-timing-dependent plasticity under external noise stimulation. Cluster Comput 22 (Suppl 3), 5217–5231 (2019). https://doi.org/10.1007/s10586-017-1188-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1188-6

Keywords

Search

Navigation