Skip to main content
Log in

Using interspike intervals to quantify noise effects on spike trains in temperature encoding neurons

  • Original Research
  • Published:
Cognitive Neurodynamics Aims and scope Submit manuscript

Abstract

This paper examines how noise interacts with the non-linear dynamical mechanisms of neuronal stimulus. We study the spike trains generated by a minimal Hodgkin-Huxley type model of a cold receptor neuron. The distributions of interspike intervals(ISIs) of purely deterministic simulations exhibit considerable differences compared to the noisy ones. We quantify the effect of noise using ISI return plots and the ISI-distance recently proposed by Kreuz et al. (J Neurosci Meth, 165:151–161, 2007). It is shown that the spike trains of a cold receptor neuron are more strongly affected by noise for low temperatures than for high temperatures. This trend is also observed in both regimes of cold receptors: tonic firing(which occurs for low and high temperatures) and bursting (which occurs for intermediate temperatures).

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
Fig. 6

Similar content being viewed by others

References

  • Braun HA, Huber MT, Dewald M, Schäfer K, Voigt K (1998a) Computer simulations of neuronal signal transduction: the role of nonlinear dynamics and noise. Int J Bifurcat Chaos 8:881–889

    Article  Google Scholar 

  • Braun HA, Schäfer K, Voigt K, Peters R, Bretschneider F, Pei X, Wilkens L, Moss F (1998b) Low dimensional dynamics in sensory biology I: thermally sensitive electroreceptors of the catfish. J Comput Neurosci 4:335–347

    Article  Google Scholar 

  • Braun HA, Huber MT, Anthes N, Voigt K, Neiman A, Pei X, Moss F (2001) Noise-induced impulse pattern modifications at different dynamical period-one situations in a computer model of temperature encoding. Biosystems 62:99–112

    Article  CAS  PubMed  Google Scholar 

  • Braun HA, Voigt K, Huber MT (2003) Oscillations, resonances and noise: basis of flexible neuronal pattern generation. Biosystems 71:39–50

    Article  PubMed  Google Scholar 

  • Berry MJ, Warland DK, Meister M (1997) The structure and precision of retinal spike trains. Proc Natl Acad Sci USA 94:5411–5416

    Article  CAS  PubMed  Google Scholar 

  • Eiesinga TPH, Fellous JM, Sejnowski TJ (2002) Attractor reliability reveals deterministic structure in neuronal spike trains. Neural Comput 14:1629–1650

    Article  Google Scholar 

  • Fox RF, Gatland IR, Roy R, Vemuri G (1998) Fast, accurate algorithm for numerical simulation of exponentially correlated colored noise. Phys Rev A.38:5938–5940

    Article  Google Scholar 

  • Gutfreund Y, Yarom Y, Segev I (1995) Subthreshold oscillations and resonant frequencies in guinea-pig cortical neurons, physiology and modelling. J Physiol 483:621–640

    CAS  PubMed  Google Scholar 

  • Horsthemke W, Lefever R (1984) Noise-induced transitions. Springer, Berlin

    Google Scholar 

  • Hodgkin AL, Huxley AF (1952) A qualitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117: 500–544

    CAS  PubMed  Google Scholar 

  • Hunter JD, Milton G (2003) Amplitude and frequency dependence of spike timing: implications for dynamic regulation. J Comput Neurosci 90:387–394

    Google Scholar 

  • Kreuz T, Haas JS, Morelli A, Abarbanel HDI, Politi A (2007) Measuring spike train synchrony. J Neurosci Meth 165:151–161

    Article  Google Scholar 

  • Lindner B, Garcia a Ojalvo J, Neiman A, Schimansky-Geier L (2004) Effects of noise in excitable systems. Phys Rep 392:321–424

    Article  Google Scholar 

  • Mainen Z, Sejnowski TJ (1995) Reliability of spike timing in neocortical neurons. Science 268:1503–1506

    Article  CAS  PubMed  Google Scholar 

  • Pare D, Pape HC, Dong J (1995) Bursting and oscillating neurons of the cat basolateral amygdaloid complex in vivo, electrophysiological properties and morphological features. J Neurophysiol 74:1179–1191

    CAS  PubMed  Google Scholar 

  • Quian Quiroga R, Kreuz T, Grassberger P (2002) Event synchronization: a simple and fast method to measure synchronicity and time delay patterns. Phys Rev E 66:041904

    Article  CAS  Google Scholar 

  • Schreiber S, Fellous JM, Whitmer JH, Tiesinga PHE, Sejnowski TJ (2003) A new correlation-based measure of spike timing reliability. Neurocomputing 52:925–931

    Article  PubMed  Google Scholar 

  • Victor JD, Purpura K (1996) Nature and precision of temporal coding in visual cortex: a metric-space analysis. J Neurophysiol 76:1310–1326

    CAS  PubMed  Google Scholar 

  • Van Rossum MCW (2001) A novel spike distance. Neural Comput 13:751–763

    Article  PubMed  Google Scholar 

  • Wolf Singer (2009) Distributed processing and temporal codes in neuronal networks. Cogn Neurodyn 3:189–196

    Article  PubMed  Google Scholar 

  • Walter JF (2009) Deep analysis of perception through dynamic structures that emerge in cortical activity from self-regulated noise. Cogn Neurodyn 3:105–116

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No.10872014,10872068,10672057) and the Special Foundation of ECUST for Young Teacher. We wish to thank Kreuz et al. for sharing their method and code in the internet.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ying Du.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Du, Y., Lu, Q. & Wang, R. Using interspike intervals to quantify noise effects on spike trains in temperature encoding neurons. Cogn Neurodyn 4, 199–206 (2010). https://doi.org/10.1007/s11571-010-9112-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11571-010-9112-2

Keywords

Navigation