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).
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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
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
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
Braun HA, Voigt K, Huber MT (2003) Oscillations, resonances and noise: basis of flexible neuronal pattern generation. Biosystems 71:39–50
Berry MJ, Warland DK, Meister M (1997) The structure and precision of retinal spike trains. Proc Natl Acad Sci USA 94:5411–5416
Eiesinga TPH, Fellous JM, Sejnowski TJ (2002) Attractor reliability reveals deterministic structure in neuronal spike trains. Neural Comput 14:1629–1650
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
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
Horsthemke W, Lefever R (1984) Noise-induced transitions. Springer, Berlin
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
Hunter JD, Milton G (2003) Amplitude and frequency dependence of spike timing: implications for dynamic regulation. J Comput Neurosci 90:387–394
Kreuz T, Haas JS, Morelli A, Abarbanel HDI, Politi A (2007) Measuring spike train synchrony. J Neurosci Meth 165:151–161
Lindner B, Garcia a Ojalvo J, Neiman A, Schimansky-Geier L (2004) Effects of noise in excitable systems. Phys Rep 392:321–424
Mainen Z, Sejnowski TJ (1995) Reliability of spike timing in neocortical neurons. Science 268:1503–1506
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
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
Schreiber S, Fellous JM, Whitmer JH, Tiesinga PHE, Sejnowski TJ (2003) A new correlation-based measure of spike timing reliability. Neurocomputing 52:925–931
Victor JD, Purpura K (1996) Nature and precision of temporal coding in visual cortex: a metric-space analysis. J Neurophysiol 76:1310–1326
Van Rossum MCW (2001) A novel spike distance. Neural Comput 13:751–763
Wolf Singer (2009) Distributed processing and temporal codes in neuronal networks. Cogn Neurodyn 3:189–196
Walter JF (2009) Deep analysis of perception through dynamic structures that emerge in cortical activity from self-regulated noise. Cogn Neurodyn 3:105–116
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.
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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
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DOI: https://doi.org/10.1007/s11571-010-9112-2