On the mechanisms underlying the depolarization block in the spiking dynamics of CA1 pyramidal neurons
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Under sustained input current of increasing strength neurons eventually stop firing, entering a depolarization block. This is a robust effect that is not usually explored in experiments or explicitly implemented or tested in models. However, the range of current strength needed for a depolarization block could be easily reached with a random background activity of only a few hundred excitatory synapses. Depolarization block may thus be an important property of neurons that should be better characterized in experiments and explicitly taken into account in models at all implementation scales. Here we analyze the spiking dynamics of CA1 pyramidal neuron models using the same set of ionic currents on both an accurate morphological reconstruction and on its reduction to a single-compartment. The results show the specific ion channel properties and kinetics that are needed to reproduce the experimental findings, and how their interplay can drastically modulate the neuronal dynamics and the input current range leading to a depolarization block. We suggest that this can be one of the rate-limiting mechanisms protecting a CA1 neuron from excessive spiking activity.
KeywordsDepolarization block CA1 pyramidal neuron Bifurcation analysis Kinetics
Financial support from “Compagnia di San Paolo” is gratefully acknowledged. We thank Drs. S. Cuomo and P. De Michele (Department of Mathematics and Applications “Renato Caccioppoli”, University of Naples Federico II) for assistance in running the parallel version of our morphological model and for the use of the S.Co.P.E. Grid infrastructure of University of Naples Federico II.
- Hines, M. L., & Carnevale, N. T. (2003). The NEURON simulation environment. In The handbook of brain theory and neural networks (2nd ed., pp. 769–773). Cambridge: MIT Press.Google Scholar
- Koch, C. (1999). Biophysics of computation: Information processing in single neurons. New York: Oxford University Press.Google Scholar
- Marasco, A., & Romano, A. (2001). Scientific computing with mathematica: Mathematical problems for ordinary differential equations. Boston: Birkhauser. ISBN 0-8176-4205-6.Google Scholar
- Rüdiger, S. (2010). Practical bifurcation and stability analysis practical bifurcation and stability analysis. In Springer series: Interdisciplinary applied mathematics (Vol. 5, 3rd ed.). New York: Springer.Google Scholar
- Scorza, C. A., Araujo, B. H., Leite, L. A., Torres, L. B., Otalora, L. F., Oliveira, M. S., et al. (2011). Morphological and electrophysiological properties of pyramidal-like neurons in the stratum oriens of Cornu ammonis 1 and Cornu ammonis 2 area of Proechimys. Neuroscience, 177, 252–268.PubMedCrossRefGoogle Scholar
- Troy, W. C. (1974). Oscillatory phenomena in nerve conduction equations. Ph.D. dissertation, SUNY at Buffalo.Google Scholar
- Troy, W. C. (1978). The bifurcation of periodic solutions in the Hodgkin–Huxley equations. Quarterly of Applied Mathematics, 36, 73–83.Google Scholar