The influence of sodium and potassium dynamics on excitability, seizures, and the stability of persistent states: II. Network and glial dynamics
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In these companion papers, we study how the interrelated dynamics of sodium and potassium affect the excitability of neurons, the occurrence of seizures, and the stability of persistent states of activity. We seek to study these dynamics with respect to the following compartments: neurons, glia, and extracellular space. We are particularly interested in the slower time-scale dynamics that determine overall excitability, and set the stage for transient episodes of persistent oscillations, working memory, or seizures. In this second of two companion papers, we present an ionic current network model composed of populations of Hodgkin–Huxley type excitatory and inhibitory neurons embedded within extracellular space and glia, in order to investigate the role of micro-environmental ionic dynamics on the stability of persistent activity. We show that these networks reproduce seizure-like activity if glial cells fail to maintain the proper micro-environmental conditions surrounding neurons, and produce several experimentally testable predictions. Our work suggests that the stability of persistent states to perturbation is set by glial activity, and that how the response to such perturbations decays or grows may be a critical factor in a variety of disparate transient phenomena such as working memory, burst firing in neonatal brain or spinal cord, up states, seizures, and cortical oscillations.
KeywordsNeuronal networks Instability Glia buffering Seizures Persistent activity
We thank Jokubas Ziburkus, Andrew J Trevelyan, Maxim Bazhenov, and Partha Mitra, for their valuable discussions. This work was funded by NIH Grants K02MH01493 (SJS), R01MH50006 (SJS, GU), F32NS051072 (JRC), and CRCNS-R01MH079502 (EB).
- Cressman, J. R., Ullah, G., Ziburkus, J., Schiff, S. J., & Barreto, E. (2009). The influence of sodium and potassium dynamics on excitability, seizures, and the stability of persistent states: I. Single neuron dynamics. Journal of Computational Neuroscience. doi: 10.1007/s10827-008-0132-4.
- Forster, D. (1990). Hydrodynamic fluctuations, broken symmetry, and correlation. Boulder: Westview.Google Scholar
- Frohlich, F., Timofeev, I., Sejnowski, T. J., & Bazhenov, M. (2008). Extracellular potassium dynamics and epileptogenesis. In I. Soltesz, & K. Staley (Eds.), Computational neuroscience in epilepsy. Amsterdam: Academic.Google Scholar
- Fuster, J. M. (1995). Memory in the cerebral cortex. Cambridge: MIT.Google Scholar
- Murray, J. D. (2003). Mathematical Biology II: Spatial models and biomedical applications. New York: Springer.Google Scholar
- Nadkarni, S., & Jung, P. (2003). Spontaneous oscillations of dressed neurons: A new mechanism for epilepsy? Physical Review Letters, 91(268101), 1–4.Google Scholar
- Soltesz, I., & Staley, K. (2008). Computational neuroscience in epilepsy. Amsterdam: Academic.Google Scholar
- Somjen, G. G. (2004). Ions in the brain: normal function, seizures, and stoke. Oxford: Oxford University Press.Google Scholar