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Clustering Predicted by an Electrophysiological Model of the Suprachiasmatic Nucleus

Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan, Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan

Daniel B. Forger

Department of Mathematics, University of Michigan, Ann Arbor, Michigan, Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, forger{at}umich.edu

Despite the wealth of experimental data on the electrophysiology of individual neurons in the suprachiasmatic nuclei (SCN), the neural code of the SCN remains largely unknown. To predict the electrical activity of the SCN, the authors simulated networks of 10,000 GABAergic SCN neurons using a detailed model of the ionic currents within SCN neurons. Their goal was to understand how neuronal firing, which occurs on a time scale faster than a second, can encode a set phase of the circadian (24-h) cycle. The authors studied the effects of key network properties including: 1) the synaptic density within the SCN, 2) the magnitude of postsynaptic currents, 3) the heterogeneity of circadian phase in the neuronal population, 4) the degree of synaptic noise, and 5) the balance between excitation and inhibition. Their main result was that under a wide variety of conditions, the SCN network spontaneously organized into (typically 3) groups of synchronously firing neurons. They showed that this type of clustering can lead to the silencing of neurons whose intracellular clocks are out of circadian phase with the rest of the population. Their results provide clues to how the SCN may generate a coherent electrical output signal at the tissue level to time rhythms throughout the body.

Key Words: circadian rhythms • neuronal firing • suprachiasmatic nucleus • mathematical modeling • clustering

Journal of Biological Rhythms, Vol. 24, No. 4, 322-333 (2009)
DOI: 10.1177/0748730409337601


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