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Individual neurons dissociated from rat suprachiasmatic nucleus express independently phased circadian firing rhythms.

Neuron

analysis, Action Potentials, Animals, Animals, Newborn, Cells, Cultured, Circadian Rhythm, Neurons, physiology, Rats, Rats, Sprague-Dawley, Suprachiasmatic Nucleus, cytology, Synapses, Synaptic Vesicles, Tetrodotoxin, pharmacology, Vasoactive Intestinal Peptide, Vasopressins, gamma-Aminobutyric Acid

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      Abstract

      Within the mammalian hypothalamus, the suprachiasmatic nucleus (SCN) contains a circadian clock for timing of diverse neuronal, endocrine, and behavioral rhythms. By culturing cells from neonatal rat SCN on fixed microelectrode arrays, we have been able to record spontaneous action potentials from individual SCN neurons for days or weeks, revealing prominent circadian rhythms in firing rate. Despite abundant functional synapses, circadian rhythms expressed by neurons in the same culture are not synchronized. After reversible blockade of neuronal firing lasting 2.5 days, circadian firing rhythms re-emerge with unaltered phases. These data suggest that the SCN contains a large population of autonomous, single-cell circadian oscillators, and that synapses formed in vitro are neither necessary for operation of these oscillators nor sufficient for synchronizing them.

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        The chi square periodogram: its utility for analysis of circadian rhythms.

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          Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains.

          The statistical analysis of two simultaneously observed trains of neuronal spikes is described, using as a conceptual framework the theory of stochastic point processes.The first statistical question that arises is whether the observed trains are independent; statistical techniques for testing independence are developed around the notion that, under the null hypothesis, the times of spike occurrence in one train represent random instants in time with respect to the other. If the null hypothesis is rejected-if dependence is attributed to the trains-the problem then becomes that of characterizing the nature and source of the observed dependencies. Statistical signs of various classes of dependencies, including direct interaction and shared input, are discussed and illustrated through computer simulations of interacting neurons. The effects of nonstationarities on the statistical measures for simultaneous spike trains are also discussed. For two-train comparisons of irregularly discharging nerve cells, moderate nonstationarities are shown to have little effect on the detection of interactions.Combining repetitive stimulation and simultaneous recording of spike trains from two (or more) neurons yields additional clues as to possible modes of interaction among the monitored neurons; the theory presented is illustrated by an application to experimentally obtained data from auditory neurons.A companion paper covers the analysis of single spike trains.
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            7718233

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