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Biophys J, August 2000, p. 629-637, Vol. 79, No. 2
and
*Center for Neural Science and Courant Institute of Mathematical
Sciences, New York University, New York, New York USA, and
Laboratory of Neurophysiology, Biomedical Research
Institute, Kaunas University of Medicine, Kaunas, Lithuania
The spike trains that transmit information between
neurons are stochastic. We used the theory of random point processes
and simulation methods to investigate the influence of temporal
correlation of synaptic input current on firing statistics. The theory
accounts for two sources for temporal correlation: synchrony between
spikes in presynaptic input trains and the unitary synaptic current
time course. Simulations show that slow temporal correlation of
synaptic input leads to high variability in firing. In a leaky
integrate-and-fire neuron model with spike afterhyperpolarization the
theory accurately predicts the firing rate when the spike threshold is
higher than two standard deviations of the membrane potential
fluctuations. For lower thresholds the spike afterhyperpolarization
reduces the firing rate below the theory's predicted level when the
synaptic correlation decays rapidly. If the synaptic correlation decays slower than the spike afterhyperpolarization, spike bursts can occur
during single broad peaks of input fluctuations, increasing the firing
rate over the prediction. Spike bursts lead to a coefficient of
variation for the interspike intervals that can exceed one, suggesting
an explanation of high coefficient of variation for interspike
intervals observed in vivo.
Biophys J, August 2000, p. 629-637, Vol. 79, No. 2
© 2000 by the Biophysical Society 0006-3495/00/08/629/09 $2.00
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