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The Journal of Neurophysiology Vol. 86 No. 6 December 2001, pp. 2736-2747
Copyright ©2001 by the American Physiological Society
Departments of Pediatrics and Neurology, University of Colorado Health Sciences Center, Denver, Colorado 80262
Staley, Kevin J.,
Jaideep S. Bains,
Audrey Yee,
Jennifer Hellier, and
J. Mark Longacher.
Statistical Model Relating CA3 Burst Probability to Recovery From
Burst-Induced Depression at Recurrent Collateral Synapses. J. Neurophysiol. 86: 2736-2747, 2001. When
neuronal excitability is increased in area CA3 of the hippocampus in
vitro, the pyramidal cells generate periodic bursts of action
potentials that are synchronized across the network. We have previously
provided evidence that synaptic depression at the excitatory recurrent
collateral synapses in the CA3 network terminates each population burst
so that the next burst cannot begin until these synapses have
recovered. These findings raise the possibility that burst timing can
be described in terms of the probability of recovery of this population
of synapses. Here we demonstrate that when neuronal excitability is
changed in the CA3 network, the mean and variance of the interburst
interval change in a manner that is consistent with a timing mechanism comprised of a pool of exponentially relaxing pacemakers. The relaxation time constant of these pacemakers is the same as the time
constant describing the recovery from activity-dependent depression of
recurrent collateral synapses. Recovery was estimated from the rate of
spontaneous transmitter release versus time elapsed since the last CA3
burst. Pharmacological and long-term alterations of synaptic strength
and network excitability affected CA3 burst timing as predicted by the
cumulative binomial distribution if the burst pace-maker consists of a
pool of recovering recurrent synapses. These findings indicate that the
recovery of a pool of synapses from burst-induced depression is a
sufficient explanation for burst timing in the in vitro CA3 neuronal
network. These findings also demonstrate how information regarding the
nature of a pacemaker can be derived from the temporal pattern of
synchronous network activity. This information could also be extracted
from less accessible networks such as those generating interictal
epileptiform discharges in vivo.
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