Statistical Model Relating CA3 Burst Probability to Recovery From Burst-Induced Depression at Recurrent Collateral Synapses

Kevin J. Staley, Jaideep S. Bains, Audrey Yee, Jennifer Hellier, J. Mark Longacher
2001 Journal of Neurophysiology  
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
more » ... 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.
doi:10.1152/jn.2001.86.6.2736 pmid:11731533 fatcat:r3gzuehv3zceza5cc2ey45uj6q