Bertram, Arnot, Zamponi, 2002
This CellML model represents the pre-synaptic cell. The model runs in both OpenCell and COR, but the membrane only depolarises to -5 mV as opposed to ~40 mV. The units have been checked and there are no inconsistencies (just equivalences).
ABSTRACT: Computational modeling is used to investigate the functional impact of G protein-mediated presynaptic autoinhibition on synaptic filtering properties. It is demonstrated that this form of autoinhibition, which is relieved by depolarization, acts as a high-pass filter. This contrasts with vesicle depletion, which acts as a low-pass filter. Model parameters are adjusted to reproduce kinetic slowing data from different Gbetagamma dimeric isoforms, which produce different degrees of slowing. With these sets of parameter values, we demonstrate that the range of frequencies filtered out by the autoinhibition varies greatly depending on the Gbetagamma isoform activated by the autoreceptors. It is shown that G protein autoinhibition can enhance the spatial contrast between a spatially distributed high-frequency signal and surrounding low-frequency noise, providing an alternate mechanism to lateral inhibition. It is also shown that autoinhibition can increase the fidelity of coincidence detection by increasing the signal-to-noise ratio in the postsynaptic cell. The filter cut, the input frequency below which signals are filtered, depends on several biophysical parameters in addition to those related to Gbetagamma binding and unbinding. By varying one such parameter, the rate at which transmitter unbinds from autoreceptors, we show that the filter cut can be adjusted up or down for several of the Gbetagamma isoforms. This allows for great synapse-to-synapse variability in the distinction between signal and noise.
The original paper reference is cited below:
Role for G Protein G-Beta-Gamma Isoform Specificity in Synaptic Signal Processing: A Computational Study, Richard Bertram, Michelle I. Arnot, and Gerald W. Zamponi, 2002,Journal of Neurophysiology , 87, 2612-2623. PubMed ID: 11976397
|Schematic diagram of the presynaptic model.|