The Journal of Neurophysiology Vol. 87 No. 5 May 2002, pp. 2612-2623
Copyright ©2002 by the American Physiological Society

Role for G Protein G Isoform Specificity in Synaptic Signal Processing: A Computational Study

 ¹Department of Mathematics and Kasha Laboratory of Biophysics, Florida State University, Tallahassee, Florida 32306; and  ²Department of Physiology and Biophysics, University of Calgary, Calgary, Alberta T2N 4N1, Canada

Bertram, Richard, Michelle I. Arnot, and Gerald W. Zamponi. Role for G Protein G Isoform Specificity in Synaptic Signal Processing: A Computational Study. J. Neurophysiol. 87: 2612-2623, 2002. 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 G 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 G 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 G 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 G isoforms. This allows for great synapse-to-synapse variability in the distinction between signal and noise.