Modeling local synaptic control in neural coincidence detectors

Sensory neurons often process information over a large range of a given stimulus parameter. For example, the auditory system can function over a range of sound intensities corresponding to a 1013 fold increase in sound pressure from detection threshold. Indeed, special mechanisms at several levels of the auditory system are in place to maintain low detection thresholds but prevent saturation (overload) of the system. We are exploring one such mechanism at a synaptic site that requires very precisely timed synaptic input to achieve its computation function. Specifically, we propose that a particular receptor system (the GABAB receptor) locally controls the strength of excitatory and inhibitory inputs to this neuron type. Further, we expect that local control of synaptic strength maintains the circuit in an optimal operating range. We will use mathematical modeling techniques to independently manipulate each target of the GABAB R activation and inject our modeled synaptic currents back into the cells. Our approach will combine computational modeling with empirical hypothesis testing to determine the impact of these receptors on circuit function.