Action potential-triggered synaptic transmitter release forms a hallmark of interneuronal communication. Neurotransmitter release is critically impacted by diverse noise mechanisms, such as random arrival of action potentials, probabilistic vesicular release, and random replenishment of vesicular pools. How these noise mechanisms combine to impact fidelity of interneuronal communication is an intriguing fundamental problem. A key focus of this project is to use the mathematical formalism of Stochastic Hybrid Systems (SHS) that combine continuous dynamics with discrete random events for modeling synaptic transmission. Integration of mathematical models with electrophysiological data from brain slices will provide a platform for testing model predictions, generating new hypotheses, and elucidating transmission at various auditory and hippocampal synapses.