UC San Diego

Chemical synapses exhibit a diverse array of internal mechanisms that affect the dynamics of transmission efficacy. Many of these processes, such as release of neurotransmitter and vesicle recycling, depend strongly on activity-dependent influx and accumulation of Ca2+. To model how each of these processes may affect the processing of information in neural circuits, and how their dysfunction may lead to disease states, requires a computationally efficient modelling framework, capable of generating accurate

phenomenology without incurring a heavy computational cost per synapse. In this dissertation, I derive physically grounded mathematical models of the instantaneous rate of Ca2+-triggered neurotransmitter release. The Ca2+ traces that drive these dynamics come from simulations in MCell of spike-evoked Ca2+ influx and buffered diffusion through the presynaptic space, an approach that overcomes observational limitations of physiological experiments. With these Ca2+ traces, I drive a validated kinetic model of the SNARE complex, which mediates spike-triggered vesicle fusion for both synchronous and asynchronous release. The profiles of the resulting release rate histograms inform the parameters of the phenomenological release models, including the time scales and the facilitation of release probability. Based on these results, I construct an event-driven model of presynaptic dynamics, treating all Ca2+-sensitive processes, not just vesicle release, as Poisson processes with decaying rate parameters that may undergo activity-dependent facilitation. This approach provides a unified framework for modelling both spontaneous and spike-evoked presynaptic vesicle dynamics, for an arbitrary number of processes that define interaction between an arbitrary number of vesicle pools and recycling pathways. I validate the model against MCell and demonstrate a runtime complexity that bridges the gap between full molecular simulations and abstract synaptic models. Furthermore, I verify that Ca2+-dependent recycling mechanisms are essential for maintaining transmission fidelity during high-frequency stimuli. Finally, the versatility of the framework enables one both to model diverse types of synapses and to perform test and control modelling experiments by including different sets of features and controlling their rates and responsiveness. I present this model as a highly extensible tool for future investigations into the functional impact of different synaptic mechanisms on information processing and dysfunction in model networks.