Internal KCN: Implications of synaptic transmission on information transfer
Daniel Trotter. MSc Candidate (Naud Lab, uOttawa)
TITLE: Implications of synaptic transmission on information transfer
ABSTRACT: Information flow in the brain is controlled by several molecular mechanisms. Temporal sequences of action potentials are communicated across synapses with variant efficacy allowing the same axon to convey independent messages to multiple postsynaptic targets. Even at the level of single synapses, this is a highly variable process and changes dynamically on multiple time scales. Modeling this highly variable phenomenon requires balancing a model’s interpretability and its ability to espouse experimental data. Here a statistical approach is taken to develop a biophysically tractable gamma-mixture model for characterizing postsynaptic responses from single-spine release events. Having a synapse-level description, a system identification approach is subsequently taken to capture the evolving short-term dynamics. Extending previous phenomenological approaches allows for the characterization of a nonlinearity and the kinetics evolving on multiple time scales. Using gradient descent methods, estimates of synaptic kinetics are made from complex firing patterns comparable to those observed in vivo. Characterizing the dynamics of synaptic transmission improves our understanding of information transfer between cells, which are assumed to maximize the efficiency of this process. Extending the observations from single synapse releases, a numerical approach is taken to investigate the putative informatic redundancy of having two different postsynaptic glutamate receptors. Collectively these models of synaptic dynamics create an avenue for improved understanding of the information processing capabilities of synapses.