Bayesian Learning of Quantal Parameters at Single-Synapse Resolution

Quantitative understanding of synaptic transmission is quite limited. This is due in part to the fact that communication between a given pair of neurons mostly involves several synapses. Current models of synaptic transmission assume that all synapses between two given neurons are identical, because these models are based on average data.

To overcome the current frontier and resolve the single synapse, electrophysiological and optical recordings will be simultaneously carried out on the neuromuscular junction of the genetic model organism Drosophila melanogaster, where each presynaptic motor neuron connects via tens to hundreds of synapses onto a postsynaptic cell.

Theory on neurotransmission considering heterogeneity will form a general framework for the rigorous quantification of synaptic transmission, and can be used to evaluate changes in transmission under synaptic plasticity and after molecular perturbation. It may inform us about the roles of synaptic function in neural physiology and pathology.

Keywords: Potentiation, homeostasis, fly genetics



 Emina Ibrahimovic

Emina Ibrahimovic
University of Zürich
Institutes of Molecular Life Science & Neuroinformatics
Winterthurerstrasse 190
CH - 8057 Zurich