Linear-nonlinear cascades capture synaptic dynamics

Julian Rossbroich, Daniel Trotter, John Beninger, Katalin Toth, Richard Naud

Abstract: Author summary Understanding how information is transmitted relies heavily on knowledge of the underlying regulatory synaptic dynamics. Existing computational models for capturing such dynamics are often either very complex or too restrictive. As a result, effectively capturing the different types of dynamics observed experimentally remains a challenging problem. Here, we propose a mathematically flexible linear-nonlinear model that is capable of efficiently characterizing synaptic dynamics. We demonstrate the ability of this model to capture different features of experimentally observed data.