"Multi-scale modeling in neuroscience" Claire Guerrier (Video)

The objective of this talk is to present several tools to address multi-scale modeling in neuroscience. Specifically, I am developing models to study neuronal structures with multiple time and space scales, by combining continuum dynamics with stochastic effects. The goal is to address questions such as how the properties of synapses at the molecular level shape synaptic transmission, or how an electrical signal propagates in the dendritic tree.
I will first present neuronal organization, as a pre-requisite, and use the model of a pre-synaptic terminal to present a problem of mean first passage time to a small hole, asymptotic analysis, Brownian dynamics, and Poissonian rate equations.
The second part of my talk will concern signal propagation in neurons: what are the challenges, the models, and the tools available. The questions that modeling can investigate are: How do neurons integrate information? What are the impacts of channel type and organization on signal propagation? What is the effect of an active synapse on its neighbors, or what are the effects of back-propagating action potentials on signal propagation.

Video 1   Video 2