Sorbonne Université

Master de Sciences & Technologies

M2 Mathématiques & Applications (Sorbonne Université)

Modeling, simulations, data analysis in neuroscience and application to medical prediction using Model-Machine-Learning

D. Holcman

WHEN : Oct 2019-Jan 2020
Wednesday. 17h00-20h00. Starting date: October 9th, 2019
WHERE Salle 513, 5th floor, ENS, 46 rue d’Ulm, 75005 Paris
Class common to PSL-ENS-Sorbonne University

General description: The class aims at describing modern modeling in neuroscience from the molecular, cellular to the Brain level with medical prediction from the Electroencephalogram. We will start by introducing stochastic models to analyze large amount of single particle trajectories obtained by superresolution microscopy. Models are used to reconstruct and interpret high density domains found at pre and post synaptic terminal. We will discuss vesicular release and calcium nanodomains. The class continues with Neural network bursting modeling, model of recurrent bursting in electrophysiology traces and Up-Down state. Finally, we will introduce Model-Machine-learning method to predict Brain behavior from EEG time series.

The goal of the class is to present modeling approach, stochastics analysis, signal processing and analysis of the model equations. The class is ideal for engineers, mathematicians, physicists, theoretical chemist or computer scientists.

This class (in english) will use the Holcman's Cambridge lecture and e-class presented in http://bionewmetrics.org/stochastic-processes-and-applications-to-modeling-cellular-microdomains/#more-146 and Youtube

contact: david.holcman@ens.fr

Syllabus

Part I

Part II and III:

Evaluation : small projects.

References:

Basics :

Advanced :