Modèles d'apprentissage automatique pour les neurosciences

Dans cours

PHYS-754: Lecture series on scientific machine learning

This lecture presents ongoing work on how scientific questions can be tackled using machine learning. Machine learning enables extracting knowledge from data computationally and in an automatized way. We will learn on examples how this is influencing the v ...

Description

Cette séance de cours explore les modèles d’apprentissage automatique pour les neurosciences, en explorant comment ces modèles comblent les limites de la compréhension des fonctions cérébrales en reliant le couplage perception-action au niveau du circuit. L'instructeur explique la modélisation axée sur les tâches, montrant comment les modèles sont créés pour comprendre pourquoi les neurones se déclenchent en réponse à des stimuli. La séance de cours couvre également la reconnaissance des objets de base, en se concentrant sur les réseaux neuronaux convolutifs formés sur ImageNet, qui sont les meilleurs modèles pour la voie ventrale chez les humains et les singes. L'instructeur montre comment ces modèles peuvent expliquer l'activité neuronale dans le cerveau des primates, en particulier dans le domaine informatique. En outre, la séance de cours traite de l'application de l'apprentissage automatique dans la compréhension de la proprioception, en montrant comment les modèles candidats sont créés et testés pour expliquer les neurones sélectifs en direction dans le cerveau.

Cette vidéo est disponible exclusivement sur Mediaspace pour un public restreint. Veuillez vous connecter à Mediaspace pour y accéder si vous disposez des autorisations nécessaires.

Regarder sur Mediaspace

Enseignant·e·s (10)

Michele Ceriotti

Michele Ceriotti received his Ph.D. in Physics from ETH Zürich in 2010. He spent three years in Oxford as a Junior Research Fellow at Merton College. Since 2013 he leads the laboratory for Computational Science and Modeling in the Institute of Materials at EPFL. His research revolves around the atomic-scale modelling of materials, based on the sampling of quantum and thermal fluctuations and on the use of machine learning to predict and rationalize structure-property relations. He has been awarded the IBM Research Forschungspreis in 2010, the Volker Heine Young Investigator Award in 2013, an ERC Starting Grant in 2016, and the IUPAP C10 Young Scientist Prize in 2018.

Anne-Florence Raphaëlle Bitbol

Lenka Zdeborová

Lenka Zdeborová is a Professor of Physics and of Computer Science in École Polytechnique Fédérale de Lausanne where she leads the Statistical Physics of Computation Laboratory. She received a PhD in physics from University Paris-Sud and from Charles University in Prague in 2008. She spent two years in the Los Alamos National Laboratory as the Director's Postdoctoral Fellow. Between 2010 and 2020 she was a researcher at CNRS working in the Institute of Theoretical Physics in CEA Saclay, France. In 2014, she was awarded the CNRS bronze medal, in 2016 Philippe Meyer prize in theoretical physics and an ERC Starting Grant, in 2018 the Irène Joliot-Curie prize, in 2021 the Gibbs lectureship of AMS. She is an editorial board member for Journal of Physics A, Physical Review E, Physical Review X, SIMODS, Machine Learning: Science and Technology, and Information and Inference. Lenka's expertise is in applications of concepts from statistical physics, such as advanced mean field methods, replica method and related message-passing algorithms, to problems in machine learning, signal processing, inference and optimization. She enjoys erasing the boundaries between theoretical physics, mathematics and computer science.

Anne-Clémence Corminboeuf

David Richard Harvey

Alexander Mathis

Alexander studied pure mathematics with a minor in logic and theory of science at the Ludwig Maximilians University in Munich. For his PhD also at LMU, he worked on optimal coding approaches to elucidate the properties of grid cells. As a postdoctoral fellow with Prof. Venkatesh N. Murthy at Harvard University and Prof. Matthias Bethge at Tuebingen AI, he decided to study olfactory behaviors such as odor-guided navigation, social behaviors and the cocktail party problem in mice. During this time, he increasingly got interested sensorimotor behaviors beyond olfaction and started working on proprioception, motor adaption, as well as computer vision tools for measuring animal behavior. In his group, he is interested in elucidating how the brain gives rise to adaptive behavior. One of the major goals is to synthesize large datasets into computationally useful information. For those purposes, he develops algorithms and systems to analyze animal behavior (e.g. DeepLabCut), neural data, as well as creates experimentally testable computational models.

Source officielle