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X-WR-CALNAME:Bordeaux Neurocampus
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X-WR-CALDESC:Évènements pour Bordeaux Neurocampus
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DTSTART;TZID=Europe/Paris:20240419T140000
DTEND;TZID=Europe/Paris:20240419T140000
DTSTAMP:20260409T075720
CREATED:20240314T111449Z
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UID:170022-1713535200-1713535200@www.bordeaux-neurocampus.fr
SUMMARY:Seminar - Alexander Mathis
DESCRIPTION:Venue: Centre Broca \n\nAlexander Mathis\nEPFL\, Lausanne\nhttps://scholar.google.com/citations?hl=en&user=Y1xCzE0AAAAJ \nInvited by Anna Beyeler (Magendie) \nTitle\nModeling sensorimotor circuits with machine learning: hypotheses\, inductive biases\, latent noise and curricula  \nAbstract\nHierarchical sensorimotor processing\, modularity and experience are all essential for adaptive motor control. Recent efficient musculoskeletal simulators and machine learning algorithms provide new computational approaches to gain insights into those concepts for biological motor control. Firstly\, I will present a hypothesis-driven modeling framework to quantitatively assess the computations underlying proprioception. We trained thousands of models to transform muscle spindle inputs according to 16 different hypotheses from the literature. For all those hypotheses\, we found that hierarchical models that better satisfy those hypotheses\, also explain neural recordings in the brain stem and cortex better. We furthermore find that models trained to estimate the state of the body are best at explaining neural data. Secondly\, I will discuss key methods (inductive biases\, latent exploration\, and curricula) to close the gap between reinforcement learning algorithms and biological motor control. Taken together\, these results highlight the importance of inductive biases\, and experience for biological motor control. \n  \n
URL:https://www.bordeaux-neurocampus.fr/event/seminar-alexander-mathis/
CATEGORIES:A la une,Pour les scientifiques,Séminaire Impromptu
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