{"id":170037,"global_id":"www.bordeaux-neurocampus.fr\/en\/?id=170037","global_id_lineage":["www.bordeaux-neurocampus.fr\/en\/?id=170037"],"author":"108","status":"publish","date":"2024-03-14 12:14:49","date_utc":"2024-03-14 11:14:49","modified":"2024-04-10 18:05:04","modified_utc":"2024-04-10 16:05:04","url":"https:\/\/www.bordeaux-neurocampus.fr\/en\/event\/seminar-alexander-mathis\/","rest_url":"https:\/\/www.bordeaux-neurocampus.fr\/en\/wp-json\/tribe\/events\/v1\/events\/170037","title":"Seminar – Alexander Mathis","description":"

\"\"Venue: Centre Broca<\/strong><\/p>\n

\n

Alexander Mathis
\nEPFL, Lausanne
\n<\/span><\/span>https:\/\/scholar.google.com\/citations?hl=en&user=Y1xCzE0AAAAJ<\/a><\/span><\/span><\/p>\n

Invited by Anna Beyeler (Magendie)<\/p>\n

Title<\/h3>\n

Modeling sensorimotor circuits with machine learning: hypotheses, inductive biases, latent noise and curricula <\/strong><\/p>\n

Abstract<\/h3>\n

Hierarchical 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.<\/p>\n

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