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Mackenzie Weygandt Mathis, PhD

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merging adaptive motor control & machine learning | Prof @EPFL | 🐭@deeplabcut 🦓@cebraAI | @ELLISforEurope Scholar | @Harvard Alum | https://t.co/FakcYoDAC3

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How does sensorimotor (S1/M1) cortex support adaptive motor control? Come find out in our latest preprint, which spans the development of a full adult forelimb model + physics simulations, neural-modeling for control, complex 🐭behavior 🕹️, large-scale imaging, and of course DeepLabCut 🦄 and Bernhard Rackl! We hypothesized that S1 supports motor learning by computing prediction errors. To tackle this, we needed to understand what is being represented, and no studies have reported what forelimb S1 represents during learning in mice🧠🐭. Moreover, this requires modeling the body🦾: kinematics, torques, force, muscle activations, & proprioception (muscle spindles & GTOs). After our 7 year journey, we have an answer: S1 & M1 represent muscle-level features. During learning, computational motifs map to functional types (like muscle-encoding), and neural dynamics in S1 change & encode sensorimotor prediction errors! 🧵👇

How does sensorimotor (S1/M1) cortex support adaptive motor control? Come find out in our latest preprint, which spans the development of a full adult forelimb model + physics simulations, neural-modeling for control, complex 🐭behavior 🕹️, large-scale imaging, and of course DeepLabCut 🦄 and Bernhard Rackl! We hypothesized that S1 supports motor learning by computing prediction errors. To tackle this, we needed to understand what is being represented, and no studies have reported what forelimb S1 represents during learning in mice🧠🐭. Moreover, this requires modeling the body🦾: kinematics, torques, force, muscle activations, & proprioception (muscle spindles & GTOs). After our 7 year journey, we have an answer: S1 & M1 represent muscle-level features. During learning, computational motifs map to functional types (like muscle-encoding), and neural dynamics in S1 change & encode sensorimotor prediction errors! 🧵👇

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🥳 Kai Sandbrink & Pranav Mamidanna et al introduce task-driven modelling of the proprioceptive system, now out in eLife - the journal! Our work combines deep learning & biomechanics to test theories of💪sensory representations

🥳 Kai Sandbrink & Pranav Mamidanna et al introduce task-driven modelling of the proprioceptive system, now out in eLife - the journal! Our work combines deep learning & biomechanics to test theories of💪sensory representations

29,791 Aufrufe

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