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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! 🧵👇

98,555 次观看

🥳 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

29,792 次观看

Studying both natural & skilled behavior is critical to understand the brain. I’m very happy tools we develop- DeepLabCut 🦄- are used so broadly 💕& openning new research in neuro and ecology New nature news piece re: lab🐭: Wild:

Studying both natural & skilled behavior is critical to understand the brain. I’m very happy tools we develop- DeepLabCut 🦄- are used so broadly 💕& openning new research in neuro and ecology New nature news piece re: lab🐭: Wild:

13,377 次观看

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🧠 Can we recover the equations governing a system directly from noisy, high-dimensional observations? #DYSCO, by Paolo Muratore, is a first step: it learns latent spaces that capture the underlying dynamics & enables recovery of governing equations 📄

🧠 Can we recover the equations governing a system directly from noisy, high-dimensional observations? #DYSCO, by Paolo Muratore, is a first step: it learns latent spaces that capture the underlying dynamics & enables recovery of governing equations 📄

Mackenzie Weygandt Mathis, PhD

95,168 次观看 • 24 天前

Mice perform active sensing to acquire visual info: check out our collaborative effort, which is our 1st in motor+vision! Great to work with Andreas Tolias Lab @ Stanford University Xaq and Cris - 💰by the Brain Initiative NIH! Led by Célia Benquet & tom sainsbury Current Biology

Mice perform active sensing to acquire visual info: check out our collaborative effort, which is our 1st in motor+vision! Great to work with Andreas Tolias Lab @ Stanford University Xaq and Cris - 💰by the Brain Initiative NIH! Led by Célia Benquet & tom sainsbury Current Biology

Mackenzie Weygandt Mathis, PhD

15,309 次观看 • 18 天前

🦓 Self-supervised multimodal ML is promising the next AI breakthrough - in our new work published in nature, we debut @CEBRAai: for self-supervised hypothesis- and discovery-driven science. 📝 💻 🦓 🧵⬇️

🦓 Self-supervised multimodal ML is promising the next AI breakthrough - in our new work published in nature, we debut @CEBRAai: for self-supervised hypothesis- and discovery-driven science. 📝 💻 🦓 🧵⬇️

Mackenzie Weygandt Mathis, PhD

249,834 次观看 • 3 年前

🐁🐘🐿🔥SuperAnimals for #Pose -No human labeling - Video analysis on over 45 species with only 2 global classes of animal pose models -If SA needs fine-tuning, its 10× more data efficient & 2X outperforms prior transfer learning 📝Shaokai Ye et al 🧵⬇️

🐁🐘🐿🔥SuperAnimals for #Pose -No human labeling - Video analysis on over 45 species with only 2 global classes of animal pose models -If SA needs fine-tuning, its 10× more data efficient & 2X outperforms prior transfer learning 📝Shaokai Ye et al 🧵⬇️

Mackenzie Weygandt Mathis, PhD

81,642 次观看 • 3 年前

🚨Crowded scenes are notoriously difficult for #ComputerVision models. In our new work lead by Zhou Mu & Lucas Stoffl we developed a novel approach called BUCTD that is state-of-the-art on crowded 🐒🐠&🕺human pose estimation benchmarks!🔥 #CVPR2023 workshop

🚨Crowded scenes are notoriously difficult for #ComputerVision models. In our new work lead by Zhou Mu & Lucas Stoffl we developed a novel approach called BUCTD that is state-of-the-art on crowded 🐒🐠&🕺human pose estimation benchmarks!🔥 #CVPR2023 workshop

Mackenzie Weygandt Mathis, PhD

67,217 次观看 • 3 年前

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