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Mackenzie Weygandt Mathis, PhD

@TrackingActions • 22,451 subscribers

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

Shorts

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! 🧵👇

98,489 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

29,791 次观看

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🦓 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

248,432 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

81,615 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

67,207 次观看 • 3 年前

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