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SUMMARY:Neural circuits for goal-directed adaptive motor control
DTSTART:20190327T100000
DTEND:20190327T110000
DTSTAMP:20261005T052837Z
UID:4dbfb38afa1d19a7a18790349627b4105a3d0e80e9816f3c42021a1a
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr. Mackenzie Mathis\, Harvard University\, USA.\nOur motor ou
 tputs are constantly re-calibrated to adapt to systematic perturbations 
 – we can learn to use new tools and often improve upon already learned m
 otor skills. In this talk\, I will first discuss my development of the fir
 st mouse model of forelimb motor adaptation and experiments to probe the r
 ole of sensory and reward prediction errors in driving the adaptive behavi
 or that was observed. Then\, by systematically varying the task parameters
 \, I will show that reward feedback defines the global incentive for a par
 ticular motor output (i.e. the goal)\, but does not provide trial-by-trial
  feedback that alters performance. To causally test which proprioceptive f
 eedback pathways were required to adapt\, I perturbed regions that receive
  feedback\, namely cerebellar and cortical circuits. It was found that a c
 losed-loop optogenetic photoinhibition of somatosensory cortex (S1) applie
 d concurrently with the force field abolished adaptation (yet did not impa
 ir basic motor patterns or reward-based learning)\, which suggests that S1
  is required to learn to adapt to forelimb perturbations. Next\, to explor
 e the neural circuits required for adaptation\, we first built a deep lear
 ning toolbox for pose estimation (DeepLabCut) that allows us to perform hi
 gh fidelity tracking of the mouse\, and we now use this suite of behaviora
 l and computational tools to study neural population dynamics across multi
 ple regions of the brain during adaptation.\n\nBio\nDr. Mackenzie Mathis i
 s a Rowland Fellow at Harvard University\, where she runs a laboratory sin
 ce Sept 2017 (www.mackenziemathislab.org). The lab studies adaptive motor 
 behavior in mice\, performs large-scale recordings of neural populations\,
  builds new robotic tools for neural circuit interrogation & behavioral st
 udies\, as well as machine learning tools for behavioral analysis. Previou
 sly\, she was a Postdoctoral Fellow with Prof. Matthias Bethge (University
  of Tübingen)\, and completed her PhD studies in March 2017 at Harvard Un
 iversity under the direction of Prof. Naoshige Uchida. Her thesis work was
  focused on uncovering the neural circuits and mechanisms underlying senso
 rimotor learning.\n\nVideo transmission using zoom : https://epfl.zoom.us/
 j/9946495775
LOCATION:SV 1717 https://plan.epfl.ch/?room==SV%201717
STATUS:CONFIRMED
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