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DTSTART:20191027T020000
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DTSTAMP:20260404T004606Z
UID:5e09f84ce103e123771842@ist.ac.at
DTSTART:20200309T100000
DTEND:20200309T110000
DESCRIPTION:Speaker: Shreya Saxena\nhosted by Gasper Tkacik\nAbstract: We h
 ave a remarkable ability to effortlessly perform complex and fast movement
 s. While these movements are fundamentally constrained by the biophysics o
 f the neurons and dynamics of the muscles involved\, how these factors act
  together to limit our ability to make agile movements in health and disea
 se has not been rigorously quantified. In the first part of the talk\, I w
 ill focus on performance limitations of sensorimotor control. Using a biop
 hysically based model of neuronal dynamics\, we predict undesirable phenom
 ena that occur when tracking high frequency inputs\, including skipped cyc
 les\, overshoot and undershoot. Notably\, these specific errors are well d
 ocumented for humans and monkeys. We derive an analytical bound on the hig
 hest frequency that we can track without producing such undesirable phenom
 ena\, as a function of the neural computation and muscle dynamics. Our the
 oretical analysis can be used to guide the design of therapies for movemen
 t disorders by enhancing performance with assistive neuroprosthetic device
 s.In the second part of the talk\, I will focus on methods for the data-dr
 iven inference of the relationships between high-dimensional cortex-wide n
 eural activity and the ensuing behavior. Widefield calcium imaging enables
  recording of large-scale neural activity across the mouse dorsal cortex. 
 Here\, it is critical to demix the recordings into meaningful spatial and 
 temporal components that can be mapped onto well-defined brain regions. To
  this end\, we developed Localized semi-Nonnegative Matrix Factorization (
 LocaNMF) to extract the activity of different brain regions in individual 
 mice in a data-driven manner. The decomposition obtained by LocaNMF result
 s in interpretable components which are robust across subjects and experim
 ental conditions. We also uncover the relationship of these neural signals
  to the resulting high-dimensional behavior. I will end by providing insig
 hts into how we can leverage theoretical analyses as well as data-driven m
 odels of large-scale cortical activity to restore impaired movements due t
 o compromised neural transmission.
LOCATION:Mondi Seminar Room 2\, Central Building\, ISTA
ORGANIZER:tguggenb@ist.ac.at
SUMMARY:Shreya Saxena: Modeling the neural control of movement: performance
  limitations and data-driven inference
URL:https://talks-calendar.ista.ac.at/events/2681
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