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UID:1119@biology.technion.ac.il

DTSTART;TZID=Asia/Jerusalem:20220906T130000

DTEND;TZID=Asia/Jerusalem:20220906T130000

DTSTAMP:20220906T063724Z

URL:https://biology.technion.ac.il/en/seminars/special-faculty-seminar-dr-
 stefano-recanatesi/

SUMMARY:Special Faculty Seminar: Dr. Stefano Recanatesi [No Categories]
DESCRIPTION:Location: Biology auditorium  Dr. Stefano Recanatesi \n Affilia
 tion: The Center for Computational Neuroscience and Swartz Center for Theo
 retical Neuroscience at the University of Washington\, Seattle.\n Host:Pro
 f. Benjamin Podbilewicz\n Title: Linking learning to behaviour via neural 
 representation geometry.\n\nAbstract: Recent developments in experimental 
 neuroscience enable us to simultaneously record the activity of a large nu
 mber of neurons and to target specific components of neural circuits with 
 genetic techniques. These methods pave the way to a deeper understanding o
 f how the brain's collective dynamics instantiate learning and behaviour. 
 Obtaining such a conceptual understanding from large\, high-dimensional ne
 ural datasets\, however\, requires concurrent advances in theoretically dr
 iven circuit modelling. By examining network mechanisms that underlie the 
 emergence of complex behaviour\, I will show that neural representation ge
 ometry may be the key approach to tying animal behaviour and learning to c
 ircuit mechanisms. I will demonstrate how neural activity from cortical ar
 eas unfolds through temporal sequences of neural states organized in behav
 ioural plans or episodes. Then\, I will illustrate how these neural repres
 entations in premotor areas drive the generation of actions\, allowing us 
 to predict the intention to act. Finally\, I will investigate how key geom
 etrical aspects of these representations emerge\, de novo\, as a result of
  learning to predict upcoming sensory experience. Altogether\, I will show
  how both learning mechanisms and behavioural demands shape the geometry o
 f neural representations. I will build these results by using theoretical 
 and computational techniques that combine dynamical systems\, machine lear
 ning\, and statistical physics approaches. 
LOCATION:Biology auditorium

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TZID:Asia/Jerusalem

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