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SUMMARY:Modularity\, polyrhythms\, and what robotics and control may yet l
 earn from the brain - Professor Jean-Jacques Slotine (Nonlinear Systems La
 boratory\, MIT)
DTSTART:20090116T140000Z
DTEND:20090116T150000Z
UID:TALK13896@talks.cam.ac.uk
CONTACT:Dr Guy-Bart Stan
DESCRIPTION:Although neurons as computational elements are 7 orders of mag
 nitude slower than their artificial counterparts\, the primate brain gross
 ly outperforms robotic algorithms in all but the most structured tasks. Pa
 rallelism alone is a poor explanation\, and much recent functional modelli
 ng of the central nervous system focuses on its modular\, heavily feedback
 -based computational architecture\, the result of accumulation of subsyste
 ms throughout evolution. We discuss this architecture from a global functi
 onality point of view\, and show why evolution is likely to favor certain 
 types of aggregate stability. We then study synchronization as a model of 
 computations at different scales in the brain\, such as pattern matching\,
  restoration\, priming\, temporal binding of sensory data\, and mirror neu
 ron response. We derive a simple condition for a general dynamical system 
 to globally converge to a regime where diverse groups of fully synchronize
 d elements coexist\, and show accordingly how global "polyrhythmic" patter
 ns can be transiently selected and controlled by a very small number of in
 puts or connections. We also quantify how synchronization mechanisms can p
 rotect general nonlinear systems from noise. Applications to some classica
 l questions in robotics\, control\, and systems neuroscience are discussed
 .\n \nThe development makes extensive use of nonlinear contraction theory\
 , a comparatively recent analysis tool whose main features will be briefly
  reviewed.
LOCATION:Cambridge University Engineering Department\, Lecture Room 3B
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