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SUMMARY:Exponential inequalities and local independence graphs to unravel 
 functional neuronal connectivity - Patricia Reynaud-Bouret\, Université d
 e Nice Sophia-Antipolis
DTSTART:20140624T103000Z
DTEND:20140624T110000Z
UID:TALK53104@talks.cam.ac.uk
CONTACT:37296
DESCRIPTION:The starting theoretical point is to provide "data-driven" exp
 onential inequalities for\ncounting processes with as few assumptions as p
 ossible on the underlying\nconditional intensity. This allows us to presen
 t a weighted Lasso method in the\nabstract set-up of linear counting proce
 sses. Thanks to this method\, it is possible to\napproximate real neuronal
  data\, called spike trains\, by Hawkes models even if this\nmodel is not 
 true. This leads to an estimation of local independence graphs for which\n
 we are currently testing the adequation with the biological notion of "fun
 ctional\nconnectivity".
LOCATION:Centre for Mathematical Sciences\, Meeting Room 2
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