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SUMMARY:IC Colloquium : Some of the upcoming challenges in computational a
 nd mathematical neuroscience
DTSTART:20121119T163000
DTEND:20121119T174500
DTSTAMP:20260427T201937Z
UID:c48c66834ab6bcdf78cfe610fe43a9110d52487df3f168cb80c0a404
CATEGORIES:Conferences - Seminars
DESCRIPTION:Olivier Faugeras\, INRIA\nAbstract\nThe CNS\, like all complex
  systems\, features a large variety of spatial and temporal scales. A give
 n scale is usually accessible through a class of measurement modalities\, 
 e.g. electro-encephalography gives us access to the "mean" activity of ver
 y large populations of neurons whereas a micro-electrode can record from a
  single neuron. It is therefore important to be able to both develop theor
 ies that account for phenomena at a given scale\, for example at the singl
 e neuron level the Hodgkin-Huxley equations can reproduce many of the obse
 rved behaviours\, and are able to seamlessly traverse the scales from the 
 finest to the coarsest\, e.g. to develop a (mesoscopic) theory of say\, a 
 cortical column\, from the (microscopic) description of its individual neu
 rons and of their connections.\n\nIn the first part of this talk I describ
 e some current attempts in my research group to rigourously bridge the gap
  between theories of individual neurons behaviours and those of large popu
 lations of interconnected such neurons. This raises several important issu
 es such as the role of the uncertainty\, the noise\, in these theories\, a
 nd the optimal encoding of information. I also mention the difficulties th
 at one encounters when developing such mean field theories\, hence the cha
 llenges\, as well as underline their differences with what may be called "
 naive" mean field theories.\n\nIn the second part of the talk I focus on a
 n existing theory for describing the behaviours of entire cortical areas s
 uch as those that make up the visual system of human and non-human primate
 s. The theory of neural fields can probably be deduced from that of indivi
 dual neurons by methods such as those sketched out in the first part of th
 e talk but I will rather concentrate on two of its important features\, in
  relation to visual perception\, because I think that they are both univer
 sal in the way neuronal populations operate and unfamiliar to many of thos
 e working in computer vision or engineering in general. The first point is
  related to the idea of the symmetries of a system\, the second to the ide
 a of the bifurcations of the solutions to the equations that describe this
  system. As in the first part I will also mention a number of problems wit
 h neural fields theories\, hence again the challenges.\n\nThe talk will be
  relatively light on the mathematics\, emphasizing more the concepts than 
 the technicalities.\n\nBiography\nOlivier FAUGERAS is a graduate from the 
 Ecole Polytechnique\, France (1971). He holds a PhD in Computer Science an
 d Electrical Engineering from the University of Utah (1976) and a Doctorat
 e of Science in Mathematics from Paris VI University (1981). He is current
 ly a Senior Scientist ("Directeur de Recherche" in French) at INRIA (Mathe
 matics\, Informatics)\, where he leads the NeuroMathComp project team\, jo
 int scientific venture between Inria (Mathematics\, Computer Science) and 
 the JAD Laboratory (Mathematics) at Nice Sophia Antipolis University.\nHis
  research interests are in mathematical and computational neuroscience\, i
 .e. in applying mathematics and computers to model populations of neurons.
   Applications of his work include computer and biological visual percept
 ion\, neuronal diseases\, plasticity and learning\, models of functional i
 maging modalities (MR\, MEG\, EEG).\nHe has published extensively in archi
 val Journals\, International Conferences\, has contributed chapters to man
 y books and is the author of "Artificial 3-D Vision" published in 1993 by 
 MIT Press and\, with Quang-Tuan Luong and Théo Papadopoulo\, of "The Geom
 etry of Multiple Images" which appeared in March 2001\, also at MIT Press.
  He has co-edited with Nikos Paragios and Yunmei Chen "The Handbook of Mat
 hematical Models in Computer Vision" published in 2005 by Springer. He was
  an adjunct Professor from 1996 to 2001 in the Electrical Engineering and 
 Computer Science Department of the Massachusetts Institute of Technology a
 nd a member of the AI Lab. He has served as Associate Editor for IEEE PAMI
  from 1987 to 1990 and as co-Editor-in-Chief of the International Journal 
 of Computer Vision from 1991 to 2004. In 2011\, together with Stephen Coom
 bes from the University of Nottingham\, he started the Open Access Journal
  of Mathematical Neuroscience (JMN) published by Springer. He is a member 
 of the French Academy of Sciences and the French Academy of Technology. He
  was awarded by the European Research Council (ERC) an advanced grant enti
 tled "From single neurons to visual perception" dealing with the mathemati
 cal foundations of neuroscience.
LOCATION:BC 420 https://plan.epfl.ch/?room==BC%20420
STATUS:CONFIRMED
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