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   1 %!TEX TS-program = pdflatex
   2 %!TEX encoding = Latin1
   3 %!TEX root = perrinet09int.tex
   4 \documentclass[unicode]{beamer}%
   5 \usepackage{movie15}%
   6 \graphicspath{{./figures/},{../Learning/mp_sparsenet/mp_sparsenet/results/20080502T174325/},{../../Motion/08-06-02_seminaire_ulm/figures/},{../../Motion/09-02-28_cosyne/figures/},{../../Motion/SpatialSummation/07-10_figures_these_barth/},{../../Motion/SpatialSummation/07-08-22_mss_j_physiol/figures/},{../../Motion/SpatialSummation/06-10_NeuroComp_talk/figures/},{../../Motion/09-02-28_cosyne/figures/},{../../Motion/particles/figures/}}% 
   7 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
   8 \title{Decoding low-level neural information \\to track visual motion}%
   9 \author{Laurent Perrinet, %
  10 INCM/CNRS,  Marseille, France
  11  }%
  12 %\logo{\url{http://incm.cnrs-mrs.fr/LaurentPerrinet}}
  13 \date{``Brain dynamics, from information to behavior" workshop, April 6th and 7th, 2009}%
  14 %Moving the eyes rapidly to track a visual object moving in a cluttered environment is an essential 
  15 %function. However, doing so rapidly and efficiently is constrained by a number of noise sources in the 
  16 %visual system and by the fact that information is collected locally before giving raise to a global signal. 
  17 %After reviewing some results made in the modeling of low-level sensory areas, I will expose a method 
  18 %to decode low-level neural information as describing visual information using a probabilistic 
  19 %representation. Decisions will therefore correspond to statistical inferences which are dynamically 
  20 %resolving the veridical speed of a moving object. We will illustrate this method by showing how 
  21 %ambiguous local information can be merged to give raise to a global response which resolves the 
  22 %aperture problem. Using this theoretical approach "in computo", we will illustrate how we may better 
  23 %understand results which are observed "in vivo" (optical imaging) as a neural code linking actively 
  24 %sensation and behavior. 
  25 %
  26 %To create handouts with 4 slides on one page, run
  27 % pdfnup --nup 2x2 --orient landscape --frame true --delta \""3mm  3mm\""   --column true --columnstrict true --noautoscale false --scale 0.95 perrinet09int.pdf
  28 %\setbeameroption{show notes}%
  29 %\usefonttheme{structuresmallcapsserif}%
  30 \begin{document}%
  31 \begin{center}
  32 %----------------------------------------------------------------------------------------------------%
  33 \input{Context}%
  34 %----------------------------------------------------------------------------------------------------%
  35 %  $\xb = \sum\nolimits_j s_j . \Ab_j$%
  36 %----------------------------------------------------------------------------------------------------%
  37 \input{Motion}%
  38 %----------------------------------------------------------------------------------------------------%
  39 %
  40 \begin{frame}\frametitle{Take-home message}%
  41   \begin{itemize}%    
  42   \item<+-> Probabilistic representations are universal coders %
  43   \item<+-> They give explicit hypothesis for data fits%
  44   \item<+-> Dynamical implementation of motion estimation %
  45   \end{itemize}%xplicit coding in neural populations / validation with Optical Imaging
  46 \end{frame}%
  47 %
  48 %----------------------------------------------------------------------------------------------------%
  49 \input{Annex}%
  50 %----------------------------------------------------------------------------------------------------%
  51 \end{center}
  52 \end{document}

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  • [get | view] (2014-10-25 13:40:03, 3.1 KB) [[attachment:Annex.tex]]
  • [get | view] (2014-10-25 13:40:03, 3.7 KB) [[attachment:Context.tex]]
  • [get | view] (2014-10-25 13:40:04, 1.4 KB) [[attachment:Makefile]]
  • [get | view] (2014-10-25 13:40:05, 17.3 KB) [[attachment:Motion.tex]]
  • [get | view] (2009-04-14 10:54:25, 35599.0 KB) [[attachment:perrinet09int.pdf]]
  • [get | view] (2014-10-25 13:46:45, 3.3 KB) [[attachment:perrinet09int.tex]]
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