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Laurent Perrinet - Team InViBe
Institut de Neurosciences de la Timone UMR 7289
Aix Marseille Université, CNRS, 13385 cedex 5, Marseille, France
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http://invibe.net/LaurentPerrinet

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<Laurent DOT Perrinet AT univ-amu  DOT fr>

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Institut de Neurosciences de la Timone (UMR 7289)
Aix Marseille Université, CNRS
Faculté de Médecine - Bâtiment Neurosciences
27, Bd Jean Moulin
13385 Marseille Cedex 05
France

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+33.491 324 044

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<Laurent DOT Perrinet AT gmail DOT com>

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+33 6 19 47 81 20

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fig_oc.png

Figure 3: Is the spike representation over-complete in the retina? (Left) We compared the progressive transmission of information for different degrees of over-completeness in the retina by plotting the average MSE of the residual as a function of the information to code the spike list (in logarithmic scale, propagation up to $12.5\%$ of the relative rank for clarity). The set of neurons used rotation symmetric Mexican hat filters, with scales from layer to layer growing as $\rho=\{ 2,\sqrt{2 },\sqrt[4]{2 },\sqrt[8]{2 } \}$ (and denoted on the legend respectively as 1, 2, 4 and 8). As a comparison we plotted the method used in~\citep{van-Rullen01a} (line 'Wav'). As a function of rank, the MSE decreases more rapidly for increasing degrees of over-completeness. (Right) But if we plot the trade-off of MSE with CPU usage as a function of the over-completeness, we find that for the same amount of information the adaptive dyadic strategy is optimal. One should note that the results of the method described in the text is better than the wavelet method of [van-Rullen, 01] since it is adaptative.


"la mémoire n’est pas faite pour se rappeler du passé mais pour prédire le futur." (Alain Berthoz , 2010)

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