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1 Neuromath kickoff meeting É. Tlapale 1, G.S. Masson 2, P. Kornprobst 1 1 : Équipe Odyssée (INRIA, ENS, INPC) 2 : Équipe DyVA (INCM/CNRS)
2 Current work We design a bio-inspired model of motion integration. – Functional model (V1/MT) – Feedbacks (MT => V1) – Form modulation (V2) Why ? – To reproduce motion perception – And its dynamic
3 Influence of form Chopstick illusions
4 Influence of form Chopstick illusions
5 Influence de la forme Jonctions intrinsèques vs extrinsèques
6 Multi-layer feedback model Input : grey-level sequence
7 Multi-layer feedback model Local motion detectors: correlation or energy models
8 Multi-layer feedback model
9 Lateral inhibition => winner-take-all
10 Un modèle multi-couche rétroactif Feedback (Bayerl & Neumann, 2004)
11 Multi-layer feedback model Anisotropic diffusion
12 Form-modulated diffusion We only use luminosity for now
13 Reproducing the perception Wallach (1935)
14 Wallace, Stone et Masson (2005) Reproducing the dynamic Eye-movement like read-out
15 Reproducing the neurobiology Huang, Albright et Stoner (2007) Notion of objects
16 Future work Reformulate our model in a neural field formalism And using a multi-layer variational formalism Propose our form-motion interaction as a synchronisation mechanism
17 Journal clubs Biology: Barthelemy et al. (2008) Models: Cesmely & Wang (2000) Technologies: Python, GPGPU
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30 nm © 2005 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice Atomic Switch ITRS Emerging.
Path-Sensitive Analysis for Linear Arithmetic and Uninterpreted Functions SAS 2004 Sumit Gulwani George Necula EECS Department University of California,
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1 Copyright © 2013 Elsevier Inc. All rights reserved. Chapter 7 System Design Techniques.
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