MASSON Guillaume

Nom : MASSON
Prénom : Guillaume
Téléphone : 04 91 32 40 42
Fonction : Chef d'équipe
Grade : DR1
Bureau : 2.02

Publications


  • Barthélemy F.V., Fleuriet J., et Masson G.S. (2010). Temporal dynamics of 2D motion integration for ocular following in macaque monkeys. Journal of Neurophysiology, 103: 1275-1282.


  • Deneux T., Takerkart S., Grinvald A., Masson G.S., et Vanzetta I. (2012). A processing work-flow for measuring erythrocytes velocity in extended vascular networks from wide field high-resolution optical imaging data. NeuroImage, 59: 2569-2588.


  • Ego C., Bonhomme L., Orban de Xivry J.-J., Da Fonseca D., Lefèvre P., Masson G.S., et Deruelle C. (2016). Behavioral characterization of prediction and internal models in adolescents with autistic spectrum disorders. Neuropsychologia, 91: 335-345.

  • Fregnac Y., Baudot P., Chavane F., Lorenceau J., Marre O., Monier C., Pananceau M., Carelli P.V., et Sadoc G. (2009). Multiscale Functional Imaging in V1 and Cortical Correlates of Apparent Motion. Dynamics of Visual Motion Processing, 73-93.


  • Gekas N., Meso A.I., Masson G.S., et Mamassian P. (2017). A Normalization Mechanism for Estimating Visual Motion across Speeds and Scales. Current Biology, 27: 1514-1520.e3.


  • Hoffart L., Matonti F., Conrath J., Daniel L., Ridings B., Masson G.S., et Chavane F. (2010). Inhibition of corneal neovascularization after alkali burn: comparison of different doses of bevacizumab in monotherapy or associated with dexamethasone. Clinical & Experimental Ophthalmology, 38: 346-352.


  • Kaplan B.A., Lansner A., Masson G.S., et Perrinet L.U. (2013). Anisotropic connectivity implements motion-based prediction in a spiking neural network. Frontiers in Computational Neuroscience, 7.


  • Khoei M.A., Masson G.S., et Perrinet L.U. (2013). Motion-based prediction explains the role of tracking in motion extrapolation. Journal of Physiology-Paris, 107: 409-420.


  • Khoei M.A., Masson G.S., et Perrinet L.U. (2017). The Flash-Lag Effect as a Motion-Based Predictive Shift. PLOS Computational Biology, 13: e1005068.

  • Kremkow J., Perrinet L.U., Masson G.S., et Aertsen A. (2010). Functional consequences of correlated excitatory and inhibitory conductances in cortical networks. Journal of Computational Neuroscience, 28: 579-594.


  • Kremkow J., Perrinet L.U., Monier C., Alonso J.-M., Aertsen A., Frégnac Y., et Masson G.S. (2016). Push-Pull Receptive Field Organization and Synaptic Depression: Mechanisms for Reliably Encoding Naturalistic Stimuli in V1. Frontiers in Neural Circuits, 10.

  • Leon P.S., Vanzetta I., Masson G.S., et Perrinet L.U. (2012). Motion clouds: model-based stimulus synthesis of natural-like random textures for the study of motion perception. Journal of Neurophysiology, 107: 3217-3226.


  • Masson G.S. et Goffart L. (2013). Fixate and stabilize: shall the twain meet? Nature Neuroscience, 16: 663-664.


  • Masson G.S. et Perrinet L.U. (2012). The behavioral receptive field underlying motion integration for primate tracking eye movements. Neuroscience & Biobehavioral Reviews, 36: 1-25.


  • Medathati N.V.K., Neumann H., Masson G.S., et Kornprobst P. (2016). Bio-inspired computer vision: Towards a synergistic approach of artificial and biological vision. Computer Vision and Image Understanding, 150: 1-30.

  • Meso A.I. et Masson G.S. (2014). Dynamic resolution of ambiguity during tri-stable motion perception. Vision Research, 107C: 113-123.


  • Meso A.I., Montagnini A., Bell J., et Masson G.S. (2016). Looking for symmetry: fixational eye movements are biased by image mirror symmetry. Journal of Neurophysiology, 116: 1250-1260.


  • Meso A.I., Rankin J., Faugeras O., Kornprobst P., et Masson G.S. (2016). The relative contribution of noise and adaptation to competition during tri-stable motion perception. Journal of Vision, 16: 1-24.

  • Montagnini A., Perrinet L.U., et Masson G.S. (2015). Visual Motion Processing and Human Tracking Behavior. Biologically Inspired Computer Vision, 267-294.
  • Perrinet L. et Masson G.S. (2012). Motion-Based Prediction is Sufficient to Solve the Aperture Problem. Neural Computation, 24.


  • Reynaud A., Takerkart S., Masson G.S., et Chavane F. (2011). Linear model decomposition for voltage-sensitive dye imaging signals: Application in awake behaving monkey. NeuroImage, 54: 1196-1210.


  • Simoncini C., Perrinet L.U., Montagnini A., Mamassian P., et Masson G.S. (2012). More is not always better: adaptive gain control explains dissociation between perception and action. Nature Neuroscience, 15: 1596-1603.


  • Spotorno S., Masson G.S., et Montagnini A. (2016). Fixational saccades during grating detection and discrimination. Vision Research, 118: 105-118.
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