MASSON Guillaume

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


My main goal is understanding how the brain processes visual motion information to drive and control eye movements or to perceive our environments. More specifically, I investigate low-level mechanisms extracting local changes in luminance for the estimation of local speed and direction. These information are then grouped in a selective manner in order to parse the visual scene into particular objects of interest. Measuring their global motion is a computational challenge given the ambiguous and noisy sensory inputs. Over the last decade, my research has focused of one particular problem : how neural systems reconstruct the global 2D motion direction of an extended pattern/object from the local, ambiguous local motion signals [1]. Using eye movements, we have identified the temporal dynamics of 2D motion integration. Departing from the classical, linear filtering view of the problem, we have shown that local 1D and 2D motion signals are extracted with different latencies. As a consequence of the aperture problem of 1D signals, eye movements are systematically initiated in the direction orthogonal to the local edge. In both human and non-human primates, it takes more than 100ms for the pursuit responses to correct this initial bias bu progressively taking into account local 2D motion signals (see Masson, 2004 ; Masson et al., 2010 for reviews). Solving the aperture problem (i.e. measuring global motion) involves local linear and nonlinear integration mechanisms such as center-surround interactions and gain control. We have also shown that ocular following responses in both species reflect many of the dynamical properties of these mechanisms (see Masson & Perrinet, 2012 for a review) : gain control is dynamical and context-dependent. These behavioral researches drive physiological works in collaboration with F Chavane using VSD imaging and electrophysiology in macaque primary visual cortex. We have unveiled the contribution of local recurrent and long-distance intra-cortical interactions in the dynamics of contextual gain control, at population level. We are now investigating how these neuronal mechanisms can explain the behavioral responses. My work on 2D motion integration has also driven several theoretical studies. With Pierre Kornprobst at INRIA (Sophia-Antipolis), we have studied the role of anisotropic diffusion mechanisms in solving the aperture problem. We have proposed by dynamical interactions between motion (V1-MT) and form/luminance (V1-V2) recurrent loops can solve a wide class of motion integration problems (see Tlapale et al., 2010). At INT, with Laurent Perrinet, we explore how such diffusion can be implemented at the level of population using probabilistic codes (see Perrinet & Masson, 2012). Lastly, we Anna Montagnini, we extend these theoretical studies in the context of smooth pursuit eye movements, using the aperture problem as a probe of hierarchical inference in sensorimotor transformations (see Bogadhi et al., 2011).

My current work turned to another long-standing challenge in visual neurosciences. It is far from being understood how speed information is encoded, represented and decoded by biological system. This is surprising given the fact that many of our daily behaviors involve speed estimation. We have developped a new set of motion stimuli, called motion clouds (see Sanz-Léon et al., 2012) that fulfil two of the requirements for efficiently investigating this problem. First, information is distributed across multiple scales and their statistical properties are naturalistics. Thus, they are an exquisite tools to understand how the brain pools local motion across different spatiotemporal channels to measure speed. Second, they offer a parametric framework to investigate how the visual system processes natural images at physiological, behavioral and perceptual levels. Our recent study published in Nature Neuroscience (Simoncini et al., 2012) give a first evidence of what can be achieved with these motion clouds.

The team InViBe is the extension of the team DyVa that I founded in 2004. The goal of the team was to develop interdisciplinary research in the fields of visual neuroscience and oculomotor control. Mixing behavioral, psychophysical and physiological approaches is an essential step to deeply understanding a complex problem such as motion integration. I believe that our recent achievements are a demonstration of the need for such approach.

This integrative, system-level approach of neuroscience is at the heart of the scientific goals of the Institut de Neurosciences de la Timone. As the founder, and current Director, of the Institute, I strongly believe that day-to-day interactions between neurobiologists, neurophysiologists, psychologists and modelers is essential to promote exciting research and depassing the frontiers. This is even more imperative when addressing the challenges of brain and sensory organs diseases where translational and integrative researches forster each other.

[1] see the bood I edited with Dr Uwe Ilg (U Tuebingen) in 2010 "Dynamics of visual motion processing". Springer (Link to eBook) JPEG


  • 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., Masquelier T., Bermudez M.A., Masson G.S., Deco G., et Vanzetta I. (2017). Visual stimulation quenches global alpha range activity in awake primate V4: a case study. Neurophotonics, 4: 031222.

  • 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.

  • Medathati N.V.K., Rankin J., Meso A.I., Kornprobst P., et Masson G.S. (2017). Recurrent network dynamics reconciles visual motion segmentation and integration. Scientific Reports, 7.

  • 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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