MESO Andrew

Nom : MESO
Prénom : Andrew
Téléphone : 04 91 32 00 00
Fonction : Chercheur associé
Grade : xxxx
Bureau : xxxx


  • 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.

  • 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.

  • Vacher J., Meso A.I., Perrinet L.U., et Peyré G. (2018). Bayesian Modeling of Motion Perception Using Dynamical Stochastic Textures. Neural Computation, 1-38.
  • Vacher J., Meso A., Perrinet L., et Peyre G. (2015). Biologically Inspired Dynamic Textures for Probing Motion Perception. Advances in Neural Information Processing Systems, 28: 1918 - 1926.
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