PERRINET Laurent

Name : PERRINET
Surname : Laurent
Phone : 04 91 32 40 44
Fonction : Chercheur
Grade : CRCN
Office : 2.06

Publications


  • Adams R.A., Perrinet L.U., and Friston K. (2012). Smooth pursuit and visual occlusion: active inference and oculomotor control in schizophrenia. PloS One, 7: e47502.


  • Chemla S., Reynaud A., di Volo M., Zerlaut Y., Perrinet L., Destexhe A., and Chavane F. (2019). Suppressive Traveling Waves Shape Representations of Illusory Motion in Primary Visual Cortex of Awake Primate. The Journal of Neuroscience, 39: 4282-4298.

  • Cristóbal G., Perrinet L.U., and Keil M.S. (2015). Biologically Inspired Computer Vision: Fundamentals and Applications. Biologically Inspired Computer Vision, 1-10.


  • Damasse J.-B., Perrinet L.U., Madelain L., and Montagnini A. (2018). Reinforcement effects in anticipatory smooth eye movements. Journal of Vision, 18: 14.


  • Dupeyroux J., Boutin V., Serres J.R., Perrinet L.U., and Viollet S. (2018). M² APix: A Bio-Inspired Auto-Adaptive Visual Sensor for Robust Ground Height Estimation. 2018 IEEE International Symposium on Circuits and Systems (ISCAS), 1-4.


  • Friston K., Adams R.A., Perrinet L., and Breakspear M. (2012). Perceptions as Hypotheses: Saccades as Experiments. Frontiers in Psychology, 3.


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

  • Montagnini A., Perrinet L.U., and Masson G.S. (2015). Visual Motion Processing and Human Tracking Behavior. Biologically Inspired Computer Vision, 267-294.


  • Perrinet L.U. (2010). Role of Homeostasis in Learning Sparse Representations. Neural Computation, 22: 1812-1836.

  • Perrinet L.U., Adams R.A., and Friston K.J. (2014). Active inference, eye movements and oculomotor delays. Biological Cybernetics, 108: 777 - 801.


  • Perrinet L.U. and Bednar J.A. (2015). Edge co-occurrences can account for rapid categorization of natural versus animal images. Scientific Reports, 5: 11400.

  • Perrinet L.U. (2015). Sparse Models for Computer Vision. Biologically Inspired Computer Vision, 319-346.
  • Perrinet L. and Masson G.S. (2012). Motion-Based Prediction is Sufficient to Solve the Aperture Problem. Neural Computation, 24.


  • Ravello C.R., Perrinet L.U., Escobar M.-J., and Palacios A.G. (2019). Speed-Selectivity in Retinal Ganglion Cells is Sharpened by Broad Spatial Frequency, Naturalistic Stimuli. Scientific Reports, 9.


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


  • Taouali W., Benvenuti G., Wallisch P., Chavane F., and Perrinet L.U. (2016). Testing the odds of inherent vs. observed overdispersion in neural spike counts. Journal of Neurophysiology, 115: 434-444.


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