PERRINET Laurent

Nom : PERRINET
Prénom : Laurent
Téléphone : 04 91 32 40 44
Email : laurent.perrinet chez univ-amu.fr
Fonction : Chercheur
Grade : CRCN
Bureau : 2.06

Présentation

Let’s admit it : brains are not computers. Indeed, computers are still deceptive compared to biological perceptual systems. Think about rapidly detecting a novel object in clutter. Think about performing this with little supervision at a low energetic cost…

To narrow the gap between neuroscience and the theory of sensory processing computations, I am interested in bridging geometrical regularities found in natural scenes with the properties of neural computations as they are observed in sensory processes or behavior.

More on : https://laurentperrinet.github.io/

Publications


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


  • 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 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., et 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., et Friston K.J. (2014). Active inference, eye movements and oculomotor delays. Biological Cybernetics, 108: 777 - 801.


  • Perrinet L.U. et 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. et 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., et 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., et 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., et 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., 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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