INT - Institut de Neurosciences de la Timone
PERRINET Laurent
Nom : PERRINET
Prénom : Laurent
Téléphone : 04 91 32 40 44
Fonction : Chercheur
Grade : CRCN
Bureau : 2.06
Présentation
Let’s admit it : brains are not computers. Indeed, computers are still deceptive compared to perceptual systems. Think for instance about architectures capable of feeding noisy, ambiguous and rapidly varying raw data to your computer. Or think about architecture performing this in an autonomous manner...
To narrow the gap between neuroscience and the theory of sensory processing computations, I am interested in bridging the statistical geometrical regularities of natural scenes with the properties of neural computations as they are observed in low-level sensory processes or through low-level behavior. For instance, has the predictability of the motion of objects in physical space an impact in the activity of neurons that detect and subsequently on eye movements ? What mechanisms are used to learn statistical regularities ? What happens if these adaptive mechanisms are dysfunctional ?
More on : http://invibe.net/LaurentPerrinet
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.
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.
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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