Laurent Perrinet is a computational neuroscientist (DR2 CNRS) at the Institut de Neurosciences de la Timone (UMR 7289, CNRS / Aix-Marseille Université), within the NeOpTo team. His research investigates predictive processing in the visual system — from single cortical cells to active vision and behavior — and its translation into neuromorphic algorithms. He has co-authored more than 63 peer-reviewed articles (h-index 30), supervised 6 completed PhD students and currently directs 3 PhD students (Alexandre Lainé, Matthis Dallain, Kevin Mairot). His work combines neurophysiology (Neuropixels recordings in marmoset), computational modeling (spiking neural networks, Free-Energy Principle) and open-source algorithmic development (MotionClouds, AnEMo, LogGabor).
Interests
- Predictive Processing & Active Inference
- Spiking Neural Networks & Neuromorphic Computing
- Computational Vision & Eye Movements
Education
Habilitation à diriger des recherches, 2017
Aix-Marseille Université
PhD. in Cognitive Science, 2003
Université P. Sabatier, Toulouse, France
M.S. in Engineering, 1998
SupAéro, Toulouse, France
Latest
- A saccade-inspired approach to image classification using vision transformer attention maps
- Spiking neural nets
- Artificial neural networks and machine learning applied to the understanding of biological vision
- Variable Density, série Delaunay
- Recréer des réseaux neuronaux pour améliorer la compréhension de notre cerveau
- Al for visual processing and energy-efficient algorithms
- Neurosciences and sparsity
- Open PhD Position in Computational & Systems Neuroscience : 'Neuromodulatory control of predictive processing in visual cortical circuits'
- Physiological state Monitoring: a Riemannian Geometry based-model
- A New Look for Convolutional Deep Networks
- A New Look for Convolutional Deep Networks
- NeuroTalk sur le thème des métiers du cerveau
- Detection of spiking motifs of arbitrary length in neural activity using bounded synaptic delays
- Detection of spiking motifs of arbitrary length in neural activity using bounded synaptic delays
- Mislocalization by Design: The Flash-Lag Effect as Prediction
- Rencontre cinémas & sciences à l'école Air Bel
- DynTex: A Real-Time Generative Model of Dynamic Naturalistic Luminance Textures
- Foveated Retinotopy Improves Classification and Localization in CNNs
- Conférence immersive : La vision, réalité ou perception ?
- Ciné-Sciences : Ouvrez grand les yeux
- Artificial neural networks and machine learning applied to the understanding of biological vision
- La vibration des apparences
- Robust Unsupervised Learning of Spike Patterns with Optimal Transport Theory
- NeuroSchool PhD Program in Neuroscience: Sparse representations
- Qu'est-ce que les Neurosciences peuvent apporter à l'Intelligence Artificielle ?
- When Cortical Neurons Talk Sideways: Beyond Feedforward Visual Processing
- How and why foveated retinotopy provides efficient vision
- Classification of Mental Workload Spatial Effects using Riemannian Manifold
- Integrating the What and Where Visual Pathways to Improve CNN Categorisation
- Population decoding of visual motion direction in V1 marmoset monkey : effects of uncertainty
- NeuroAI: interactions multiples entre Neurosciences et Intelligence artificielle
- SNUFA 2024
- La vibration des apparences
- A Robust Event-Driven Approach to Always-on Object Recognition
- Robots aériens agiles bio-mimetiques pour le vol en conditions réelles: Enjeux pour l'IA embarqué
- Le mystère de la Joconde éclairé par les neurosciences
- Le mystère de la Joconde éclairé par les neurosciences
- Kernel Heterogeneity Improves Sparseness of Natural Images Representations
- Accurate Detection of Spiking Motifs in Neurobiological Data by Learning Heterogeneous Delays of a Spiking Neural Network
- Diverse Neuronal Responses to Visual Precision in Cat Cortical Area 21a: Unraveling the Complexity of Orientation Processing
- Retinotopy in CNN's implements Efficient Visual Search
- Self-Supervised Learning of Spiking Motifs in Neurobiological Data
- Rencontre cinémas & sciences à la prison des Baumettes
- Soutenance de thèse Antoine Grimaldi
- Artificial neural networks and machine learning applied to the understanding of biological vision
- PhD thesis 'Focus of attention: a sensory-motor task for energy reduction in spiking neural networks'
- Sparse representations
- Artificial neural networks applied to the understanding of biological vision
- Analyser de larges volumes de données neurobiologiques, vers une approche biomimétique
- Chats, mouches, humains : comment la vision a évolué en de multiples facettes
- Participation au jury du RISC
- Event-based vision
- Event-based vision
- Event-based vision
- Accurate Detection of Spiking Motifs by Learning Heterogeneous Delays of a Spiking Neural Network
- Time-to-Contact Map by Joint Estimation of Up-to-Scale Inverse Depth and Global Motion using a Single Event Camera
- Emergences (2023 / 2027)
- Accurate Detection of Spiking Motifs by Learning Heterogeneous Delays of a Spiking Neural Network
- Accurate Detection of Spiking Motifs by Learning Heterogeneous Delays of a Spiking Neural Network
- Retinotopy improves the categorisation and localisation of visual objects in CNNs
- Beyond $\ell_1$ sparse coding in V1
- Learning heterogeneous delays in a layer of spiking neurons for fast motion detection
- Event-based vision
- Retinotopy in CNN's implements Efficient Visual Search
- Comment notre cerveau fait-il face à l’incertitude ?
- Ce que le paranormal dit de notre cerveau
- Cortical recurrence supports resilience to sensory variance in the primary visual cortex
- Interactions between machine learning, artificial neural networks and our understanding of biological vision
- Convolutional Sparse Coding is improved by heterogeneous uncertainty modeling
- Postdoc position "Accurate detection of precise spiking motifs in neurobiological data"
- Artificial neural networks and machine learning applied to the understanding of biological vision
- Artificial neural networks and machine learning applied to the understanding of biological vision
- Ultra-Fast Image Categorization in biology and in neural models
- Resilience to sensory uncertainty in the primary visual cortex
- Learning heterogeneous delays of spiking neurons for motion detection
- Game theory and brain strategies
- 2023-01-23_game-theory-and-the-brain
- Formes et perception
- Retinotopy improves the categorisation and localisation of visual objects in CNNs
- Stakes of Neuromorphic Foveation: a promising future for embedded event cameras
- Precise spiking motifs in neurobiological and neuromorphic data
- 2022-11-21_flash-lag-effect
- Learning heterogeneous delays of spiking neurons for motion detection
- Cristal N°2
- Detection of precise spiking motifs using spike-time dependent weight and delay plasticity
- Polychronies (2022 / 2025)
- Pooling in a predictive model of V1 explains functional and structural diversity across species
- Learning heterogeneous delays of spiking neurons for motion detection
- Recurrent cortical connectivity in the primary visual cortex supports robust encoding of natural sensory inputs
- Ultra-rapid visual search in natural images using active deep learning
- A resilient neural code in V1 to process natural images
- Decoding spiking motifs using neurons with heterogeneous delays
- Ultra-rapid visual search in natural images using active deep learning
- Which sparsity problem does the brain solve?
- Learning heterogeneous delays of Spiking Neurons for motion detection
- Retinotopic mapping improves the reliability of image classification
- Contributions of neuroscience to the detection and localization of objects in visual inputs
- Polychrony detection using heterogeneous delays
- Réseaux de neurones artificiels et apprentissage machine appliqués à la compréhension de la vision
- Statistics of the sparse representations of natural images
- Revisiting Horizontal Connectivity Rules in V1: From like-to-like towards like-to-All
- Des illusions aux hallucinations visuelles: une porte sur la perception
- A Behavioral Receptive Field for Ocular Following in Monkeys: Spatial Summation and Its Spatial Frequency Tuning
- What You See Is What You Transform: Foveated Spatial Transformers as a Bio-Inspired Attention Mechanism
- Decoding orientation distributions from noisy observations in V1
- Ultra-fast categorization of images containing animals in vivo and in computo
- From event-based computations to a bio-plausible Spiking Neural Network
- Horizon Faille @ interstices
- Simulating anticipatory activity in a 1D Spiking Neural Network Model
- Soutenance de thèse Angelo Franciosini
- Dynamical processing of orientation precision in the primary visual cortex
- ANR ACES (2022/2026)
- A homeostatic gain control mechanism to improve event-driven object recognition
- Pooling in a predictive model of V1 explains functional and structural diversity across species
- Neural Turing Patterns
- Dynamical processing of orientation precision in the primary visual cortex
- Les illusions sèment le trouble dans les esprits
- ANR RubinVase (2021/2024)
- A robust bio-inspired approach to event-driven object recognition
- Sparse Deep Predictive Coding captures contour integration capabilities of the early visual system
- Le jeu du cerveau et du hasard
- Modulation of orientation selectivity by orientation precision
- Visual search as active inference
- ANR AgileNeuRobot (2021/2025)
- Modelling Complex-cells and topological structure in the visual cortex of mammals using Sparse Predictive Coding
- Understanding natural vision using deep predictive coding
- PhD offer "Ultra-fast vision using Spiking Neural Networks"
- A dual foveal-peripheral visual processing model implements efficient saccade selection
- ANR PRIOSENS (2021/2025)
- ANR ShootingStar (2021/2024)
- From the retina to action: Understanding visual processing
- Modelling Complex-cells and topological structure in the visual cortex of mammals using Sparse Predictive Coding
- 2020-03-13: Soutenance de thèse Victor Boutin
- Effect of top-down connections in Hierarchical Sparse Coding
- Humans adapt their anticipatory eye movements to the volatility of visual motion properties
- Des illusions aux hallucinations visuelles: une porte sur la perception
- La vision comme processus prédictif: Une approche bio-mimétique
- Anticipatory Responses along Motion Trajectories in Awake Monkey Area V1
- From the retina to action: Dynamics of predictive processing in the visual system
- Learning dynamics in a neural network model of the primary visual cortex
- Postdoc position on Visual computations using Spatio-temporal Diffusion Kernels and Traveling Waves
- 2019-10-10: GDR vision 2019
- 2019-10-10: Atelier Utiliser l'apprentissage profond en vision
- Le temps des sens
- A dynamic model for decoding direction and orientation in macaque primary visual cortex
- APROVIS3D (2019/2023)
- Learning where to look: a foveated visuomotor control model
- Illusions et hallucinations visuelles : une porte sur la perception
- Should I stay or should I go? Humans adapt to the volatility of visual motion properties, and know about it
- 2019-05-20: Symposium on Active Inference at NeuroFrance 2019
- Des illusions aux hallucinations visuelles: une porte sur la perception
- Should I stay or should I go? Adaption of human observers to the volatility of visual inputs
- From the retina to action: Understanding visual processing
- From the retina to action: Predictive processing in the visual system
- Suppressive waves disambiguate the representation of long-range apparent motion in awake monkey V1
- Sparse Deep Predictive Coding captures contour integration capabilities of the early visual system
- Speed-Selectivity in Retinal Ganglion Cells is Sharpened by Broad Spatial Frequency, Naturalistic Stimuli
- Should I stay or should I go? Adaption of human observers to the volatility of visual inputs
- Role of dynamics in neural computations underlying visual processing
- Efficient coding of visual information in neural computations
- Modelling spiking neural networks using Brian, Nest and pyNN
- Rencontre avec les collégiens marseillais
- A hierarchical, multi-layer convolutional sparse coding algorithm based on predictive coding
- An adaptive homeostatic algorithm for the unsupervised learning of visual features
- Illusions et hallucinations visuelles : une porte sur la perception
- Modelling Complex Cells of Early Visual Cortex using Predictive Coding
- Orientation selectivity to synthetic natural patterns in a cortical-like model of the cat primary visual cortex
- Sparse Deep Predictive Coding to model visual object recognition
- Temps et cerveau : comment notre perception nous fait voyager dans le temps
- Top-down connection in Hierarchical Sparse Coding
- Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures
- 2018-11-09 : Retinal computations
- La modélisation biomorphique de la perception visuelle
- Intervention fête de la science 2018
- Reinforcement effects in anticipatory smooth eye movements
- Principles and psychophysics of Active Inference in anticipating a dynamic, switching probabilistic bias
- 2018-04-05 : *Probabilities and Optimal Inference to understand the Brain* Workshop
- Probabilities, Bayes and the Free-energy principle
- 2018-03-26 : PhD Program: course in Computational Neuroscience
- Estimating and anticipating a dynamic probabilistic bias in visual motion direction
- Expériences autour de la perception de la forme en art et science
- A low-cost, accessible eye tracking framework
- ANEMO: Quantitative tools for the ANalysis of Eye MOvements
- Estimating and anticipating a dynamic probabilistic bias in visual motion direction
- Estimating and anticipating a dynamic probabilistic bias in visual motion direction
- From biological vision to unsupervised hierarchical sparse coding
- M2APix: a bio-inspired auto-adaptive visual sensor for robust ground height estimation
- On the Origins of Hierarchy in Visual Processing
- Selectivity to oriented patterns of different precisions
- Speed uncertainty and motion perception with naturalistic random textures
- Unsupervised Hierarchical Sparse Coding algorithm inspired by Biological Vision
- Unsupervised learning applied to robotic vision
- Participation au jury
- What dynamic neural codes for efficient visual processing
- Back to the present: dealing with delays in biological and neuromorphic systems
- Tutorial on predictive coding
- The flash-lag effect as a motion-based predictive shift
- Tutorial: Active inference for eye movements: Bayesian methods, neural inference, dynamics
- Tutorial: Sparse optimization in neural computations
- Back to the present: how neurons deal with delays
- Controlling an aerial robot with human gestures using bio-inspired algorithm
- Dynamic modulation of volatility by reward contingencies: effects on anticipatory smooth eye movement
- Efficient learning of sparse image representations using homeostatic regulation
- Efficient learning of sparse image representations using homeostatic regulation
- Estimating and anticipating a dynamic probabilistic bias in visual motion direction
- Expériences autour de la perception de la forme en art et science
- How the dynamics of human smooth pursuit is influenced by speed uncertainty
- Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit
- Participation au jury et entretien avec Clara Delmon
- The flash-lag effect as a motion-based predictive shift
- Reinforcement contingencies modulate anticipatory smooth eye movements
- Differential response of the retinal neural code with respect to the sparseness of natural images
- Biologically-inspired characterization of sparseness in natural images
- Categorization of microscopy images using a biologically inspired edge co-occurrences descriptor
- 2016-10-26 : EUVIP BICV
- Eye movements as a model for active inference
- Biologically-inspired characterization of sparseness in natural images
- Operant reinforcement versus reward expectancy: effects on anticipatory eye movements
- Modelling the dynamics of cognitive processes: from the Bayesian brain to particles
- Les illusions visuelles, un révélateur du fonctionnement de notre cerveau
- ANR BalaV1 (2013/2016)
- ANR Horizontal-V1 (2017/2021)
- ANR PredictEye (2018/2020)
- ANR TRAJECTORY (2016/2019)
- DOC2AMU (2016/2019)
- PhD ICN (2017 / 2021)
- Les illusions visuelles, un révélateur du fonctionnement de notre cerveau
- Elasticité dynamique
- Instabilité (series) @ Art-O-Rama
- Proverbes Et Citations
- Sans gravité – une poétique de l’air
- TRAMES
- TROPIQUE
- Turbulences
- Testing the odds of inherent vs. observed overdispersion in neural spike counts
- A dynamic model for decoding direction and orientation in macaque primary visual cortex
- A dynamic model for decoding direction and orientation in macaque primary visual cortex
- Compensation of oculomotor delays in the visual system's network
- Effects of motion predictability on anticipatory and visually-guided eye movements: a common prior for sensory processing and motor control?
- Modeling the effect of dynamic contingencies on anticipatory eye movements
- Push-Pull Receptive Field Organization and Synaptic Depression: Mechanisms for Reliably Encoding Naturalistic Stimuli in V1
- Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit
- Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit
- Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit
- Motion-based prediction with neuromorphic hardware
- Introduction
- Sparse Models for Computer Vision
- Visual motion processing and human tracking behavior
- Motion-based prediction with neuromorphic hardware
- Biologically Inspired Computer Vision
- Anticipatory smooth eye movements and reinforcement
- Sparse Coding Of Natural Images Using A Prior On Edge Co-Occurences
- BrainScaleS (2011/2014)
- FACETS (2006/2010)
- FACETS-ITN (2010/2013)
- PACE-ITN (2015/2019)
- A Mathematical Account of Dynamic Texture Synthesis for Probing Visual Perception
- Anticipating a moving target: role of vision and reinforcement
- Anticipatory smooth eye movements as operant behavior
- Biologically Inspired Dynamic Textures for Probing Motion Perception
- Edge co-occurrences can account for rapid categorization of natural versus animal images
- Eye tracking a self-moved target with complex hand-target dynamics
- On overdispersion in neuronal evoked activity
- Spatiotemporal tuning of retinal ganglion cells dependent on the context of signal presentation
- Active inference, eye movements and oculomotor delays
- Beyond simply faster and slower: exploring paradoxes in speed perception
- Edge co-occurrences are sufficient to categorize natural versus animal images
- Motion-based prediction model for flash lag effect
- The characteristics of microsaccadic eye movements varied with the change of strategy in a match-to-sample task
- Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network
- Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network
- 2014-04-17: Soutenance d'habilitation à diriger des recherches (HDR)
- Codage prédictif dans les transformations visuo-motrices
- WP5 - Demo 1.3 : Spiking model of motion-based prediction
- Axonal delays and on-time control of eye movements
- A Simple Model of Orientation Encoding Accounting For Multivariate Neural Noise
- A Simple Model of Orientation Encoding Accounting For Multivariate Neural Noise
- Dynamic Textures For Probing Motion Perception
- On the nature of anticipatory eye movements and the factors affecting them
- Relationship between natural image statistics and lateral connectivity in the primary visual cortex
- Demo 1, Task4: Implementation of models showing emergence of cortical fields and maps
- Motion-based prediction explains the role of tracking in motion extrapolation
- Anisotropic connectivity implements motion-based prediction in a spiking neural network
- Edge co-occurrences and categorizing natural images
- Why methods and tools are the key to artificial brain-like systems
- Active inference, eye movements and oculomotor delays
- Active inference, eye movements and oculomotor delays
- Advances in Texture Analysis for Emphysema Classification
- How and why do image frequency properties influence perceived speed?
- Measuring speed of moving textures: Different pooling of motion information for human ocular following and perception
- Motion-based prediction and development of the response to an 'on the way' stimulus
- Smooth Pursuit and Visual Occlusion: Active Inference and Oculomotor Control in Schizophrenia
- Pattern discrimination for moving random textures: Richer stimuli are more difficult to recognize
- Edge statistics in natural images versus laboratory animal environments: implications for understanding lateral connectivity in V1
- Apparent motion in V1 - Probabilistic approaches
- MotionClouds: Model-based stimulus synthesis of natural-like random textures for the study of motion perception
- The behavioral receptive field underlying motion integration for primate tracking eye movements
- Motion Clouds: Model-based stimulus synthesis of natural-like random textures for the study of motion perception
- Grabbing, tracking and sniffing as models for motion detection and eye movements
- Edge statistics in natural images versus laboratory animal environments: implications for understanding lateral connectivity in V1
- Motion-based prediction is sufficient to solve the aperture problem
- Active inference, smooth pursuit and oculomotor delays
- Complex dynamics in recurrent cortical networks based on spatially realistic connectivities
- Effect of image statistics on fixational eye movements
- Measuring speed of moving textures: Different pooling of motion information for human ocular following and perception.
- More is not always better: dissociation between perception and action explained by adaptive gain control
- Motion-based prediction is sufficient to solve the aperture problem
- Motion-based prediction is sufficient to solve the aperture problem
- Perceptions as Hypotheses: Saccades as Experiments
- Role of motion-based prediction in motion extrapolation
- Edge statistics in natural images versus laboratory animal environments: implications for understanding lateral connectivity in V1
- Demo 1, Task4: Implementation of models showing emergence of cortical fields and maps
- Edge statistics in natural images versus laboratory animal environments: implications for understanding lateral connectivity in V1
- Pattern discrimination for moving random textures: Richer stimuli are more difficult to recognize
- Propriétés émergentes d'un modèle de prédiction probabiliste utilisant un champ neural
- Qui créera le premier ordinateur intelligent?
- Pursuing motion illusions: a realistic oculomotor framework for Bayesian inference
- Saccadic foveation of a moving visual target in the rhesus monkey
- Edge statistics in natural images versus laboratory animal environments: implications for understanding lateral connectivity in V1
- Role of motion inertia in dynamic motion integration for smooth pursuit
- Probabilistic models of the low-level visual system: the role of prediction in detecting motion
- Phase space analysis of networks based on biologically realistic parameters
- Role of homeostasis in learning sparse representations
- 2010-06-22 : CodeJamNr4
- Functional consequences of correlated excitatory and inhibitory conductances in cortical networks
- 2010-05-27 : Neurocomp08
- Diffraction monochromatique, spectre audiographique
- Models of low-level vision: linking probabilistic models and neural masses
- A recurrent Bayesian model of dynamic motion integration for smooth pursuit
- Computational Neuroscience, from Multiple Levels to Multi-level
- Different pooling of motion information for perceptual speed discrimination and behavioral speed estimation
- Dynamical emergence of a neural solution for motion integration
- Dynamical emergence of a neural solution for motion integration
- Phase space analysis of networks based on biologically realistic parameters
- Probabilistic models of the low-level visual system: the role of prediction in detecting motion
- Reading out the dynamics of lateral interactions in the primary visual cortex from VSD data
- Peut-on parler d'intelligence mécanique?
- Control of the temporal interplay between excitation and inhibition by the statistics of visual input
- Decoding low-level neural information to track visual motion
- Decoding center-surround interactions in population of neurons for the ocular following response
- Dynamical state spaces of cortical networks representing various horizontal connectivities
- Dynamics of cortical networks including long-range patchy connections
- Functional consequences of correlated excitation and inhibition on single neuron integration and signal propagation through synfire chains
- Inferring monkey ocular following responses from V1 population dynamics using a probabilistic model of motion integration
- NeuralEnsemble: Towards a meta-environment for network modeling and data analysis
- Computational Neuroscience: From Representations to Behavior
- Analyzing cortical network dynamics with respect to different connectivity assumptions
- Functional properties of feed-forward inhibition
- Proceedings of the second french conference on Computational Neuroscience, Marseille
- Decoding the population dynamics underlying ocular following response using a probabilistic framework
- From neural activity to behavior: computational neuroscience as a synthetic approach for understanding the neural code.
- Dynamics of distributed 1D and 2D motion representations for short-latency ocular following
- Modeling of spikes, sparseness and adaptation in the primary visual cortex: applications to imaging
- Adaptive Sparse Spike Coding : applications of Neuroscience to the compression of natural images
- Control of the temporal interplay between excitation and inhibition by the statistics of visual input: a V1 network modelling study
- Decoding the population dynamics underlying ocular following response using a probabilistic framework
- Dynamics of cortical networks based on patchy connectivity patterns
- Modeling spatial integration in the ocular following response to center-surround stimulation using a probabilistic framework
- PyNN: A Common Interface for Neuronal Network Simulators
- What adaptive code for efficient spiking representations? A model for the formation of receptive fields of simple cells
- What efficient code for adaptive spiking representations?
- Neural Codes for Adaptive Sparse Representations of Natural Images
- Synchrony in thalamic inputs enhances propagation of activity through cortical layers
- Dynamical Neural Networks: modeling low-level vision at short latencies
- Introduction to Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision
- Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision
- Self-Invertible 2D Log-Gabor Wavelets
- Bayesian modeling of dynamic motion integration
- Dynamic inference for motion tracking
- Modeling spatial integration in the ocular following response using a probabilistic framework
- Neural Codes for Adaptive Sparse Representations of Natural Images
- On efficient sparse spike coding schemes for learning natural scenes in the primary visual cortex
- PyNN: towards a universal neural simulator API in Python
- Sparse Approximation of Images Inspired from the Functional Architecture of the Primary Visual Areas
- Visual tracking of ambiguous moving objects: A recursive Bayesian model
- An efficiency razor for model selection and adaptation in the primary visual cortex
- Bayesian modeling of dynamic motion integration
- Contrast sensitivity adaptation in a virtual spiking retina and its adequation with mammalians retinas
- Dynamical contrast gain control mechanisms in a layer 2/3 model of the primary visual cortex
- Dynamical contrast gain control mechanisms in a layer 2/3 model of the primary visual cortex
- Input-output transformation in the visuo-oculomotor loop: modeling the ocular following response to center-surround stimulation in a probabilistic framework
- Input-output transformation in the visuo-oculomotor loop: modeling the ocular following response to center-surround stimulation in a probabilistic framework
- Input-output transformation in the visuo-oculomotor loop: modeling the ocular following response to center-surround stimulation in a probabilistic framework
- Modeling of simple cells through a sparse overcomplete gabor wavelet representation based on local inhibition and facilitation
- Sparse Gabor wavelets by local operations
- Dynamics of motion representation in short-latency ocular following: A two-pathways Bayesian model
- Efficient representation of natural images using local cooperation
- Efficient Source Detection Using Integrate-and-Fire Neurons
- Coding static natural images using spiking event times: do neurons cooperate?
- Feature detection using spikes : the greedy approach
- Sparse spike coding in an asynchronous feed-forward multi-layer neural network using matching pursuit
- Finding Independent Components using spikes : a natural result of Hebbian learning in a sparse spike coding scheme
- Comment déchiffrer le code impulsionnel de la vision ? Étude du flux parallèle, asynchrone et épars dans le traitement visuel ultra-rapide
- Emergence of filters from natural scenes in a sparse spike coding scheme
- Coherence detection in a spiking neuron via Hebbian learning
- Sparse Image Coding Using an Asynchronous Spiking Neural Network
- Visual Strategies for Sparse Spike Coding
- Network of integrate-and-fire neurons using Rank Order Coding A: how to implement spike timing dependant plasticity
- Network of integrate-and-fire neurons using Rank Order Coding B: spike timing dependant plasticity and emergence of orientation selectivity
- A generative model for Spike Time Dependent Hebbian Plasticity
- Apprentissage hebbien d'un reseau de neurones asynchrone a codage par rang