Full list of articles as primary author

Citekey Title
Perrinet02sparse Sparse spike coding in an asynchronous feed-forward multi-layer neural network using Matching Pursuit
Perrinet02stdp Coherence detection in a spiking neuron via hebbian learning
Perrinet03ieee Coding static natural images using spiking event times : do neurons cooperate?
Perrinet03 Emergence of filters from natural scenes in a sparse spike coding scheme
Perrinet04nc Finding Independent Components using spikes : a natural result of hebbian learning in a sparse spike coding scheme
Perrinet04tauc Feature detection using spikes : the greedy approach
Perrinet06 Dynamical Neural Networks: modeling low-level vision at short latencies
Perrinet07neurocomp Modeling spatial integration in the ocular following response using a probabilistic framework
Perrinet10shl Role of homeostasis in learning sparse representations
Perrinet12pred Motion-based prediction is sufficient to solve the aperture problem
Perrinet15bicv Sparse Models for Computer Vision
Perrinet15eusipco Sparse coding of natural images using a prior on edge co-occurences
Perrinet16EUVIP Biologically-inspired characterization of sparseness in natural images
PerrinetAdamsFriston14 Active Inference, tracking eye movements and oculomotor delays
PerrinetBednar15 Edge co-occurrences can account for rapid categorization of natural versus animal images

Full Fist of publications

Citekey Title
Adams12 Smooth Pursuit and Visual Occlusion: Active Inference and Oculomotor Control in Schizophrenia
Barthelemy07 Dynamics of distributed 1D and 2D motion representations for short-latency ocular following
Bogadhi10vss A recurrent Bayesian model of dynamic motion integration for smooth pursuit
Bogadhi10 Pursuing motion illusions: a realistic oculomotor framework for Bayesian inference
Cessac07 Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision
CristobalPerrinetKeil15bicv_chap1 Introduction
CristobalPerrinetKeil15bicv Biologically Inspired Computer Vision
Damasse14gdr On the nature of anticipatory eye movements and the factors affecting them
Damasse15gdr Anticipatory smooth eye movements as operant behavior
Damasse15vss Anticipatory smooth eye movements and reinforcement
Damasse16ecvp Modeling the effect of dynamic contingencies on anticipatory eye movements
Damasse16vss Operant reinforcement versus reward expectancy: effects on anticipatory eye movements
Danion15sfn Eye tracking a self-moved target with complex hand-target dynamics
Daucé10 Computational Neuroscience, from Multiple Levels to Multi-level
Davison07cns PyNN: towards a universal neural simulator api in Python
Davison08 PyNN: A common interface for neuronal network simulators
Fischer05 Efficient representation of natural images using local cooperation
Fischer07cv Self-invertible 2D log-Gabor wavelets
Fischer07 Sparse approximation of images inspired from the functional architecture of the primary visual areas
Friston12 Perceptions as Hypotheses: Saccades as Experiments
Kaplan13 Anisotropic connectivity implements motion-based prediction in a spiking neural network
Kaplan14beijing Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network
KaplanKhoei14 Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network
Khoei10tauc Dynamical emergence of a neural solution for motion integration
Khoei11cns Motion-based predictive coding is sufficient to solve the aperture problem
Khoei11ecvp Role of motion inertia in dynamic motion integration for smooth pursuit
Khoei11sdn Dynamical solution for aperture problem using Motion-based predictive coding
Khoei12sfn Role of motion-based prediction in motion extrapolation
Khoei13cns Motion-based prediction and development of the response to an 'on the way' stimulus
Khoei13jpp Role of motion-based prediction in motion extrapolation
Khoei14vss Motion-based prediction model for flash lag effect
KhoeiMassonPerrinet17 The flash-lag effect as a motion-based predictive shift
Kremkow07cns Synchrony in thalamic inputs enhances propagation of activity through cortical layers
Kremkow08neurocomp Functional properties of feed-forward inhibition
Kremkow08sfn Control of the temporal interplay between excitation and inhibition by the statistics of visual input: a V1 network modelling study
Kremkow09cnstalk Control of the temporal interplay between excitation and inhibition by the statistics of visual input
Kremkow09cns Dynamics of non-linear cortico-cortical interactions during motion integration in early visual cortex: A spiking neuron model of an optical imaging study in the awake monkey
Kremkow09gns Functional consequences of correlated excitation and inhibition on single neuron integration and signal propagation through synfire chains
Kremkow10jcns Functional consequences of correlated excitatory and inhibitory conductances in cortical networks
Kremkow16 Push-Pull Receptive Field Organization and Synaptic Depression: Mechanisms for Reliably Encoding Naturalistic Stimuli in V1
Mansour16ecvp Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit
Mansour16gdr Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit
Mansour16sfn Voluntarily tracking moving clouds: Effects of spatial frequency bandwidth on human smooth pursuit
Masson12areadne Motion-based prediction is sufficient to solve the aperture problem
Masson12 The behavioral receptive field underlying motion integration for primate tracking eye movements
Meso13vss How and why do image frequency properties influence perceived speed?
Meso14vss Beyond simply faster and slower: exploring paradoxes in speed perception
Montagnini07 Bayesian modeling of dynamic motion integration
Montagnini15bicv Visual motion processing and human tracking behavior
Montagnini15sfn Anticipating a moving target: role of vision and reinforcement
Montagnini16ecvp Effects of motion predictability on anticipatory and visually-guided eye movements: a common prior for sensory processing and motor control?
Nava13 Advances in Texture Analysis for Emphysema Classification
Perrinet02esann Sparse Image Coding Using an Asynchronous Spiking Neural Network
Perrinet02sparse Sparse spike coding in an asynchronous feed-forward multi-layer neural network using Matching Pursuit
Perrinet02stdp Coherence detection in a spiking neuron via hebbian learning
Perrinet03ieee Coding static natural images using spiking event times : do neurons cooperate?
Perrinet03thesis abtract
Perrinet03thesis soutenance
Perrinet03 Emergence of filters from natural scenes in a sparse spike coding scheme
Perrinet04nc Finding Independent Components using spikes : a natural result of hebbian learning in a sparse spike coding scheme
Perrinet04tauc Feature detection using spikes : the greedy approach
Perrinet05ecvp Dynamics of motion representation in short-latency ocular following: A two-pathways bayesian model
Perrinet05icann Summary
Perrinet06cns An efficiency razor for model selection and adaptation in the primary visual cortex
Perrinet06fens additional information
Perrinet06neurocomp Input-output transformation in the visuo-oculomotor loop: modeling the ocular following response to center-surround stimulation in a probabilistic framework
Perrinet06neurocomp accompanying poster
Perrinet06 Dynamical Neural Networks: modeling low-level vision at short latencies
Perrinet07cns On efficient sparse spike coding schemes for learning natural scenes in the primary visual cortex
Perrinet07neurocomp Modeling spatial integration in the ocular following response using a probabilistic framework
Perrinet08areadne Decoding the population dynamics underlying ocular following response using a probabilistic framework
Perrinet08cosyne_learning What adaptive code for efficient spiking representations? A model for the formation of receptive fields of simple cell
Perrinet08cosyne_motion Modeling spatial integration in the ocular following response to center-surround stimulation using a probabilistic framework
Perrinet08spie Adaptive sparse spike coding : applications of neuroscience to the compression of natural images
Perrinet09cns Decoding the population dynamics underlying ocular following response using a probabilistic framework
Perrinet09cosyne Decoding center-surround interactions in population of neurons for the ocular following response
Perrinet09vss Inferring monkey ocular following responses from V1 population dynamics using a probabilistic model of motion integration
Perrinet09vss accompanying poster
Perrinet09vss related bibliography
Perrinet10areadne Emergence of behavioral receptive fields in a probabilistic motion field
Perrinet10shl Role of homeostasis in learning sparse representations
Perrinet11sfn Edge statistics in natural images versus laboratory animal environments: implications for understanding lateral connectivity in V1
Perrinet12areadne Active inference, smooth pursuit and oculomotor delays
Perrinet12pred Motion-based prediction is sufficient to solve the aperture problem
Perrinet13cns Active inference, smooth pursuit and oculomotor delays
Perrinet13jffos Active inference, smooth pursuit and oculomotor delays
Perrinet14hdr Codage prédictif dans les transformations visuo-motrices
Perrinet14vss Edge co-occurrences are sufficient to categorize natural versus animal images
Perrinet15bicv Sparse Models for Computer Vision
Perrinet15eusipco Sparse coding of natural images using a prior on edge co-occurences
Perrinet16EUVIP Biologically-inspired characterization of sparseness in natural images
Perrinet16networks Compensation of oculomotor delays in the visual system's network
PerrinetAdamsFriston14 Active Inference, tracking eye movements and oculomotor delays
PerrinetBednar15 Edge co-occurrences can account for rapid categorization of natural versus animal images
Ravello16droplets Differential response of the retinal neural code with respect to the sparseness of natural images
Rudiger14cosyne Relationship between natural image statistics and lateral connectivity in the primary visual cortex
Sanz12 Motion Clouds: Model-based stimulus synthesis of natural-like random textures for the study of motion perception
Simoncini10vss Different pooling of motion information for perceptual speed discrimination and behavioral speed estimation
Simoncini11ecem Ocular Following Response for natural-statistic visual stimuli
Simoncini12Pattern Pattern discrimination for moving random textures: Richer stimuli are more difficult to recognize
Simoncini12coding Measuring speed of moving textures: Different pooling of motion information for human ocular following and perception
Simoncini12vss Effect of image statistics on fixational eye movements
Simoncini12 More is not always better: dissociation between perception and action explained by adaptive gain control
Simoncini13vss Measuring speed of moving textures: Different temporal integration for ocular following and speed perception
Simoncini14vss The characteristics of microsaccadic movements varied with the change of strategy in a match-to-sample task
Taouali14areadne A Simple Model of Orientation Encoding Accounting For Multivariate Neural Noise
Taouali14neurocomp A Simple Model of Orientation Encoding Accounting For Multivariate Neural Noise
Taouali15icmns On overdispersion in neuronal evoked activity
Taouali15vss A dynamic model for decoding direction and orientation in macaque primary visual cortex
Taouali15 Testing the Odds of Inherent versus Observed Overdispersion in Neural Spike Counts
Taouali16areadne A dynamic model for decoding direction and orientation in macaque primary visual cortex
Vacher14ihp Dynamic Textures For Probing Motion Perception
Vacher15icms A Mathematical Account of Dynamic Texture Synthesis for Probing Visual Perception
Vacher15nips Biologically Inspired Dynamic Textures for Probing Motion Perception
Vacher16 Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures
Voges08fens Dynamics of cortical networks based on patchy connectivity patterns
Voges08neurocomp Analyzing cortical network dynamics with respect to different connectivity assumptions
Voges09cns Recurrent cortical networks with realistic horizontal connectivities show complex dynamics
Voges09cosyne Dynamical state spaces of cortical networks representing various horizontal connectivities
Voges09gns Dynamics of cortical networks including long-range patchy connections
Voges10neurocomp Phase space analysis of networks based on biologically realistic parameters
Voges11bccn Variations of horizontal cortical network structures and their corresponding state space dynamics
Voges12 Complex dynamics in recurrent cortical networks based on spatially realistic connectivities
Wohrer06 Contrast sensitivity adaptation in a virtual spiking retina and its adequation with mammalians retinas
Yger09gns Neuralensemble: Towards a meta-environment for network modeling and data analysis


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