NeuroAI & Machine Learning

MotionClouds: Model-based stimulus synthesis of natural-like random textures for the study of motion perception
2012-03-22 Computational Neuroscience, NeuroAI & Machine Learning
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The behavioral receptive field underlying motion integration for primate tracking eye movements
Short-latency ocular following are reflexive, tracking eye movements that are observed in human and non-human primates in response to a …
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Motion Clouds: Model-based stimulus synthesis of natural-like random textures for the study of motion perception
Choosing an appropriate set of stimuli is essential to characterize the response of a sensory system to a particular functional …
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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
Moving objects generate sensory information that may be noisy and ambiguous, yet it is important to be able to reconstruct object speed …
2012-01-27 Behavioural Neuroscience, Biological Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning
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Edge statistics in natural images versus laboratory animal environments: implications for understanding lateral connectivity in V1
Oriented edges in images of natural scenes tend to be aligned in collinear or co-circular arrangements, with lines and smooth curves …
2012-01-24 Behavioural Neuroscience, NeuroAI & Machine Learning, Visual Neuroscience
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Motion-based prediction is sufficient to solve the aperture problem
In low-level sensory systems, it is still unclear how the noisy information collected locally by neurons may give rise to a coherent …
2012-01-12 Computational Neuroscience, NeuroAI & Machine Learning
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Active inference, smooth pursuit and oculomotor delays
We consider the problem of sensorimotor delays in the optimal control of movement under uncertainty. Specifically, we consider axonal …
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Complex dynamics in recurrent cortical networks based on spatially realistic connectivities
Most studies on the dynamics of recurrent cortical networks are either based on purely random wiring or neighborhood couplings. …
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Complex dynamics in recurrent cortical networks based on spatially realistic connectivities
Effect of image statistics on fixational eye movements
Under natural viewing conditions, small movements of the eyes prevent the maintenance of a steady direction of gaze. It is unclear how …
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Measuring speed of moving textures: Different pooling of motion information for human ocular following and perception.
To measure speed and direction of moving objects, the cortical motion system pools information across different spatiotemporal …
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More is not always better: dissociation between perception and action explained by adaptive gain control
Moving objects generate motion information at different scales, which are processed in the visual system with a bank of spatiotemporal …
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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
In low-level sensory systems, it is still unclear how the noisy information collected locally by neurons may give rise to a coherent …
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Motion-based prediction is sufficient to solve the aperture problem
Perceptions as Hypotheses: Saccades as Experiments
If perception corresponds to hypothesis testing (Gregory, 1980); then visual searches might be construed as experiments that generate …
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Perceptions as Hypotheses: Saccades as Experiments
Edge statistics in natural images versus laboratory animal environments: implications for understanding lateral connectivity in V1
Oriented edges in images of natural scenes tend to be aligned in collinear or co-circular arrangements, with lines and smooth curves …
2011-11-15 Behavioural Neuroscience, NeuroAI & Machine Learning, Visual Neuroscience
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Demo 1, Task4: Implementation of models showing emergence of cortical fields and maps
2011-10-05 Computational Neuroscience, NeuroAI & Machine Learning
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Edge statistics in natural images versus laboratory animal environments: implications for understanding lateral connectivity in V1
Oriented edges in images of natural scenes tend to be aligned in collinear or co-circular arrangements, with lines and smooth curves …
2011-09-28 Behavioural Neuroscience, NeuroAI & Machine Learning, Visual Neuroscience
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Pattern discrimination for moving random textures: Richer stimuli are more difficult to recognize
In order to analyze the characteristics of a rich dynamic visual environment, the visual system must integrate information collected at …
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Propriétés émergentes d'un modèle de prédiction probabiliste utilisant un champ neural
Sensory informations such as visual images are inherently variable. We use probabilistic models to describe how the low-level visual …
2011-07-02 Computational Neuroscience, NeuroAI & Machine Learning
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Qui créera le premier ordinateur intelligent?
Qui créera le premier ordinateur intelligent? Les ordinateurs classiques sont de plus en plus puissants, mais restent toujours aussi « …
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Qui créera le premier ordinateur intelligent?
Pursuing motion illusions: a realistic oculomotor framework for Bayesian inference
Accuracy in estimating an object’s global motion over time is not only affected by the noise in visual motion information but …
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Saccadic foveation of a moving visual target in the rhesus monkey
When generating a saccade toward a moving target, the target displacement that occurs during the period spanning from its detection to …
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Edge statistics in natural images versus laboratory animal environments: implications for understanding lateral connectivity in V1
Oriented edges in images of natural scenes tend to be aligned in collinear or co-circular arrangements, with lines and smooth curves …
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Probabilistic models of the low-level visual system: the role of prediction in detecting motion
Sensory informations such as visual images are inherently variable. We use probabilistic models to describe how the low-level visual …
2010-12-17 Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
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Phase space analysis of networks based on biologically realistic parameters
We study cortical network dynamics for a spatially embedded network model. It represents, in terms of spatial scale, a large piece of …
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Role of homeostasis in learning sparse representations
Neurons in the input layer of primary visual cortex in primates develop edge-like receptive fields. One approach to understanding the …
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Role of homeostasis in learning sparse representations
2010-06-22 : CodeJamNr4
We held a CodeJam 22nd-24th June 2010, in Marseille.
2010-06-22 1 min read Computational Neuroscience, NeuroAI & Machine Learning
2010-06-22 : CodeJamNr4
2010-05-27 : Neurocomp08
La deuxième conférence française de Neurosciences Computationnelles, Neurocomp08, s’est déroulée à la Faculté de Médecine de Marseille du 8 au 11 octobre 2008.
2010-05-27 2 min read Computational Neuroscience, NeuroAI & Machine Learning
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2010-05-27 : Neurocomp08
Diffraction monochromatique, spectre audiographique
En perception, les neurones « parlent » tous en même temps par de brèves impulsions électrochimiques, générant un mélange de signaux, …
2010-04-14 Computational Neuroscience, NeuroAI & Machine Learning
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Diffraction monochromatique, spectre audiographique
Phase space analysis of networks based on biologically realistic parameters
We study cortical network dynamics for a more realistic network model. It represents, in terms of spatial scale, a large piece of …
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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
Sensory informations such as visual images are inherently variable. We use probabilistic models to describe how the low-level visual …
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Reading out the dynamics of lateral interactions in the primary visual cortex from VSD data
Short presentation of a large moving pattern elicits an ocular following response that exhibits many of the properties attributed to …
2009-11-30 Behavioural Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience, Visual Neuroscience
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Peut-on parler d'intelligence mécanique?
Nous parlerons de cette partie "mécanique" du cerveau animal ou humain qui permet de percevoir les mouvements et de … …
2009-11-24 Behavioural Neuroscience, NeuroAI & Machine Learning
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Decoding low-level neural information to track visual motion
Moving the eyes rapidly to track a visual object moving in a cluttered environment is an essential function. However, doing so rapidly …
2009-04-01 Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
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Correlating Excitation and Inhibition in Visual Cortical Circuits : Functional Consequences and Biological Feasibility
The primary visual cortex (V1) is one of the most studied cortical area in the brain. Together with the retina and the lateral …
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Decoding center-surround interactions in population of neurons for the ocular following response
Short presentation of a large moving pattern elicits an Ocular Following Response (OFR) that exhibits many of the properties attributed …
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Dynamical state spaces of cortical networks representing various horizontal connectivities
Most studies of cor tical network dynamics are either based on purely random wiring or neighborhood couplings, e.g., [Kumar, Schrader, …
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Dynamics of cortical networks including long-range patchy connections
Most studies of cortical network dynamics are either based on purely random wiring or neighborhood couplings [1], focussing on a rather …
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Functional consequences of correlated excitation and inhibition on single neuron integration and signal propagation through synfire chains
Neurons receive a large number of excitatory and inhibitory synaptic inputs whose temporal interplay determines their spiking behavior. …
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Inferring monkey ocular following responses from V1 population dynamics using a probabilistic model of motion integration
Short presentation of a large moving pattern elicits an ocular following response that exhibits many of the properties attributed to …
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Computational Neuroscience: From Representations to Behavior
Computational Neuroscience: From Representations to Behavior, the Second NeuroComp Marseille Workshop.
2008-10-08 8 min read Behavioural Neuroscience, Biological Neuroscience, Computational Neuroscience, Computer Vision, NeuroAI & Machine Learning, Theoretical Neuroscience, Visual Neuroscience
Project
Computational Neuroscience: From Representations to Behavior
Decoding the population dynamics underlying ocular following response using a probabilistic framework
The machinery behind the visual perception of motion and the subsequent sensorimotor transformation, such as in Ocular Following …
2008-06-01 Behavioural Neuroscience, Biological Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
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From neural activity to behavior: computational neuroscience as a synthetic approach for understanding the neural code.
Computational Neuroscience is a synthetic, inter-disciplinary approach aiming at understanding cognition by analyzing the mechanisms …
2008-04-01 Behavioural Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning, Visual Neuroscience
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Dynamics of distributed 1D and 2D motion representations for short-latency ocular following
Integrating information is essential to measure the physical 2D motion of a surface from both ambiguous local 1D motion of its …
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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
related publications @ FENS 2006, @ NeuroComp 2008 and @ AREADNE 2008
2008-02-01 Computational Neuroscience, NeuroAI & Machine Learning, Visual Neuroscience
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Decoding the population dynamics underlying ocular following response using a probabilistic framework
The machinery behind the visual perception of motion and the subsequent sensorimotor transformation, such as in Ocular Following …
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Decoding the population dynamics underlying ocular following response using a probabilistic framework
PyNN: A Common Interface for Neuronal Network Simulators
Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and …
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What efficient code for adaptive spiking representations?
2007-12-01 Computational Neuroscience, NeuroAI & Machine Learning
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Neural Codes for Adaptive Sparse Representations of Natural Images
I will illustrate in this talk how computational neuroscience may inspire and be inspired by mathematical image processing. Focusing on …
2007-09-01 Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience, Visual Neuroscience
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Dynamical Neural Networks: modeling low-level vision at short latencies
The machinery behind the visual perception of motion and the subsequent sensori-motor transformation, such as in ocular following …
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Self-Invertible 2D Log-Gabor Wavelets
Meanwhile biorthogonal wavelets got a very popular image processing tool, alternative multiresolution transforms have been proposed for …
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Self-Invertible 2D Log-Gabor Wavelets
Bayesian modeling of dynamic motion integration
The quality of the representation of an object’s motion is limited by the noise in the sensory input as well as by an intrinsic …
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Dynamic inference for motion tracking
When the visual information about an object’s motion differs at the local level, the visuomotor system needs to integrate …
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Modeling spatial integration in the ocular following response using a probabilistic framework
The machinery behind the visual perception of motion and the subsequent sensori-motor transformation, such as in Ocular Following …
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Modeling spatial integration in the ocular following response using a probabilistic framework
Neural Codes for Adaptive Sparse Representations of Natural Images
I will illustrate in this talk how computational neuroscience may inspire and be inspired by mathematical image processing. Focusing on …
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Neural Codes for Adaptive Sparse Representations of Natural Images
PyNN: towards a universal neural simulator API in Python
Trends in programming language development and adoption point to Python as the high-level systems integration language of choice. …
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Sparse Approximation of Images Inspired from the Functional Architecture of the Primary Visual Areas
relies on log-Gabor filters: Sylvain Fischer, Filip Šroubek, Laurent U Perrinet, Rafael Redondo, Gabriel Cristóbal (2007). …
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Sparse Approximation of Images Inspired from the Functional Architecture of the Primary Visual Areas
Visual tracking of ambiguous moving objects: A recursive Bayesian model
Perceptual and oculomotor data demonstrate that, when the visual information about an object’s motion differs on the local …
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An efficiency razor for model selection and adaptation in the primary visual cortex
We describe the theoretical formulation of a learning algorithm in a model of the primary visual cortex (V1) and present results of the …
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Bayesian modeling of dynamic motion integration
The quality of the representation of an object’s motion is limited by the noise in the sensory input as well as by an intrinsic …
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Dynamical contrast gain control mechanisms in a layer 2/3 model of the primary visual cortex
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Dynamical contrast gain control mechanisms in a layer 2/3 model of the primary visual cortex
Computations in a cortical column are characterized by the dynamical, event-based nature of neuronal signals and are structured by the …
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Input-output transformation in the visuo-oculomotor loop: modeling the ocular following response to center-surround stimulation in a probabilistic framework
The quality of the representation of an object’s motion is limited by the noise in the sensory input as well as by an intrinsic …
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Input-output transformation in the visuo-oculomotor loop: modeling the ocular following response to center-surround stimulation in a probabilistic framework
The quality of the representation of an object’s motion is limited by the noise in the sensory input as well as by an intrinsic …
2006-01-01 Behavioural Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
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Modeling of simple cells through a sparse overcomplete gabor wavelet representation based on local inhibition and facilitation
We present a biologically plausible model of simple cortical cells as 1) a linear transform representing edges and 2) a non-linear …
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Sparse Gabor wavelets by local operations
Efficient sparse coding of overcomplete transforms remains still anopen problem. Different methods have been proposed in theliterature, …
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Dynamics of motion representation in short-latency ocular following: A two-pathways Bayesian model
The integration of information is essential to measure the exact 2D motion of a surface from both local ambiguous 1D motion produced by …
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Efficient representation of natural images using local cooperation
Low-level perceptual computations may be understood in terms of efficient codes (Simoncelli and Olshausen, 2001, Annual Review of …
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Efficient Source Detection Using Integrate-and-Fire Neurons
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Efficient Source Detection Using Integrate-and-Fire Neurons
Coding static natural images using spiking event times: do neurons cooperate?
To understand possible strategies of temporal spike coding in the central nervous system, we study functional neuromimetic models of …
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Coding static natural images using spiking event times: do neurons cooperate?
Comment déchiffrer le code impulsionnel de la vision ? Étude du flux parallèle, asynchrone et épars dans le traitement visuel ultra-rapide
Le jury était consistué (de gauche à droite) de Jeanny Hérault (Rapporteur), Michel Imbert (Président), Yves Burnod (Rapporteur, …
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Apprentissage hebbien d'un reseau de neurones asynchrone a codage par rang
Travail de master sur la STDP.
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2020-12-10_agileneurobot_anr
Présentation du projet - L. Perrinet [2020-12-10] Réunion de lancement AgileNeuRobot: Fiche d’identité Titre : Robots aériens …
0001-01-01 Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
2022-07-01_grimaldi-22-areadne
Decoding spiking motifs using neurons with heterogeneous delays [2022-07-01] AREADNE 2022 conference --- Spiking Neural Networks From …
0001-01-01 Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience