Talks

Statistics of the sparse representations of natural images
In most mammals, the primary visual cortex (V1) processes complex mixtures of orientations to construct an accurate neural …
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2021-10-12
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Dynamical processing of orientation precision in the primary visual cortex
The primary visual cortex (V1) processes complex mixtures of orientations to build neural representations of our visual environment. It …
Last updated on 2021-10-12
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Pooling in a predictive model of V1 explains functional and structural diversity across species
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Dynamical processing of orientation precision in the primary visual cortex
The primary visual cortex (V1) processes complex mixtures of orientations to build neural representations of our visual environment. It …
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Understanding natural vision using deep predictive coding
Building models which efficiently process images is a great source of inspiration to better understand the processes which underly our …
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Visual search as active inference
Visual search is an essential cognitive ability, offering a prototypical control problem to be addressed with Active Inference. Under a …
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From the retina to action: Understanding visual processing
Visual areas are essential in transforming the raw luminous signal into a representation which efficiently conveys information about …
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Des illusions aux hallucinations visuelles: une porte sur la perception
Les illusions visuelles sont des créations d’artistes, de scientifiques et plus récemment, grâce aux réseaux sociaux, du grand …
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Should I stay or should I go? Humans adapt to the volatility of visual motion properties, and know about it
Animal behavior must constantly adapt to changes, for example when the state of an environmental context changes unexpectedly. For an …
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Should I stay or should I go? Adaption of human observers to the volatility of visual inputs
Animal behavior has to constantly adapt to changes, for instance when unexpectedly switching the state of an environmental context. For …
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From the retina to action: Understanding visual processing
Visual areas are essential in transforming the raw luminous signal into a representation which efficiently conveys information about …
Last updated on 2021-10-12
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From the retina to action: Predictive processing in the visual system
Visual areas are essential in transforming the raw luminous signal into a representation which efficiently conveys information about …
Last updated on 2021-10-12
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Des illusions aux hallucinations visuelles: une porte sur la perception
Les illusions visuelles sont des créations d’artistes, de scientifiques et plus récemment, grâce aux réseaux sociaux, du grand …
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Efficient coding of visual information in neural computations
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Learning where to look: a foveated visuomotor control model
In computer vision, the visual search task consists in extracting a scarce and specific visual information (the target) from a large …
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Modelling spiking neural networks using Brian, Nest and pyNN
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Rencontre avec les collégiens marseillais
Le jeudi 10 janvier 2019, je suis venu échanger au côté de Serge Dentin autour de films traitant du rapport fiction/réel, des illusion …
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Role of dynamics in neural computations underlying visual processing
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Should I stay or should I go? Adaption of human observers to the volatility of visual inputs
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Estimating and anticipating a dynamic probabilistic bias in visual motion direction
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Expériences autour de la perception de la forme en art et science
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Intervention fête de la science 2018
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La modélisation biomorphique de la perception visuelle
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Principles and psychophysics of Active Inference in anticipating a dynamic, switching probabilistic bias
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Probabilities, Bayes and the Free-energy principle
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Back to the present: dealing with delays in biological and neuromorphic systems
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Back to the present: how neurons deal with delays
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Participation au jury
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Tutorial on predictive coding
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Tutorial: Active inference for eye movements: Bayesian methods, neural inference, dynamics
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Tutorial: Sparse optimization in neural computations
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Unsupervised learning applied to robotic vision
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What dynamic neural codes for efficient visual processing
This seminar is an exercise to introduce the AMU masters into the format of international conferences. As such, we will try to …
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Biologically-inspired characterization of sparseness in natural images
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Categorization of microscopy images using a biologically inspired edge co-occurrences descriptor
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Eye movements as a model for active inference
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Les illusions visuelles, un révélateur du fonctionnement de notre cerveau
Ce lundi 25 avril de 9h à 12h, je suis venu échanger au côté de Serge Dentin autour de films traitant du rapport fiction/réel, des …
Last updated on 2021-10-12
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Les illusions visuelles, un révélateur du fonctionnement de notre cerveau
Les illusions visuelles sont des créations d’artistes, de scientifiques et plus récemment, grâce aux réseaux sociaux, du grand …
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Modelling the dynamics of cognitive processes: from the Bayesian brain to particles
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Participation au jury et entretien avec Clara Delmon
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Reinforcement contingencies modulate anticipatory smooth eye movements
Natural environments potentially contain several interesting targets for goal-directed behavior. Thus sensorimotor systems need to …
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The flash-lag effect as a motion-based predictive shift
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Motion-based prediction with neuromorphic hardware
We stand at a point in history where our phones have become smart but lack a feature which prevails in most forms of living …
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Motion-based prediction with neuromorphic hardware
We stand at a point in history where our phones have become smart but lack a feature which prevails in most forms of living …
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Axonal delays and on-time control of eye movements
Moving objects generate sensory information that may be noisy and ambiguous, yet it is important to be able to reconstruct object speed …
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Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network
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WP5 - Demo 1.3 : Spiking model of motion-based prediction
The question how the visual system is able to create a coherent representation of a rapidly changing environment in the presence of …
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Demo 1, Task4: Implementation of models showing emergence of cortical fields and maps
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Edge co-occurrences and categorizing natural images
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Why methods and tools are the key to artificial brain-like systems
This session aims at presenting new ideas that emerged during the first years of BrainScaleS. Indeed, the collaborations that were …
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Apparent motion in V1 - Probabilistic approaches
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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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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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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 …
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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 …
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MotionClouds: Model-based stimulus synthesis of natural-like random textures for the study of motion perception
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Demo 1, Task4: Implementation of models showing emergence of cortical fields and maps
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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 …
Last updated on 2021-10-12
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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 …
Last updated on 2021-10-12
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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 …
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Diffraction monochromatique, spectre audiographique
En perception, les neurones « parlent » tous en même temps par de brèves impulsions électrochimiques, générant un mélange de signaux, …
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Models of low-level vision: linking probabilistic models and neural masses
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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 …
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Control of the temporal interplay between excitation and inhibition by the statistics of visual input
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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 …
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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 … …
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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 …
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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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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 …
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Modeling of spikes, sparseness and adaptation in the primary visual cortex: applications to imaging
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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 …
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What efficient code for adaptive spiking representations?
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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 …
Last updated on 2021-10-12
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