NeuroAI & Machine Learning

Efficient coding of visual information in neural computations
2019-01-16 Computational Neuroscience, NeuroAI & Machine Learning
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Modelling spiking neural networks using Brian, Nest and pyNN
2019-01-14 Computational Neuroscience, NeuroAI & Machine Learning
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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 …
2019-01-10 Computational Neuroscience, NeuroAI & Machine Learning
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A hierarchical, multi-layer convolutional sparse coding algorithm based on predictive coding
Sparse coding holds the idea that signals can be concisely described as a linear mixture of few components (called atoms) picked from a …
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An adaptive homeostatic algorithm for the unsupervised learning of visual features
The formation of structure in the visual system, that is, of the connections between cells within neural populations, is by large an …
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Illusions et 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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Modelling Complex Cells of Early Visual Cortex using Predictive Coding
see a follow-up in: Victor Boutin, Angelo Franciosini, Frédéric Chavane, Franck Ruffier, Laurent U Perrinet (2021). Sparse Deep …
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Orientation selectivity to synthetic natural patterns in a cortical-like model of the cat primary visual cortex
A key property of the neurons in the primary visual cortex (V1) is their selectivity to oriented stimuli in the visual field. …
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Sparse Deep Predictive Coding to model visual object recognition
see a follow-up in: Victor Boutin, Angelo Franciosini, Frédéric Chavane, Franck Ruffier, Laurent U Perrinet (2021). Sparse Deep …
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Sparse Deep Predictive Coding to model visual object recognition
Temps et cerveau : comment notre perception nous fait voyager dans le temps
Lorsque nous observons un sablier, lorsque nous fixons notre regard sur les grains de sable qui tombent, nous avons le sentiment que le …
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Temps et cerveau : comment notre perception nous fait voyager dans le temps
Top-down connection in Hierarchical Sparse Coding
The brain has to solve inverse problems to correctly interpret sensory data and infer the set of causes that generated the sensory …
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Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures
A common practice to account for psychophysical biases in vision is to frame them as consequences of a dynamic process relying on …
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Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures
La modélisation biomorphique de la perception visuelle
in “La modélisation de la genèse physico-mathématique du vivant” BIOMORPHISME ET CREATION ARTISTIQUE – Session 3 Date 11 …
2018-10-11 Behavioural Neuroscience, NeuroAI & Machine Learning
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Reinforcement effects in anticipatory smooth eye movements
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Smooth Pursuit Eye Movements and Learning : Role of Motion Probability and Reinforcement Contingencies
Un défi majeur pour les organismes vivants est leur capacité d’adapter constamment leurs comportements moteurs. Dans la première étude …
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Principles and psychophysics of Active Inference in anticipating a dynamic, switching probabilistic bias
See the final publication @ Humans adapt their anticipatory eye movements to the volatility of visual motion properties Chloé …
2018-04-05 Computational Neuroscience, NeuroAI & Machine Learning
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2018-04-05 : *Probabilities and Optimal Inference to understand the Brain* Workshop
We organize a Symposium at NeuroFrance 2019 entitled Active Inference: Bridging theoretical and experimental neurosciences. This is part of a series of theoretical neuroscience symposia organized in this international conference from the french Neursocience Society.
2018-04-05 1 min read Behavioural Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
2018-04-05 : *Probabilities and Optimal Inference to understand the Brain* Workshop
2018-03-26 : PhD Program: course in Computational Neuroscience
PhD Program: course in Computational Neuroscience.
2018-03-26 4 min read Behavioural Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
Project Project
Estimating and anticipating a dynamic probabilistic bias in visual motion direction
See the final publication @ Humans adapt their anticipatory eye movements to the volatility of visual motion properties Chloé …
2018-02-01 Computational Neuroscience, NeuroAI & Machine Learning
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Expériences autour de la perception de la forme en art et science
Elasticité dynamique est composée des pièces Expansion, Trame et Lignes sonores. Volume hexagonal en miroir de 7 mètres de diamètre, …
2018-01-25 Computational Neuroscience, NeuroAI & Machine Learning
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A low-cost, accessible eye tracking framework
Recording eye movements is a technique that attracts an increasing number of scientists, but also in the general public. Indeed, this …
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Estimating and anticipating a dynamic probabilistic bias in visual motion direction
Humans are able to accurately track a moving object with a combination of saccades and smooth eye movements. These movements allow …
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Estimating and anticipating a dynamic probabilistic bias in visual motion direction
Humans are able to accurately track a moving object with a combination of saccades and smooth eye movements. These movements allow …
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From biological vision to unsupervised hierarchical sparse coding
The formation of connections between neural cells is essentially emerging from an unsupervised learning process. During the development …
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M2APix: a bio-inspired auto-adaptive visual sensor for robust ground height estimation
This paper presents for the first time the embedded stand-alone version of the bio-inspired M2APix (Michaelis-Menten auto-adaptive …
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On the Origins of Hierarchy in Visual Processing
It is widely assumed that visual processing follows a forward sequence of processing steps along a hierarchy of laminar sub-populations …
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Selectivity to oriented patterns of different precisions
The selectivity of the visual system to oriented patterns is very well documented in a wide range of species, especially in mammals. In …
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Selectivity to oriented patterns of different precisions
Speed uncertainty and motion perception with naturalistic random textures
It is still not fully understood how visual system integrates motion energy across different spatial and temporal frequencies to build …
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Unsupervised Hierarchical Sparse Coding algorithm inspired by Biological Vision
The brain has to solve inverse problems to correctly interpret sensory data and infer the set of causes that generated the sensory …
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Unsupervised learning applied to robotic vision
see a related work describing SDPC in: Sparse Deep Predictive Coding captures contour integration capabilities of the early visual …
2017-11-24 Computational Neuroscience, NeuroAI & Machine Learning
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Participation au jury
FESTIVAL INTERFÉRENCES​ Cinéma Documentaire et Débat Public FESTIVAL INTERFÉRENCES​ Le collectif Scènes Publiques composé de citoyens, …
2017-11-17 Computational Neuroscience, NeuroAI & Machine Learning
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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 …
2017-11-15 Computational Neuroscience, Computer Vision, NeuroAI & Machine Learning
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Back to the present: dealing with delays in biological and neuromorphic systems
2017-06-28 Computational Neuroscience, NeuroAI & Machine Learning
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Tutorial on predictive coding
2017-06-28 Computational Neuroscience, NeuroAI & Machine Learning
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The flash-lag effect as a motion-based predictive shift
Due to its inherent neural delays, the visual system has an outdated access to sensory information about the current position of moving …
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The flash-lag effect as a motion-based predictive shift
Tutorial: Active inference for eye movements: Bayesian methods, neural inference, dynamics
2017-01-20 Computational Neuroscience, NeuroAI & Machine Learning
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Tutorial: Sparse optimization in neural computations
2017-01-19 Computational Neuroscience, NeuroAI & Machine Learning
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Back to the present: how neurons deal with delays
2017-01-18 Computational Neuroscience, NeuroAI & Machine Learning
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Controlling an aerial robot with human gestures using bio-inspired algorithm
Improve performances of existing recognition computer vision algorithms with biological concepts. The gain are expected in the …
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How the dynamics of human smooth pursuit is influenced by speed uncertainty
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Participation au jury et entretien avec Clara Delmon
RENCONTRES INTERNATIONALES SCIENCES & CINÉMAS cinéma les Variétés …
2016-11-20 Computational Neuroscience, NeuroAI & Machine Learning
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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 …
2016-11-03 Behavioural Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning
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2016-10-26 : EUVIP BICV
EUVIP Session 7: Biologically Inspired Computer Vision (Special Session).
2016-10-26 2 min read Behavioural Neuroscience, Computational Neuroscience, Computer Vision, NeuroAI & Machine Learning
2016-10-26 : EUVIP BICV
Eye movements as a model for active inference
See the final publication @ Humans adapt their anticipatory eye movements to the volatility of visual motion properties Chloé …
2016-10-13 Computational Neuroscience, NeuroAI & Machine Learning
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Operant reinforcement versus reward expectancy: effects on anticipatory eye movements
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Modelling the dynamics of cognitive processes: from the Bayesian brain to particles
See a followup in Perrinet et al, 2012
2016-07-07 NeuroAI & Machine Learning, Theoretical Neuroscience
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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 …
2016-04-28 Behavioural Neuroscience, NeuroAI & Machine Learning
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Les illusions visuelles, un révélateur du fonctionnement de notre cerveau
ANR CausaL (2018/2020)
ANR CausaL (2018/2020) : Cognitive​ ​architectures​ ​of​ Causal​ ​Learning.
Behavioural Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning
ANR CausaL (2018/2020)
ANR PredictEye (2018/2020)
ANR PredictEye (2018/2020) : Mapping and predicting trajectories for eye movements
Biological Neuroscience, NeuroAI & Machine Learning
ANR PredictEye (2018/2020)
DOC2AMU (2016/2019)
DOC2AMU: An Excellence Fellowship (2016/2019).
NeuroAI & Machine Learning
DOC2AMU (2016/2019)
PhD ICN (2017 / 2021)
A grant from the Ph.D. program in Integrative and Clinical Neuroscience (PhD position, 2017 / 2021).
NeuroAI & Machine Learning
PhD ICN (2017 / 2021)
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 …
2016-04-25 Behavioural Neuroscience, NeuroAI & Machine Learning
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Instabilité (series) @ Art-O-Rama
Installation (sérigraphie, dessin mural, lumière), 2018; Guest artist, Mécènes du Sud / Art-O-Rama (Fair), Marseille I 2018.
Last updated on 2018-01-13 1 min read NeuroAI & Machine Learning
Project
Instabilité (series) @ Art-O-Rama
Sans gravité – une poétique de l’air
Sans gravité – une poétique de l’air – Ardenome à Avignon.
Last updated on 2018-01-13 3 min read NeuroAI & Machine Learning
Project
Sans gravité – une poétique de l’air
Turbulences
L’installation Turbulences explore l’émergence de caustiques, phénomènes caractéristiques de la relation entre l’eau, la lumière et l’air. Le mouvement y perturbe un état optique stable. La turbulence des plis lumineux donne l’illusion d’un corps flottant.
Last updated on 2018-01-13 1 min read NeuroAI & Machine Learning
Project
Turbulences
Testing the odds of inherent vs. observed overdispersion in neural spike counts
The repeated presentation of an identical visual stimulus in the receptive field of a neuron may evoke different spiking patterns at …
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A dynamic model for decoding direction and orientation in macaque primary visual cortex
Natural scenes generally contain objects in motion. The local orientation of their contours and the direction of motion are two …
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A dynamic model for decoding direction and orientation in macaque primary visual cortex
Natural scenes generally contain objects in motion. The local orientation of their contours and the direction of motion are two …
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Compensation of oculomotor delays in the visual system's network
We consider the problem of sensorimotor delays in the optimal control of movement under uncertainty. Specifically, we consider axonal …
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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 …
2015-11-05 Behavioural Neuroscience, NeuroAI & Machine Learning
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Introduction
This is the introductory chapter of the book, which serves as a comprehensive but rigorous reference in the area of biologically …
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Sparse Models for Computer Vision
The representation of images in the brain is known to be sparse. That is, as neural activity is recorded in a visual area, for instance …
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Sparse Models for Computer Vision
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 …
2015-10-07 Behavioural Neuroscience, NeuroAI & Machine Learning
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Motion-based prediction with neuromorphic hardware
Anticipatory smooth eye movements and reinforcement
When an object is moving in the visual field, we are able to accurately track it with a combination of saccades and smooth eye …
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Sparse Coding Of Natural Images Using A Prior On Edge Co-Occurences
Oriented edges in images of natural scenes tend to be aligned in co-linear or co-circular arrangements, with lines and smooth curves …
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BrainScaleS (2011/2014)
BrainScaleS: Brain-inspired multiscale computation in neuromorphic hybrid systems (2011/2014).
Computational Neuroscience, NeuroAI & Machine Learning
BrainScaleS (2011/2014)
CODDE (2008/2012)
CODDE: understanding brain and behaviour (2008/2012).
Behavioural Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning
CODDE (2008/2012)
PACE-ITN (2015/2019)
PACE-ITN: ITN Marie Curie network (2015/2019).
Behavioural Neuroscience, NeuroAI & Machine Learning
PACE-ITN (2015/2019)
Anticipating a moving target: role of vision and reinforcement
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Anticipatory smooth eye movements as operant behavior
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Edge co-occurrences can account for rapid categorization of natural versus animal images
Making a judgment about the semantic category of a visual scene, such as whether it contains an animal, is typically assumed to involve …
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Edge co-occurrences can account for rapid categorization of natural versus animal images
Eye tracking a self-moved target with complex hand-target dynamics
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On overdispersion in neuronal evoked activity
The repeated presentation of an identical visual stimulus in the receptive field of a neuron may evoke different spiking patterns at …
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Active inference, eye movements and oculomotor delays
This paper considers the problem of sensorimotor delays in the optimal control of (smooth) eye movements under uncertainty. …
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Active inference, eye movements and oculomotor delays
Une Approche Computationnelle de La Dépendance Au Mouvement Du Codage de La Position Dans La Système Visuel
Cette thèse est centralisée sur cette question : comment est-ce que le système visuel peut coder efficacement la position des objets en …
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Beyond simply faster and slower: exploring paradoxes in speed perception
Estimating object speed in visual scenes is a critical part of perception. While various aspects of speed computation including …
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Edge co-occurrences are sufficient to categorize natural versus animal images
Analysis and interpretation of a visual scene to extract its category, such as whether it contains an animal, is typically assumed to …
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Motion-based prediction model for flash lag effect
The flash lag effect (FLE) is a well known visual illusion that reveals the perceptual difference in position coding of moving and …
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The characteristics of microsaccadic eye movements varied with the change of strategy in a match-to-sample task
Under natural viewing conditions, large eye movements are interspace by small eye movements (microsaccade). Recent works have shown …
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Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network
As it is confronted to inherent neural delays, how does the visual system create a coherent representation of a rapidly changing …
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Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network
see Kaplan and al, 2014
2014-04-25 Computational Neuroscience, NeuroAI & Machine Learning
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2014-04-17: Soutenance d'habilitation à diriger des recherches (HDR)
J’ai soutenu mon habilitation à diriger des recherche (HDR) le 17 avril 2014, celle-ci ayant pour titre: Codage prédictif dans les transformations visuo-motrices
Last updated on 2014-04-17 1 min read NeuroAI & Machine Learning
Project
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 …
2014-03-20 Behavioural Neuroscience, Biological Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience, Visual Neuroscience
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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 …
2014-01-10 Behavioural Neuroscience, Biological Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning
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A Simple Model of Orientation Encoding Accounting For Multivariate Neural Noise
see a follow-up in this publication
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A Simple Model of Orientation Encoding Accounting For Multivariate Neural Noise
see a follow-up in this publication
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On the nature of anticipatory eye movements and the factors affecting them
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Demo 1, Task4: Implementation of models showing emergence of cortical fields and maps
Together with Bernhard Kaplan, we talked about how we aim at “compiling” a predictive motion-based approach as a spiking …
2013-11-26 Computational Neuroscience, NeuroAI & Machine Learning
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Motion-based prediction explains the role of tracking in motion extrapolation
During normal viewing, the continuous stream of visual input is regularly interrupted, for instance by blinks of the eye. Despite these …
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Anisotropic connectivity implements motion-based prediction in a spiking neural network
Predictive coding hypothesizes that the brain explicitly infers upcoming sensory input to establish a coherent representation of the …
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Edge co-occurrences and categorizing natural images
See a followup in Edge co-occurrences can account for rapid categorization of natural versus animal images Laurent U Perrinet, James A …
2013-07-05 Computational Neuroscience, NeuroAI & Machine Learning
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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 …
2013-03-21 Behavioural Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning
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Active inference, eye movements 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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Active inference, eye movements 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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How and why do image frequency properties influence perceived speed?
Humans are able to interact successfully with moving objects in our dynamic world and the visual system effi ciently performs the …
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Measuring speed of moving textures: Different pooling of motion information for human ocular following and perception
The visual system does not process information instantaneously, but rather integrates over time. Integration occurs both for stationary …
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Smooth Pursuit and Visual Occlusion: Active Inference and Oculomotor Control in Schizophrenia
This paper introduces a model of oculomotor control during the smooth pursuit of occluded visual targets. This model is based upon …
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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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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
Oriented edges in images of natural scenes tend to be aligned in collinear or co-circular arrangements, with lines and smooth curves …
2012-05-10 Behavioural Neuroscience, NeuroAI & Machine Learning, Visual Neuroscience
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Apparent motion in V1 - Probabilistic approaches
2012-03-23 Computational Neuroscience, NeuroAI & Machine Learning
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