Talks & Courses | Next-generation neural computations

Dans l'intelligence du regard: l'art révèle la diversité de notre vision

Spiking neural nets

Artificial neural networks and machine learning applied to the understanding of biological vision

Recréer des réseaux neuronaux pour améliorer la compréhension de notre cerveau

Al for visual processing and energy-efficient algorithms

Neurosciences and sparsity

Lab Tour for Art - Perception Collaboration

A New Look for Convolutional Deep Networks

Mislocalization by Design: The Flash-Lag Effect as Prediction

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

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

NeuroAI: interactions multiples entre Neurosciences et Intelligence artificielle

Robots aériens agiles bio-mimetiques pour le vol en conditions réelles: Enjeux pour l'IA embarqué

Artificial neural networks and machine learning applied to the understanding of biological vision

Sparse representations

Artificial neural networks applied to the understanding of biological vision

Analyser de larges volumes de données neurobiologiques, vers une approche biomimétique

Event-based vision

Accurate Detection of Spiking Motifs by Learning Heterogeneous Delays of a Spiking Neural Network

Event-based vision

Interactions between machine learning, artificial neural networks and our understanding of biological vision

Artificial neural networks and machine learning applied to the understanding of biological vision

Game theory and brain strategies

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

Des illusions aux hallucinations visuelles: une porte sur la perception

Dynamical processing of orientation precision in the primary visual cortex

Pooling in a predictive model of V1 explains functional and structural diversity across species

Dynamical processing of orientation precision in the primary visual cortex

Understanding natural vision using deep predictive coding

Visual search as active inference

From the retina to action: Understanding visual processing

Des illusions aux hallucinations visuelles: une porte sur la perception

Learning where to look: a foveated visuomotor control model

Should I stay or should I go? Humans adapt to the volatility of visual motion properties, and know about it

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

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

La modélisation biomorphique de la perception visuelle

Intervention fête de la science 2018

Principles and psychophysics of Active Inference in anticipating a dynamic, switching probabilistic bias

Probabilities, Bayes and the Free-energy principle

Estimating and anticipating a dynamic probabilistic bias in visual motion direction

Expériences autour de la perception de la forme en art et science

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

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

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

Biologically-inspired characterization of sparseness in natural images

Categorization of microscopy images using a biologically inspired edge co-occurrences descriptor

Eye movements as a model for active inference

Modelling the dynamics of cognitive processes: from the Bayesian brain to particles

Les illusions visuelles, un révélateur du fonctionnement de notre cerveau

Motion-based prediction with neuromorphic hardware

Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network

WP5 - Demo 1.3 : Spiking model of motion-based prediction

Axonal delays and on-time control of eye movements

Demo 1, Task4: Implementation of models showing emergence of cortical fields and maps

Edge co-occurrences and categorizing natural images

Why methods and tools are the key to artificial brain-like systems

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

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

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

Propriétés émergentes d'un modèle de prédiction probabiliste utilisant un champ neural