Theoretical Neuroscience

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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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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Computational Neuroscience, from Multiple Levels to Multi-level
Despite the long and fruitful history of neuroscience, a global, multi-level description of cardinal brain functions is still far from …
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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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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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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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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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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
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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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Adaptive Sparse Spike Coding : applications of Neuroscience to the compression of natural images
If modern computers are sometimes superior to cognition in some specialized tasks such as playing chess or browsing a large database, …
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Adaptive Sparse Spike Coding : applications of Neuroscience to the compression of natural images
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
Modeling spatial integration in the ocular following response to center-surround stimulation using a probabilistic framework
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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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Introduction to Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision
Dynamical Neural Networks (DyNNs) are a class of models for networks of neurons where particular focus is put on the role of time in …
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Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision
Dynamical Neural Networks (DyNNs) are a class of models for networks of neurons where particular focus is put on the role of time in …
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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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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 …
Cite
Neural Codes for Adaptive Sparse Representations of Natural Images
On efficient sparse spike coding schemes for learning natural scenes 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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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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Input-output transformation in the visuo-oculomotor loop: modeling the ocular following response to center-surround stimulation in a probabilistic framework
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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
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 …
Cite
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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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?
Finding Independent Components using spikes : a natural result of Hebbian learning in a sparse spike coding scheme
To understand possible strategies of temporal spike coding in the central nervous system, we study functional neuromimetic models of …
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Network of integrate-and-fire neurons using Rank Order Coding B: spike timing dependant plasticity and emergence of orientation selectivity
Rank Order Coding is an alternative to conventional rate coding schemes that uses the order in which a neuron’s inputs fire to …
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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