Computational Neuroscience

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
Feature detection using spikes : the greedy approach
A goal of low-level neural processes is to build an efficient code extracting the relevant information from the sensory input. It is …
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Feature detection using spikes : the greedy approach
Sparse spike coding in an asynchronous feed-forward multi-layer neural network using matching pursuit
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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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Emergence of filters from natural scenes in a sparse spike coding scheme
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Emergence of filters from natural scenes in a sparse spike coding scheme
Coherence detection in a spiking neuron via Hebbian learning
It is generally assumed that neurons in the central nervous system communicate through temporal firing patterns. As a first step, we …
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Coherence detection in a spiking neuron via Hebbian learning
Sparse Image Coding Using an Asynchronous Spiking Neural Network
Progressive reconstruction of a static image using spikes in a Laplacian pyramid.
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Visual Strategies for Sparse Spike Coding
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Visual Strategies for Sparse Spike Coding
Network of integrate-and-fire neurons using Rank Order Coding A: how to implement spike timing dependant plasticity
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A generative model for Spike Time Dependent Hebbian Plasticity
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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