Sparse Coding

Pooling in a predictive model of V1 explains functional and structural diversity across species
Neurons in the primary visual cortex are selective to orientation with various degrees of selectivity to the spatial phase, from high …
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Pooling in a predictive model of V1 explains functional and structural diversity across species
Sparse Deep Predictive Coding captures contour integration capabilities of the early visual system
Both neurophysiological and psychophysical experiments have pointed out the crucial role of recurrent and feedback connections to …
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Sparse Deep Predictive Coding captures contour integration capabilities of the early visual system
Modelling Complex-cells and topological structure in the visual cortex of mammals using Sparse Predictive Coding
Cells in the primary visual cortex of mammals (V1) have historically been divided into two classes: simple and complex. Simple cells …
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Modelling Complex-cells and topological structure in the visual cortex of mammals using Sparse Predictive Coding
Cells in the primary visual cortex of mammals (V1) have historically been divided into two classes: simple and complex. Simple cells …
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Effect of top-down connections in Hierarchical Sparse Coding
Hierarchical Sparse Coding (HSC) is a powerful model to efficiently represent multi-dimensional, structured data such as images. The …
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Effect of top-down connections in Hierarchical Sparse Coding
Sparse Deep Predictive Coding captures contour integration capabilities of the early visual system
Both neurophysiological and psychophysical experiments have pointed out the crucial role of recurrent and feedback connections to …
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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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Modelling Complex Cells of Early Visual Cortex using Predictive Coding
Sparse Deep Predictive Coding to model visual object recognition
Sparse Deep Predictive Coding to model visual object recognition
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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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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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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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
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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Efficient learning of sparse image representations using homeostatic regulation
One core advantage of sparse representations is the efficient coding of complex signals using compact codes. For instance, it allows …
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Efficient learning of sparse image representations using homeostatic regulation
One core advantage of sparse representations is the efficient coding of complex signals using compact codes. For instance, it allows …
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Differential response of the retinal neural code with respect to the sparseness of natural images

Sparse coding of images in the retina follows regular statistics at the global, not the local scale
Sparse coding of images in the retina follows regular statistics at the global, not the local scale
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Differential response of the retinal neural code with respect to the sparseness of natural images
Biologically-inspired characterization of sparseness in natural images
Natural images follow statistics inherited by the structure of our physical (visual) environment. In particular, a prominent facet of …
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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
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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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
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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Advances in Texture Analysis for Emphysema Classification
In recent years, with the advent of High-resolution Computed Tomography (HRCT), there has been an increased interest for diagnosing …
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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 …
2012-05-10
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Demo 1, Task4: Implementation of models showing emergence of cortical fields and maps
2011-10-05
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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 …
2011-09-28
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Role of homeostasis in learning sparse representations
Neurons in the input layer of primary visual cortex in primates develop edge-like receptive fields. One approach to understanding the …
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Role of homeostasis in learning sparse representations
Functional consequences of correlated excitatory and inhibitory conductances in cortical networks
Neurons in the neocortex receive a large number of excitatory and inhibitory synaptic inputs. Excitation and inhibition dynamically …
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Functional consequences of correlated excitatory and inhibitory conductances in cortical networks
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
What adaptive code for efficient spiking representations? A model for the formation of receptive fields of simple cells
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What adaptive code for efficient spiking representations? A model for the formation of receptive fields of simple cells
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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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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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?
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, absent de la photo), Manuel Samuelides (Directeur de thèse) et Simon Thorpe (Co-directeur de thèse).
Le jury était consistué (de gauche à droite) de Jeanny Hérault (Rapporteur), Michel Imbert (Président), Yves Burnod (Rapporteur, absent de la photo), Manuel Samuelides (Directeur de thèse) et Simon Thorpe (Co-directeur de thèse).
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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
Sparse Image Coding Using an Asynchronous Spiking Neural Network
*Progressive reconstruction of a static image using spikes in a Laplacian pyramid.*
Progressive reconstruction of a static image using spikes in a Laplacian pyramid.
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