sparse coding

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 unsupervised learning process: the emergence of this architecture is mostly self-organized. In the primary visual …

Meaningful representations emerge from Sparse Deep Predictive Coding

The formation of connections between neural cells is essentially emerging from an unsupervised learning process. During the development of primary visual cortex (V1) of mammals, for example, one may observe the emergence of cells selective to …

Top-down feedback in Hierarchical Sparse Coding

From a computer science perspective, the problem of optimal representation using Hierarchical Sparse Coding (HSC) is often solved using a stack of independent subproblems with for instance the Lasso formulation at each layer. However, recent …

Unsupervised learning applied to robotic vision

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 for the representation of any image as a combination of few elements drawn from a large dictionary of basis …

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 for the representation of any image as a combination of few elements drawn from a large dictionary of basis …

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 this structure is that images can be described by a relatively sparse number of features. We designed a sparse …

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 the primary visual cortex of primates--- only a few neurons are active at a given time with respect to the whole …

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 more common than other possible arrangements of edges (the good continuation law of Gestalt psychology). The visual …

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 high-level associative brain areas. Previous explanations require progressively analyzing the scene hierarchically …

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 involve higher-level associative brain areas. Previous proposals have been based on a series of processing steps …

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 Chronic Obstructive Pulmonary Disease (COPD), which is commonly presented as emphysema. Since low-attenuation areas in …

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 more common than other possible arrangements of edges (Geisler et al., Vis Res 41:711-24, 2001). The visual system …

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

Oriented edges in images of natural scenes tend to be aligned in collinear or co-circular arrangements, with lines and smooth curves more common than other possible arrangements of edges (Geisler et al., Vis Res 41:711-24, 2001). The visual system …

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 emergence of this response is to state that neural activity has to efficiently represent sensory data with respect …

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 balance each other, with inhibition lagging excitation by only few milliseconds. To characterize the functional …

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, they can't beat the efficiency of biological vision for such simple tasks as recognizing a relative or following an …

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 response (OFR), is confronted to uncertainties which are efficiently resolved in the primate's visual system. We may …

Sparse Gabor wavelets by local operations

Efficient sparse coding of overcomplete transforms remains still anopen problem. Different methods have been proposed in theliterature, but most of them are limited by a heavy computationalcost and by difficulties to find the optimal solutions. We …

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 visual processing for static images. We will first present the retinal model which was introduced by Van Rullen and …

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 believed that this is implemented in cortical areas by elementary inferential computations dynamically extracting the …

Sparse spike coding in an asynchronous feed-forward multi-layer neural network using matching pursuit

Comment déchiffrer le code impulsionnel de la vision ? Étude du flux parallèle, asynchrone et épars dans le traitement visuel ultra-rapide

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.