coding decoding

A dynamic model for decoding direction and orientation in macaque primary visual cortex

Natural scenes generally contain objects in motion. The local orientation of their contours and the direction of motion are two essential components of visual information which are processed in parallel in the early visual areas. Focusing on the …

Testing the odds of inherent vs. observed overdispersion in neural spike counts

The repeated presentation of an identical visual stimulus in the receptive field of a neuron may evoke different spiking patterns at each trial. Probabilistic methods are essential to understand the functional role of this variance within the neural …

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 g̈ood continuation lawöf Gestalt psychology). The visual …

On overdispersion in neuronal evoked activity

The repeated presentation of an identical visual stimulus in the receptive field of a neuron may evoke different spiking patterns at each trial. Probabilistic methods are essential to understand its functional role within the neural activity. In that …

A Simple Model of Orientation Encoding Accounting For Multivariate Neural Noise

A Simple Model of Orientation Encoding Accounting For Multivariate Neural Noise

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 …

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 system could describe superposed and ambiguous information. This allows to describe the interactions of neighboring …

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 …

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 …

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 will study the learning of a layer of realistic neurons in the particular case where the relevant messages are formed …