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NeuralEnsemble: Towards a meta-environment for network modeling and data analysis

NeuralEnsemble (http://neuralensemble.org) is a multilateral effort to coordinate and organise neuroscience software development efforts based around the Python programming language into a larger, meta-simulator software system. To this end, …

Analyzing cortical network dynamics with respect to different connectivity assumptions

Based on Nicole Voges, Laurent U Perrinet (2010). Phase space analysis of networks based on biologically realistic parameters. Journal of Physiology-Paris. PDF Cite DOI see follow-up : Nicole Voges, Laurent U Perrinet (2012).

Functional properties of feed-forward inhibition

see this subsequent paper in the Journal of Computational Neuroscience

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 …

Control of the temporal interplay between excitation and inhibition by the statistics of visual input: a V1 network modelling study

In the primary visual cortex (V1), single cell responses to simple visual stimuli (gratings) are usually dense but with a high trial-by-trial variability. In contrast, when exposed to full field natural scenes, the firing patterns of these neurons …

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

Dynamics of cortical networks based on patchy connectivity patterns

Based on Nicole Voges, Laurent U Perrinet (2010). Phase space analysis of networks based on biologically realistic parameters. Journal of Physiology-Paris. PDF Cite DOI see follow-up : Nicole Voges, Laurent U Perrinet (2012).

Modeling spatial integration in the ocular following response to center-surround stimulation using a probabilistic framework

What adaptive code for efficient spiking representations? A model for the formation of receptive fields of simple cells

Synchrony in thalamic inputs enhances propagation of activity through cortical layers

see this subsequent paper in the Journal of Computational Neuroscience

Dynamic inference for motion tracking

When the visual information about an object's motion differs at the local level, the visuomotor system needs to integrate information across time to solve this ambiguity and converge to the final motion solution. For an oblique line moving …

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 efficiently representing natural images in the primary visual cortex, we derive an event-based adaptive algorithm …

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 efficiency of this algorithm by comparing it to the SparseNet algorithm [1]. As the SparseNet algorithm, it is …

PyNN: towards a universal neural simulator API in Python

Trends in programming language development and adoption point to Python as the high-level systems integration language of choice. Python leverages a vast developer-base external to the neuroscience community, and promises leaps in simulation …

Visual tracking of ambiguous moving objects: A recursive Bayesian model

Perceptual and oculomotor data demonstrate that, when the visual information about an object's motion differs on the local (edge-related) and global levels, the local 1D motion cues dominate initially, whereas 2D information takes progressively over …

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 efficiency of this algorithm by comparing it to the SparseNet algorithm (Olshausen, 1996). As the SparseNet …

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 ambiguity due to the spatial limitation of the visual motion analyzers (aperture problem). Perceptual and oculomotor data …

Contrast sensitivity adaptation in a virtual spiking retina and its adequation with mammalians retinas

Dynamical contrast gain control mechanisms in a layer 2/3 model of the primary visual cortex

Dynamical contrast gain control mechanisms in a layer 2/3 model of the primary visual cortex

Computations in a cortical column are characterized by the dynamical, event-based nature of neuronal signals and are structured by the layered and parallel structure of cortical areas. But they are also characterized by their efficiency in terms of …

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 ambiguity due to the spatial limitation of the visual motion analyzers (aperture problem). Perceptual and oculomotor data …

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 iterative stage of inhibition and facilitation between neighboring coefficients. The linear transform is a complex …

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 …

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 elongated edges and local non-ambiguous 2D motion from features such as corners, end-points or texture elements. …

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 Neuroscience 24 1193-216). Following this argument, we explore models of representation for natural static images as a …

Efficient Source Detection Using Integrate-and-Fire Neurons

Sparse Image Coding Using an Asynchronous Spiking Neural Network

Progressive reconstruction of a static image using spikes in a Laplacian pyramid.

Visual Strategies for Sparse Spike Coding

A generative model for Spike Time Dependent Hebbian Plasticity