Biological Neuroscience

An open-source vision-science tool for the auto-regressive generation of dynamic stochastic textures Motion Clouds
Motion Clouds are a generative model for naturalistic visual stimulation that offer full parametric control and more naturalism than …
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Cortical recurrence supports resilience to sensory variance in the primary visual cortex
Our daily endeavors occur in a complex visual environment, whose intrinsic variability challenges the way we integrate information to …
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Cortical recurrence supports resilience to sensory variance in the primary visual cortex
Ultra-rapid visual search in natural images using active deep learning

Ultra-rapid visual search in natural images using active deep learning
A resilient neural code in V1 to process natural images
On a daily basis, the primary visual cortex (V1) detects oriented elements from sensory inputs made of orientation distributions. To …
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A resilient neural code in V1 to process natural images
Retinotopic mapping improves the reliability of image classification
Last updated on 2022-05-20 Biological Neuroscience
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Revisiting Horizontal Connectivity Rules in V1: From like-to-like towards like-to-All
Horizontal connections in the primary visual cortex of carnivores, ungulates and primates organize on a near-regular lattice. Given the …
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Anticipatory Responses along Motion Trajectories in Awake Monkey Area V1
What are the neural mechanisms underlying motion integration of translating objects? Visual motion integration is generally conceived …
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A dynamic model for decoding direction and orientation in macaque primary visual cortex
When objects are in motion, the local orientation of their contours and the direction of motion are two essential components of visual …
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Suppressive waves disambiguate the representation of long-range apparent motion in awake monkey V1
Traveling waves have recently been observed in different animal species, brain areas and behavioral states. However, it is still unclear what are their functional roles. In the case of cortical visual processing, waves propagate across retinotopic maps and can hereby generate interactions between spatially and temporally separated instances of feedforward driven activity. Such interactions could participate in processing long-range apparent motion stimuli, an illusion for which no clear neuronal mechanisms have yet been proposed. Using this paradigm in awake monkeys, we show that suppressive traveling waves produce to a spatio-temporal normalization of apparent motion stimuli. Our study suggests that cortical waves shape the representation of illusory moving stimulus within retinotopic maps for an straightforward read-out by downstream areas.
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Suppressive waves disambiguate the representation of long-range apparent motion in awake monkey V1
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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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 …
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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 …
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Motion-based prediction explains the role of tracking in motion extrapolation
During normal viewing, the continuous stream of visual input is regularly interrupted, for instance by blinks of the eye. Despite these …
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Anisotropic connectivity implements motion-based prediction in a spiking neural network
Predictive coding hypothesizes that the brain explicitly infers upcoming sensory input to establish a coherent representation of the …
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Phase space analysis of networks based on biologically realistic parameters
We study cortical network dynamics for a spatially embedded network model. It represents, in terms of spatial scale, a large piece of …
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Computational Neuroscience, from Multiple Levels to Multi-level
Despite the long and fruitful history of neuroscience, a global, multi-level description of cardinal brain functions is still far from …
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NeuralEnsemble: Towards a meta-environment for network modeling and data analysis
NeuralEnsemble (https://neuralensemble.org) is a multilateral effort to coordinate and organise neuroscience software development …
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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 …
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Sparse Approximation of Images Inspired from the Functional Architecture of the Primary Visual Areas
Sparse Approximation of Images Inspired from the Functional Architecture of the Primary Visual Areas
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 …
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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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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