Facets

Push-Pull Receptive Field Organization and Synaptic Depression: Mechanisms for Reliably Encoding Naturalistic Stimuli in V1
Neurons in the primary visual cortex are known for responding vigorously but with high variability to classical stimuli such as …
Cite DOI URL HAL
Push-Pull Receptive Field Organization and Synaptic Depression: Mechanisms for Reliably Encoding Naturalistic Stimuli in V1
Complex dynamics in recurrent cortical networks based on spatially realistic connectivities
Most studies on the dynamics of recurrent cortical networks are either based on purely random wiring or neighborhood couplings. …
PDF Cite DOI URL
Complex dynamics in recurrent cortical networks based on spatially realistic connectivities
Role of motion inertia in dynamic motion integration for smooth pursuit
Based on Laurent U Perrinet, Guillaume S Masson (2012). Motion-based prediction is sufficient to solve the aperture problem. Neural Computation. PDF Cite Pdf arXiv see follow-up on motion extrapolation: Mina A Khoei, Guillaume S Masson, Laurent U Perrinet (2013).
Cite URL
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 …
PDF Cite DOI URL
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 …
PDF Cite DOI URL
Functional consequences of correlated excitatory and inhibitory conductances in cortical networks
Dynamical emergence of a neural solution for motion integration
Based on Laurent U Perrinet, Guillaume S Masson (2012). Motion-based prediction is sufficient to solve the aperture problem. Neural Computation. PDF Cite Pdf arXiv see follow-up on motion extrapolation: Mina A Khoei, Guillaume S Masson, Laurent U Perrinet (2013).
Cite URL
Dynamical emergence of a neural solution for motion integration
Cite
Phase space analysis of networks based on biologically realistic parameters
We study cortical network dynamics for a more realistic network model. It represents, in terms of spatial scale, a large piece of …
Cite
Phase space analysis of networks based on biologically realistic parameters
Reading out the dynamics of lateral interactions in the primary visual cortex from VSD data
Short presentation of a large moving pattern elicits an ocular following response that exhibits many of the properties attributed to …
2009-11-30
Cite URL
Control of the temporal interplay between excitation and inhibition by the statistics of visual input
see this subsequent paper in the Journal of Computational Neuroscience
2009-07-18
Cite DOI
Decoding low-level neural information to track visual motion
Moving the eyes rapidly to track a visual object moving in a cluttered environment is an essential function. However, doing so rapidly …
2009-04-01
Cite URL
Decoding center-surround interactions in population of neurons for the ocular following response
Short presentation of a large moving pattern elicits an Ocular Following Response (OFR) that exhibits many of the properties attributed …
Cite
Dynamical state spaces of cortical networks representing various horizontal connectivities
Most studies of cor tical network dynamics are either based on purely random wiring or neighborhood couplings, e.g., [Kumar, Schrader, …
Cite
Dynamics of cortical networks including long-range patchy connections
Most studies of cortical network dynamics are either based on purely random wiring or neighborhood couplings [1], focussing on a rather …
Cite
Functional consequences of correlated excitation and inhibition on single neuron integration and signal propagation through synfire chains
Neurons receive a large number of excitatory and inhibitory synaptic inputs whose temporal interplay determines their spiking behavior. …
Cite
Inferring monkey ocular following responses from V1 population dynamics using a probabilistic model of motion integration
Short presentation of a large moving pattern elicits an ocular following response that exhibits many of the properties attributed to …
Cite
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 …
Cite Project
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 URL see follow-up : Nicole Voges, Laurent U Perrinet (2012).
Cite
Functional properties of feed-forward inhibition
see this subsequent paper in the Journal of Computational Neuroscience
Cite
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 …
2008-06-01
Cite URL
From neural activity to behavior: computational neuroscience as a synthetic approach for understanding the neural code.
Computational Neuroscience is a synthetic, inter-disciplinary approach aiming at understanding cognition by analyzing the mechanisms …
2008-04-01
Cite URL
Dynamics of distributed 1D and 2D motion representations for short-latency ocular following
Integrating information is essential to measure the physical 2D motion of a surface from both ambiguous local 1D motion of its …
PDF Cite DOI URL
Dynamics of distributed 1D and 2D motion representations for short-latency ocular following
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, …
Cite arXiv
Adaptive Sparse Spike Coding : applications of Neuroscience to the compression of natural images
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 …
Cite
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 URL see follow-up : Nicole Voges, Laurent U Perrinet (2012).
Cite
Modeling spatial integration in the ocular following response to center-surround stimulation using a probabilistic framework
Cite
PyNN: A Common Interface for Neuronal Network Simulators
Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and …
PDF Cite Project DOI URL HAL
What adaptive code for efficient spiking representations? A model for the formation of receptive fields of simple cells
PDF Cite
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
Cite DOI URL
Introduction to Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision
Dynamical Neural Networks (DyNNs) are a class of models for networks of neurons where particular focus is put on the role of time in the emergence of functional computational properties. The definition and study of these models involves the cooperation of a large range of scientific fields from statistical physics, probabilistic modelling, neuroscience and psychology to control theory.
Cite DOI URL
Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision
Dynamical Neural Networks (DyNNs) are a class of models for networks of neurons where particular focus is put on the role of time in the emergence of functional computational properties. The definition and study of these models involves the cooperation of a large range of scientific fields from statistical physics, probabilistic modelling, neuroscience and psychology to control theory.
Cite
Self-Invertible 2D Log-Gabor Wavelets
Meanwhile biorthogonal wavelets got a very popular image processing tool, alternative multiresolution transforms have been proposed for …
PDF Cite DOI Code URL
Self-Invertible 2D Log-Gabor Wavelets
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. …
Cite Project DOI URL
Sparse Approximation of Images Inspired from the Functional Architecture of the Primary Visual Areas
relies on log-Gabor filters: Sylvain Fischer, Filip Šroubek, Laurent U Perrinet, Rafael Redondo, Gabriel Cristóbal (2007). Self-Invertible 2D Log-Gabor Wavelets. International Journal of Computer Vision. PDF Cite DOI Code URL Schematic structure of the primary visual cortex implemented in the present study.
PDF Cite DOI URL
Sparse Approximation of Images Inspired from the Functional Architecture of the Primary Visual Areas
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 …
Cite PDF
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 …
Cite DOI
Contrast sensitivity adaptation in a virtual spiking retina and its adequation with mammalians retinas
Cite
Dynamical contrast gain control mechanisms in a layer 2/3 model of the primary visual cortex
Cite
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 …
Cite
Input-output transformation in the visuo-oculomotor loop: modeling the ocular following response to center-surround stimulation in a probabilistic framework
Cite
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 …
Cite
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 …
Cite
Sparse Gabor wavelets by local operations
Efficient sparse coding of overcomplete transforms remains still anopen problem. Different methods have been proposed in theliterature, …
Cite DOI URL
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 …
Cite
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 …
Cite
Efficient Source Detection Using Integrate-and-Fire Neurons
Cite DOI URL
Efficient Source Detection Using Integrate-and-Fire Neurons
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 …
Cite DOI URL arXiv
Feature detection using spikes : the greedy approach