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 …
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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. …
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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 …
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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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2010-06-22 : CodeJamNr4
We held a CodeJam 22nd-24th June 2010, in Marseille.
2010-06-22 1 min read Computational Neuroscience, NeuroAI & Machine Learning
2010-06-22 : CodeJamNr4
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 …
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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 …
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Dynamical emergence of a neural solution for motion integration
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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 …
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Phase space analysis of networks based on biologically realistic parameters
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 …
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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, …
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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 …
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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. …
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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 …
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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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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 …
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Functional properties of feed-forward inhibition
see this subsequent paper in the Journal of Computational Neuroscience
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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 Behavioural Neuroscience, Biological Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
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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 …
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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, …
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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 …
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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 …
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Modeling spatial integration in the ocular following response to center-surround stimulation using a probabilistic framework
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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 …
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What adaptive code for efficient spiking representations? A model for the formation of receptive fields of simple cells
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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
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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 …
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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 …
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Self-Invertible 2D Log-Gabor Wavelets
Meanwhile biorthogonal wavelets got a very popular image processing tool, alternative multiresolution transforms have been proposed for …
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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. …
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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). …
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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 …
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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 …
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Contrast sensitivity adaptation in a virtual spiking retina and its adequation with mammalians retinas
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Dynamical contrast gain control mechanisms in a layer 2/3 model of the primary visual cortex
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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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Input-output transformation in the visuo-oculomotor loop: modeling the ocular following response to center-surround stimulation in a probabilistic framework
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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 …
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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 …
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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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Efficient Source Detection Using Integrate-and-Fire Neurons
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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 …
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Feature detection using spikes : the greedy approach