Computational Neuroscience

Biologically Inspired Dynamic Textures for Probing Motion Perception
Talk @ NeurIPS: https://neurips.cc/Conferences/2015/Schedule?showEvent=5418 See a followup in Jonathan Vacher, Andrew Isaac Meso, …
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Biologically Inspired Dynamic Textures for Probing Motion Perception
Eye tracking a self-moved target with complex hand-target dynamics
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Spatiotemporal tuning of retinal ganglion cells dependent on the context of signal presentation
Follow-up paper Cesar U Ravello, Laurent U Perrinet, Maria-José Escobar, Adrián G Palacios (2019). Speed-Selectivity in Retinal …
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Active inference, eye movements and oculomotor delays
This paper considers the problem of sensorimotor delays in the optimal control of (smooth) eye movements under uncertainty. …
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Active inference, eye movements and oculomotor delays
Une Approche Computationnelle de La Dépendance Au Mouvement Du Codage de La Position Dans La Système Visuel
Cette thèse est centralisée sur cette question : comment est-ce que le système visuel peut coder efficacement la position des objets en …
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Beyond simply faster and slower: exploring paradoxes in speed perception
Estimating object speed in visual scenes is a critical part of perception. While various aspects of speed computation including …
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The characteristics of microsaccadic eye movements varied with the change of strategy in a match-to-sample task
Under natural viewing conditions, large eye movements are interspace by small eye movements (microsaccade). Recent works have shown …
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Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network
As it is confronted to inherent neural delays, how does the visual system create a coherent representation of a rapidly changing …
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Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network
see Kaplan and al, 2014
2014-04-25 Computational Neuroscience, NeuroAI & Machine Learning
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Codage prédictif dans les transformations visuo-motrices
Laurent U Perrinet (2014). 2014-04-17: Soutenance d'habilitation à diriger des recherches (HDR). Project Voir une extension dans …
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Axonal delays and on-time control of eye movements
Moving objects generate sensory information that may be noisy and ambiguous, yet it is important to be able to reconstruct object speed …
2014-01-10 Behavioural Neuroscience, Biological Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning
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A Simple Model of Orientation Encoding Accounting For Multivariate Neural Noise
see a follow-up in this publication
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A Simple Model of Orientation Encoding Accounting For Multivariate Neural Noise
see a follow-up in this publication
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Dynamic Textures For Probing Motion Perception
This work extends the MotionClouds dynamic texture model testing aspects of its parametrization with an application in psychophysics.
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On the nature of anticipatory eye movements and the factors affecting them
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Relationship between natural image statistics and lateral connectivity in the primary visual cortex
see a follow-up: Laurent U Perrinet, James A Bednar (2015). Edge co-occurrences can account for rapid categorization of natural versus …
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Demo 1, Task4: Implementation of models showing emergence of cortical fields and maps
Together with Bernhard Kaplan, we talked about how we aim at “compiling” a predictive motion-based approach as a spiking …
2013-11-26 Computational Neuroscience, NeuroAI & Machine Learning
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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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Edge co-occurrences and categorizing natural images
See a followup in Edge co-occurrences can account for rapid categorization of natural versus animal images Laurent U Perrinet, James A …
2013-07-05 Computational Neuroscience, NeuroAI & Machine Learning
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Why methods and tools are the key to artificial brain-like systems
This session aims at presenting new ideas that emerged during the first years of BrainScaleS. Indeed, the collaborations that were …
2013-03-21 Behavioural Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning
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Active inference, eye movements and oculomotor delays
We consider the problem of sensorimotor delays in the optimal control of movement under uncertainty. Specifically, we consider axonal …
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Active inference, eye movements and oculomotor delays
We consider the problem of sensorimotor delays in the optimal control of movement under uncertainty. Specifically, we consider axonal …
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Motion-based prediction and development of the response to an 'on the way' stimulus
Based on Laurent U Perrinet, Guillaume S Masson (2012). Motion-based prediction is sufficient to solve the aperture problem. Neural …
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Smooth Pursuit and Visual Occlusion: Active Inference and Oculomotor Control in Schizophrenia
This paper introduces a model of oculomotor control during the smooth pursuit of occluded visual targets. This model is based upon …
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Pattern discrimination for moving random textures: Richer stimuli are more difficult to recognize
In order to analyze the characteristics of a rich dynamic visual environment, the visual system must integrate information collected at …
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Pattern discrimination for moving random textures: Richer stimuli are more difficult to recognize
Apparent motion in V1 - Probabilistic approaches
2012-03-23 Computational Neuroscience, NeuroAI & Machine Learning
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MotionClouds: Model-based stimulus synthesis of natural-like random textures for the study of motion perception
2012-03-22 Computational Neuroscience, NeuroAI & Machine Learning
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Grabbing, tracking and sniffing as models for motion detection and eye movements
Moving objects generate sensory information that may be noisy and ambiguous, yet it is important to be able to reconstruct object speed …
2012-01-27 Behavioural Neuroscience, Biological Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning
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Motion-based prediction is sufficient to solve the aperture problem
In low-level sensory systems, it is still unclear how the noisy information collected locally by neurons may give rise to a coherent …
2012-01-12 Computational Neuroscience, NeuroAI & Machine Learning
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Active inference, smooth pursuit and oculomotor delays
We consider the problem of sensorimotor delays in the optimal control of movement under uncertainty. Specifically, we consider axonal …
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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
Motion-based prediction is sufficient to solve the aperture problem
In low-level sensory systems, it is still unclear how the noisy information collected locally by neurons may give rise to a coherent …
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Motion-based prediction is sufficient to solve the aperture problem
In low-level sensory systems, it is still unclear how the noisy information collected locally by neurons may give rise to a coherent …
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Motion-based prediction is sufficient to solve the aperture problem
Perceptions as Hypotheses: Saccades as Experiments
If perception corresponds to hypothesis testing (Gregory, 1980); then visual searches might be construed as experiments that generate …
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Perceptions as Hypotheses: Saccades as Experiments
Role of motion-based prediction in motion extrapolation
Based on Laurent U Perrinet, Guillaume S Masson (2012). Motion-based prediction is sufficient to solve the aperture problem. Neural …
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Demo 1, Task4: Implementation of models showing emergence of cortical fields and maps
2011-10-05 Computational Neuroscience, NeuroAI & Machine Learning
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Pattern discrimination for moving random textures: Richer stimuli are more difficult to recognize
In order to analyze the characteristics of a rich dynamic visual environment, the visual system must integrate information collected at …
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Propriétés émergentes d'un modèle de prédiction probabiliste utilisant un champ neural
Sensory informations such as visual images are inherently variable. We use probabilistic models to describe how the low-level visual …
2011-07-02 Computational Neuroscience, NeuroAI & Machine Learning
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Qui créera le premier ordinateur intelligent?
Qui créera le premier ordinateur intelligent? Les ordinateurs classiques sont de plus en plus puissants, mais restent toujours aussi « …
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Qui créera le premier ordinateur intelligent?
Pursuing motion illusions: a realistic oculomotor framework for Bayesian inference
Accuracy in estimating an object’s global motion over time is not only affected by the noise in visual motion information but …
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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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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 …
2010-12-17 Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
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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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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 …
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Role of homeostasis in learning sparse representations
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
2010-05-27 : Neurocomp08
La deuxième conférence française de Neurosciences Computationnelles, Neurocomp08, s’est déroulée à la Faculté de Médecine de Marseille du 8 au 11 octobre 2008.
2010-05-27 2 min read Computational Neuroscience, NeuroAI & Machine Learning
Project Project
2010-05-27 : Neurocomp08
Diffraction monochromatique, spectre audiographique
En perception, les neurones « parlent » tous en même temps par de brèves impulsions électrochimiques, générant un mélange de signaux, …
2010-04-14 Computational Neuroscience, NeuroAI & Machine Learning
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Diffraction monochromatique, spectre audiographique
Models of low-level vision: linking probabilistic models and neural masses
see this more recent talk @ UCL, London
2010-01-08 Computational Neuroscience
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A recurrent Bayesian model of dynamic motion integration for smooth pursuit
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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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Different pooling of motion information for perceptual speed discrimination and behavioral speed estimation
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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
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 …
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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 Computational Neuroscience
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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 Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
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Correlating Excitation and Inhibition in Visual Cortical Circuits : Functional Consequences and Biological Feasibility
The primary visual cortex (V1) is one of the most studied cortical area in the brain. Together with the retina and the lateral …
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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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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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Computational Neuroscience: From Representations to Behavior
Computational Neuroscience: From Representations to Behavior, the Second NeuroComp Marseille Workshop.
2008-10-08 8 min read Behavioural Neuroscience, Biological Neuroscience, Computational Neuroscience, Computer Vision, NeuroAI & Machine Learning, Theoretical Neuroscience, Visual Neuroscience
Project
Computational Neuroscience: From Representations to Behavior
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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Proceedings of the second french conference on Computational Neuroscience, Marseille
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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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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 Behavioural Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning, Visual 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
Modeling of spikes, sparseness and adaptation in the primary visual cortex: applications to imaging
related publications @ FENS 2006, @ NeuroComp 2008 and @ AREADNE 2008
2008-02-01 Computational Neuroscience, NeuroAI & Machine Learning, Visual Neuroscience
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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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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 …
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Decoding the population dynamics underlying ocular following response using a probabilistic framework
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
What efficient code for adaptive spiking representations?
2007-12-01 Computational Neuroscience, NeuroAI & Machine Learning
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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 …
2007-09-01 Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience, Visual Neuroscience
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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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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 …
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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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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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Modeling spatial integration in the ocular following response using a probabilistic framework
The machinery behind the visual perception of motion and the subsequent sensori-motor transformation, such as in Ocular Following …
PDF Cite DOI URL
Modeling spatial integration in the ocular following response using a probabilistic framework
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 …
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Neural Codes for Adaptive Sparse Representations of Natural Images
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 …
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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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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 …
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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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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 …
2006-01-01 Behavioural Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
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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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