Biological Neuroscience

Kevin Mairot
PhD Student (2023-10 / 2027-09): L’intelligence artificielle comme aide au diagnostic des dystrophies rétiniennes Institut des …
2026-08-27 Biological Neuroscience, Clinical Neuroscience, NeuroAI & Machine Learning
Artificial neural networks and machine learning applied to the understanding of biological vision
Visual areas are essential in transforming the raw luminous signal into a representation which efficiently conveys information about …
2026-03-05 Biological Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
Cite Project Slides URL
Foveated Retinotopy Improves Classification and Localization in CNNs
Foveated Retinotopy in CNNs. We represent Left an input image and how it is transformed by foveated retinotopy. We show below a …
PDF Cite DOI URL arXiv
Foveated Retinotopy Improves Classification and Localization in CNNs
Open PhD Position in Computational & Systems Neuroscience : 'Neuromodulatory control of predictive processing in visual cortical circuits'
The position has been filled. We are recruiting a PhD student to work on neuromodulatory control of predictive processing in mouse …
2026-01-03 1 min read Behavioural Neuroscience, Biological Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning
Open PhD Position in Computational & Systems Neuroscience : 'Neuromodulatory control of predictive processing in visual cortical circuits'
A New Look for Convolutional Deep Networks
By construction, deep convolutional networks treat all pixels equally. Result: they are energy-hungry, fragile to rotations or zooms, …
2025-12-12 Behavioural Neuroscience, Biological Neuroscience, Computer Vision, NeuroAI & Machine Learning, Visual Neuroscience
Cite MAIN Slides YouTube
Foveal Retinotopy and Dual Pathways: A Computational Model for Active Visual Search
Abstract This thesis investigates visual search through the lens of the dual visual pathways found in biological systems : the ventral …
Cite
Soutenance de Jean-Nicolas Jérémie "Foveal Retinotopy and Dual Pathways: A Computational Model for Active Visual Search"
Jean-Nicolas Jérémie soutiendra publiquement ses travaux de thèse intitulés: Foveal Retinotopy and Dual Pathways: A Computational Model …
2025-10-10 4 min read Behavioural Neuroscience, Biological Neuroscience, Computational Neuroscience, Computer Vision, NeuroAI & Machine Learning, Theoretical Neuroscience
Soutenance de Jean-Nicolas Jérémie "Foveal Retinotopy and Dual Pathways: A Computational Model for Active Visual Search"
DynTex: A Real-Time Generative Model of Dynamic Naturalistic Luminance Textures
The visual systems of animals work in diverse and constantly changing environments where organism survival requires effective senses. …
Cite Project DOI URL HAL
DynTex: A Real-Time Generative Model of Dynamic Naturalistic Luminance Textures
Artificial neural networks and machine learning applied to the understanding of biological vision
Visual areas are essential in transforming the raw luminous signal into a representation which efficiently conveys information about …
Last updated on 2025-05-26 Biological Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
Cite Project Slides URL
Artificial neural networks and machine learning applied to the understanding of biological vision
How and why foveated retinotopy provides efficient vision
The retinas of many animals, including humans, have a specialized area of high visual acuity called the fovea, while peripheral regions …
2025-01-08 Biological Neuroscience, Computational Neuroscience, Computer Vision, NeuroAI & Machine Learning, Visual Neuroscience
Cite URL
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 …
Cite Pdf URL
Kernel Heterogeneity Improves Sparseness of Natural Images Representations
Both biological and artificial neural networks inherently balance their performance with their operational cost, which balances their …
Cite Dataset DOI URL HAL
Soutenance de thèse Antoine Grimaldi
Antoine Grimaldi (Equipe NeOpTo) a soutenu sa thèse de doctorat intitulée: Neural computations with precise spiking motifs for dynamic vision le Jeudi 16 mai à 15h
Last updated on 2024-05-15 4 min read Behavioural Neuroscience, Biological Neuroscience, NeuroAI & Machine Learning
Soutenance de thèse Antoine Grimaldi
Artificial neural networks and machine learning applied to the understanding of biological vision
Visual areas are essential in transforming the raw luminous signal into a representation which efficiently conveys information about …
Last updated on 2024-05-13 Biological Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
Cite Project Slides URL
ANR MarmoCatch (2023-10/2029-04)
ANR MarmoCatch (2023-10/2029-04)
Biological Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
Retinotopy in CNN's implements Efficient Visual Search
While foveated vision, a trait shared by many animals including humans, is a major contributor to biological visual performance, it has …
PDF Cite URL
Retinotopy in CNN's implements Efficient Visual Search
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 …
PDF Cite DOI Dataset Code URL bioRxiv HAL
Cortical recurrence supports resilience to sensory variance in the primary visual cortex
Convolutional Sparse Coding is improved by heterogeneous uncertainty modeling
Aleatoric uncertainty characterizes the variability of features found in natural images, and echoes the epistemic uncertainty …
PDF Cite URL
Artificial neural networks and machine learning applied to the understanding of biological vision
Visual areas are essential in transforming the raw luminous signal into a representation which efficiently conveys information about …
Last updated on 2023-03-29 Biological Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
Cite Project Slides URL
Artificial neural networks and machine learning applied to the understanding of biological vision
Visual areas are essential in transforming the raw luminous signal into a representation which efficiently conveys information about …
Last updated on 2023-03-29 Biological Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
Cite Project Slides URL
Resilience to sensory uncertainty in the primary visual cortex
Our daily endeavors occur in a complex visual environment, whose intrinsic variability shapes the way we integrate information to make …
Cite URL
Ultra-rapid visual search in natural images using active deep learning
This work extends to natural scenes a previous work on visual search on a simplified task formulated in Emmanuel Daucé, Pierre …
Cite URL
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 …
PDF Cite Venue
A resilient neural code in V1 to process natural images
Ultra-rapid visual search in natural images using active deep learning
Visual search, that is, the simultaneous localization and detection of a visual target of interest, is a vital task. Applied to the …
PDF Cite Venue
Ultra-rapid visual search in natural images using active deep learning
Retinotopic mapping improves the reliability of image classification
Follows a previous work A dual foveal-peripheral visual processing model implements efficient saccade selection Emmanuel Daucé, Pierre …
Last updated on 2022-05-20 Biological Neuroscience, Computer Vision, Visual Neuroscience
PDF Cite URL
Contributions of neuroscience to the detection and localization of objects in visual inputs
for visual search see: A dual foveal-peripheral visual processing model implements efficient saccade selection Emmanuel Daucé, Pierre …
Last updated on 2022-06-16 Biological Neuroscience, NeuroAI & Machine Learning, Visual Neuroscience
Cite location
Réseaux de neurones artificiels et apprentissage machine appliqués à la compréhension de la vision
Visual areas are essential in transforming the raw luminous signal into a representation which efficiently conveys information about …
2022-03-23 Biological Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
Cite Project Slides Slides Code URL
Statistics of the sparse representations of natural images
In most mammals, the primary visual cortex (V1) processes complex mixtures of orientations to construct an accurate neural …
Last updated on 2021-10-12 Behavioural Neuroscience, Biological Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience, Visual Neuroscience
Cite URL
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 …
PDF Cite DOI URL
Sparse Deep Predictive Coding captures contour integration capabilities of the early visual system
Both neurophysiological and psychophysical experiments have pointed out the crucial role of recurrent and feedback connections to …
PDF Cite DOI Code URL arXiv
Sparse Deep Predictive Coding captures contour integration capabilities of the early visual system
Understanding natural vision using deep predictive coding
Building models which efficiently process images is a great source of inspiration to better understand the processes which underly our …
2020-09-25 Biological Neuroscience, Computational Neuroscience, Computer Vision, NeuroAI & Machine Learning, Theoretical Neuroscience
Cite Slides Code URL
A dual foveal-peripheral visual processing model implements efficient saccade selection
In computer vision, the visual search task consists in extracting a scarce and specific visual information (the target) from a large …
Cite DOI Pdf Code URL bioRxiv
ANR PRIOSENS (2021/2025)
Integration sensory and prior information to control behavior (CRCNS US-French Research Proposal)
Behavioural Neuroscience, Biological Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning
ANR PRIOSENS (2021/2025)
From the retina to action: Understanding visual processing
Visual areas are essential in transforming the raw luminous signal into a representation which efficiently conveys information about …
2020-04-03 Biological Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
Cite Project Slides Code URL
2020-03-13: Soutenance de thèse Victor Boutin
Victor Boutin (Equipe NeOpTo) a soutenu sa thèse de doctorat intitulée: Sparse deep predictive coding: a bio-inspired model of visual perception / Etude d’un algorithme hiérarchique et codage épars et prédictif : vers un modèle bio-inspiré de la perception visuelle le Vendredi 13 mars à 14h
Last updated on 2020-03-04 4 min read Biological Neuroscience, Computational Neuroscience, Computer Vision, NeuroAI & Machine Learning, Theoretical Neuroscience
2020-03-13: Soutenance de thèse Victor Boutin
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 …
Cite DOI bioRxiv
From the retina to action: Dynamics of predictive processing in the visual system
Within the central nervous system, visual areas are essential in transforming the raw luminous signal into a representation which …
PDF Cite DOI Code URL HAL
From the retina to action: Dynamics of predictive processing in the visual system
Postdoc position on Visual computations using Spatio-temporal Diffusion Kernels and Traveling Waves
THE POSITION HAS BEEN FILLED. 18 month Post-doc position coordinated by Laurent Perrinet, supported by (INT, Marseille) and Yves Frégnac (UNIC-NeuroPSI, Gif).
2019-10-21 7 min read Behavioural Neuroscience, Biological Neuroscience, Computational Neuroscience, Computer Vision, NeuroAI & Machine Learning, Theoretical Neuroscience, Visual Neuroscience
Postdoc position on Visual computations using Spatio-temporal Diffusion Kernels and Traveling Waves
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 …
PDF Cite URL
Learning where to look: a foveated visuomotor control model
In computer vision, the visual search task consists in extracting a scarce and specific visual information (the target) from a large …
2019-07-15 Behavioural Neuroscience, Biological Neuroscience, Computational Neuroscience, Computer Vision, NeuroAI & Machine Learning, Theoretical Neuroscience, Visual Neuroscience
PDF Cite Slides Pdf Code URL
From the retina to action: Understanding visual processing
Visual areas are essential in transforming the raw luminous signal into a representation which efficiently conveys information about …
2019-04-03 Behavioural Neuroscience, Biological Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
Cite Project Slides Code URL
From the retina to action: Predictive processing in the visual system
Visual areas are essential in transforming the raw luminous signal into a representation which efficiently conveys information about …
2019-03-25 Biological Neuroscience, Computational Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience
Cite Slides Code URL
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.
Cite DOI URL HAL
Suppressive waves disambiguate the representation of long-range apparent motion in awake monkey V1
Sparse Deep Predictive Coding captures contour integration capabilities of the early visual system
Both neurophysiological and psychophysical experiments have pointed out the crucial role of recurrent and feedback connections to …
Cite URL arXiv
2018-11-09 : Retinal computations
A seminar by María José Escobar, Ph.D. at the Institute of Neurosciences Timone in Marseille.
2018-11-09 1 min read Biological Neuroscience
2018-11-09 : Retinal computations
The flash-lag effect as a motion-based predictive shift
Due to its inherent neural delays, the visual system has an outdated access to sensory information about the current position of moving …
Cite Slides DOI Press Code URL HAL
The flash-lag effect as a motion-based predictive shift
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 …
Cite DOI Code URL arXiv
ANR BalaV1 (2013/2016)
ANR BalaV1: Balanced states in area V1 (2013–2016)
Biological Neuroscience, Computational Neuroscience, Visual Neuroscience
ANR BalaV1 (2013/2016)
ANR Horizontal-V1 (2017/2021)
Connectivité Horizontale et Prédiction de Cohérences dans l’Intégration de Contour et Mouvement dans le Cortex Visuel Primaire
Behavioural Neuroscience, Biological Neuroscience, Computational Neuroscience, Visual Neuroscience
ANR Horizontal-V1 (2017/2021)
ANR PredictEye (2018/2020)
ANR PredictEye (2018/2020) : Mapping and predicting trajectories for eye movements
Biological Neuroscience, NeuroAI & Machine Learning
ANR PredictEye (2018/2020)
ANR TRAJECTORY (2016/2019)
ANR TRAJECTORY (2016/2019).
Biological Neuroscience, Computational Neuroscience, Computer Vision, Visual Neuroscience
Project
ANR TRAJECTORY (2016/2019)
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 …
Cite DOI URL HAL
A dynamic model for decoding direction and orientation in macaque primary visual cortex
Natural scenes generally contain objects in motion. The local orientation of their contours and the direction of motion are two …
Cite DOI URL
A dynamic model for decoding direction and orientation in macaque primary visual cortex
Natural scenes generally contain objects in motion. The local orientation of their contours and the direction of motion are two …
PDF Cite DOI
FACETS (2006/2010)
FACETS: Fast Analog Computing with Emergent Transient States (2006/2010).
Biological Neuroscience, Computational Neuroscience
FACETS (2006/2010)
FACETS-ITN (2010/2013)
FACETS-ITN: From Neuroscience to neuro-inspired computing (2010/2013)
Biological Neuroscience, Computational Neuroscience
FACETS-ITN (2010/2013)
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 …
Cite
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 …
Cite DOI URL
WP5 - Demo 1.3 : Spiking model of motion-based prediction
The question how the visual system is able to create a coherent representation of a rapidly changing environment in the presence of …
2014-03-20 Behavioural Neuroscience, Biological Neuroscience, NeuroAI & Machine Learning, Theoretical Neuroscience, Visual Neuroscience
PDF Cite Slides Conference URL
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
Cite URL
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 …
PDF Cite DOI URL
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 …
Cite DOI URL
Motion Clouds: Model-based stimulus synthesis of natural-like random textures for the study of motion perception
Choosing an appropriate set of stimuli is essential to characterize the response of a sensory system to a particular functional …
PDF Cite DOI HAL URL arXiv Supp
Motion Clouds: Model-based stimulus synthesis of natural-like random textures for the study of motion perception
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
Cite URL
Saccadic foveation of a moving visual target in the rhesus monkey
When generating a saccade toward a moving target, the target displacement that occurs during the period spanning from its detection to …
Cite DOI 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
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 …
Cite DOI 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 DOI
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 …
Cite Project
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
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
Cite URL
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
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 …
Cite
Decoding the population dynamics underlying ocular following response using a probabilistic framework
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 …
PDF Cite DOI
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 …
Cite URL
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
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). …
PDF Cite DOI URL
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 …
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
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