Novel visual computations

Let’s admit it: brains are not computers. Indeed, computers are still deceptive compared to biological perceptual systems. Think about rapidly detecting a novel object in clutter. Think about performing this with little supervision at a low energetic cost…

To narrow the gap between neuroscience and the theory of sensory processing computations, I am interested in bridging geometrical regularities found in natural scenes with the properties of neural computations as they are observed in sensory processes or behavior.

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Laurent U Perrinet

Laurent U Perrinet

Researcher in Computational Neuroscience


Laurent Perrinet is a computational neuroscientist specialized in large scale neural network models of low-level vision, perception and action, currently at the “Institut de Neurosciences de la Timone” (France), a joint research unit (CNRS / Aix-Marseille Université). He co-authored more than 40 articles in computational neuroscience and computer vision. He graduated from the aeronautics engineering school SUPAERO, in Toulouse (France) with a signal processing and applied mathematics degree. He received a PhD in Cognitive Science in 2003 on the mathematical analysis of temporal spike coding of images by using a multi-scale and adaptive representation of natural scenes. His research program is focusing in bridging the complex dynamics of realistic, large-scale models of spiking neurons with functional models of low-level vision. In particular, as part of the FACETS and BrainScaleS consortia, he has developed experimental protocols in collaboration with neurophysiologists to characterize the response of population of neurons. Recently, he extended models of visual processing in the framework of predictive processing in collaboration with the team of Karl Friston at the University College of London. This method aims at characterizing the processing of dynamical flow of information as an active inference process. His current challenge within the NeOpTo team is to translate, or compile in computer terminology, this mathematical formalism with the event-based nature of neural information with the aim of pushing forward the frontiers of Artificial Intelligence systems.


  • Computational Neuroscience
  • Machine Learning
  • Vision


  • Habilitation à diriger des recherches , 2014

    Aix Marseille Université

  • PhD. in Cognitive Science, 2003

    Université P. Sabatier, Toulouse, France

  • M.S. in Engineering, 1998

    Supaéro, Toulouse, France


Art <> Sciences

Liste d’actions entre art et sciences.

Cours et de tutoriels

Liste de cours et tutoriels.

Open Science

To enable the dissemination of the knowledge that is produced in our lab, we share all source code with open source licences.

Tout public!

Liste d’actions destinées à la culture scientifique et au public en général.

Recent Publications

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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 …

Effect of top-down connections in Hierarchical Sparse Coding

Hierarchical Sparse Coding (HSC) is a powerful model to efficiently represent multi-dimensional, structured data such as images. The …

Humans adapt their anticipatory eye movements to the volatility of visual motion properties

Humans are able to accurately track a moving object with a combination of saccades and smooth eye movements. These movements allow …

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 …

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.

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 …

Speed-Selectivity in Retinal Ganglion Cells is Sharpened by Broad Spatial Frequency, Naturalistic Stimuli

Motion detection represents one of the critical tasks of the visual system and has motivated a large body of research. However, it …

An adaptive homeostatic algorithm for the unsupervised learning of visual features

The formation of structure in the visual system, that is, of the connections between cells within neural populations, is by large an …

This would not be possible without…



Current Students


Alberto Arturo Vergani

Post-Doc in Computational Neuroscience


Angelo Franciosini

Phd candidate in Computational Neuroscience


Hugo Ladret

Phd candidate in Computational Neuroscience

Former Students


Jean-Bernard Damasse

Phd in Computational Neuroscience


Jens Kremkow

Phd in Computational Neuroscience


Kiana Mansour-Pour

Phd in Computational Neuroscience


Mina A Khoei

Phd in Computational Neuroscience


Nicole Voges

PostDoc in Computational Neuroscience


Victor Boutin

Phd candidate in Computational Neuroscience


Wahiba Taouali

PostDoc in Computational Neuroscience

Recent Events

2020-03-13 Soutenance Victor Boutin

Victor Boutin (Equipe NeOpTo) soutiendra 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

Postdoc position on Visual computations using Spatio-temporal Diffusion Kernels and Traveling Waves

18 month Post-doc position coordinated by Laurent Perrinet, supported by (INT, Marseille) and Yves Frégnac (UNIC-NeuroPSI, Gif).

2019-10-10: GDR vision 2019

Le GDR Vision réunit toute la communauté des chercheurs en France travaillant sur la perception visuelle (de la perception des attributs visuels comme la couleur et le mouvement à la reconnaissance des mots et des visages), l’intégration multi-sensorielle, la motricité (dont notamment le contrôle des mouvements des yeux et de la tête dans une variété de tâches; des plus basiques aux plus cognitives), la représentation de l’espace et les processus décisionnels. Il s’agit d’un réseau pluridisciplinaire réunissant psychophysiciens, psychologues, chercheurs en neurosciences (électrophysiologie et imagerie chez l’animal et l’Homme), et modélisateurs.

2019-10-10: Atelier Utiliser l'apprentissage profond en vision

Le GDR Vision réunit toute la communauté française de chercheurs en vision. Nous aurons un atelier méthodologique le jeudi matin sur les apports possibles du Deep Learning pour les sciences de la vision: Utiliser l’apprentissage profond en vision.

2019-10-07: Le temps des sens

Dans le monde qui nous entoure, nous percevons le temps s’écouler de façon immuable et universelle. Pourtant, il existe un temps pour chaque sens. Laurent Perrinet (AMU) exposera la dynamique des réseaux de neurones et le temps particulier qui sont associés à l’un d’entre eux, la vision.

Recent & Upcoming Talks

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 …

Des illusions aux hallucinations visuelles: une porte sur la perception

Les illusions visuelles sont des créations d’artistes, de scientifiques et plus récemment, grâce aux réseaux sociaux, du grand …

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 …

Should I stay or should I go? Humans adapt to the volatility of visual motion properties, and know about it

Animal behavior must constantly adapt to changes, for example when the state of an environmental context changes unexpectedly. For an …

Des illusions aux hallucinations visuelles: une porte sur la perception

Les objectifs sont : – mieux comprendre la fonction de la perception visuelle en explorant certaines limites ; – mieux comprendre l’importance de l’aspect dynamique de la perception ; – mieux comprendre le rôle de l’action dans la perception.



MesoCentre (2018/2022)

MesoCentre (2018/2022) : access to the HPC resources of Aix-Marseille Université.

SpikeAI: laureat du Défi Biomimétisme (2019)

Algorithmes événementiels d’Intelligence Artificielle / Event-Based Artificial Inteligence (2019).

ANR BalaV1 (2013/2016)

ANR BalaV1: Balanced states in area V1 (2013–2016)

ANR CausaL (2018/2022)

ANR CausaL (2018/2022) : Cognitive​ ​architectures​ ​of​ Causal​ ​Learning.

ANR Horizontal-V1 (2017/2021)

ANR Horizontal-V1 (2017/2021): Connectivité Horizontale et Prédiction de Cohérences dans l’Intégration de Contour et Mouvement dans le Cortex Visuel Primaire

ANR PredictEye (2018/2022)

ANR PredictEye (2018/2022) : Mapping and predicting trajectories for eye movements

ANR REM (2013/2016)

ANR REM : Renforcement et mouvements oculaires (2013/2016).

ANR SPEED (2013/2016)

ANR SPEED: Traitement de la vitesse dans les scènes visuelles naturelles (2013/2016).

ANR TRAJECTORY (2016/2019)

ANR TRAJECTORY (2016/2019).

DOC2AMU (2016/2019)

DOC2AMU: An Excellence Fellowship (2016/2019).

PhD ICN (2017 / 2021)

A grant from the Ph.D. program in Integrative and Clinical Neuroscience (PhD position, 2017 / 2021).

BrainScaleS (2011/2014)

BrainScaleS: Brain-inspired multiscale computation in neuromorphic hybrid systems (2011/2014).

CODDE (2008/2012)

CODDE: understanding brain and behaviour (2008/2012).

FACETS (2006/2010)

FACETS: Fast Analog Computing with Emergent Transient States (2006/2010).

FACETS-ITN (2010/2013)

FACETS-ITN: From Neuroscience to neuro-inspired computing (2010/2013)

PACE-ITN (2015/2019)

PACE-ITN: ITN Marie Curie network (2015/2019).