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

Recent Publications

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Recent Events

2020-09-11 : Feedforward and feedback processes in visual recognition (T Serre)

A seminar by Thomas Serre at the Institute of Neurosciences Timone in Marseille.



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 (2018/2020)

ANR CausaL (2018/2020) : 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 (2018/2020)

ANR PredictEye (2018/2020) : 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).


Cours et tutoriels

Liste de cours et tutoriels.

Art <> Sciences

Liste d’actions entre art et sciences.

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.

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 in Computational Neuroscience


Wahiba Taouali

PostDoc in Computational Neuroscience

Recent & Upcoming Talks


  • +33 619 478 120
  • NeOpTo Team
    Institut de Neurosciences de la Timone (UMR 7289)
    Aix Marseille Université, CNRS
    Faculté de Médecine - Bâtiment Neurosciences,
    27, Bd Jean Moulin, Marseille, PACA 13385 Marseille Cedex 05
  • Enter INT Building 1 and take the stairs to Floor 2
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