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.

Follow @laurentperrinet on GitHub.

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

This would not be possible without…




Andrew Isaac Meso

Lecturer, King’s College London (IOPPN).


Jonathan Vacher

Maître de Conférence (Associate Professor) @ MAP5, Université Paris-Cité.

Current Students


Hugo Ladret

Phd candidate in Computational Neuroscience


Antoine Grimaldi

Phd candidate in Computational Neuroscience


Jean-Nicolas Jérémie

Phd candidate in Computational Neuroscience

Former Students


Angelo Franciosini

Biomedical Engineer @ Avicenna.AI.


Victor Boutin

Post-doc @ Serre Lab, BRown University.


Alberto Arturo Vergani

Post-doctoral Researcher @ Sant’Anna School of Advanced Studies, Pisa, Italy.


Wahiba Taouali

Consulting manager @ Enthought, Cambridge, United Kingdom.


Jean-Bernard Damasse

Phd in Computational Neuroscience


Kiana Mansour-Pour

Executive DirectorExecutive Director, Shotise


Mina A Khoei

Senior AI/ML scientist @ SynSense, Zurich, Switzerland.


Jens Kremkow

PI @ Neuroscience Research Center, Charité, Berlin, Germany.


Nicole Voges

PostDoc in Computational Neuroscience


Quickly discover relevant content by filtering publications.
(2007). Bayesian modeling of dynamic motion integration. Neuro-Computation: From Sensorimotor Integration to Computational Frameworks.


Recent & Upcoming Talks

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