Visual-Cortex | Laurent Perrinet

Revisiting Horizontal Connectivity Rules in V1: From like-to-like towards like-to-All

Sat, 05 Feb 2022 00:00:00 +0000

Check-out this presentation of the paper:

When Cortical Neurons Talk Sideways: Beyond Feedforward Visual Processing

In this seminar we will challenge the traditional understanding of neuronal connectivity in primary visual cortex. While current theory suggests that neurons connect preferentially to others with similar orientation preferences, I will present evidence for a more complex connectivity pattern based on a distance-dependent rule: short-range connections show a like-to-like bias, while long-range connections connect more widely. This revised model better explains how the visual cortex processes complex stimuli and accounts for observed variations in neuronal interactions at different scales.

Laurent U Perrinet
2025-02-11 NeuroAI & Machine Learning

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A Behavioral Receptive Field for Ocular Following in Monkeys: Spatial Summation and Its Spatial Frequency Tuning

Sat, 01 Jan 2022 00:00:00 +0000

Dynamical processing of orientation precision in the primary visual cortex

Fri, 27 Aug 2021 00:00:00 +0000

This is 40th edition of Dynamicsdays
Nice, 23-27 August 2021 - https://dynamicsdays2021.univ-cotedazur.fr
check out the book of abstracts
In this talk, we will present the following paper :
Preliminary Program:
- Bruno Cessac, The Retina as a Dynamical System
- Hugo Ladret & Laurent Perrinet, Dynamics of the processing of orientation precision in the primary visual cortex
- Gianluigi Mongillo, Glassy phase in dynamically balanced networks
- Romain Veltz, Spatial and color hallucinations in a mathematical model of primary visual cortex

Dynamical processing of orientation precision in the primary visual cortex

Thu, 20 May 2021 14:00:00 +0000

As presented during the NeuroFrance 2021 meeting
get the abstract book
In this talk, we will present the following paper :

Modulation of orientation selectivity by orientation precision

Fri, 01 Jan 2021 00:00:00 +0000

This poster is presented in the following paper (published in Nature Comm Biology):
Hugo Ladret, Nelson Cortes, Lamyae Ikan, Frédéric Chavane, Christian Casanova, Laurent U Perrinet (2023). Cortical recurrence supports resilience to sensory variance in the primary visual cortex. Nature Communications Biology.
PDF Cite DOI Dataset Code URL bioRxiv HAL

From the retina to action: Dynamics of predictive processing in the visual system

Wed, 01 Jan 2020 00:00:00 +0000

Find the text at https://laurentperrinet.github.io/Perrinet20PredictiveProcessing/
The source code of the text is available at https://github.com/laurentperrinet/Perrinet20PredictiveProcessing This chapter is available as part of the book “The Philosophy and Science of Predictive Processing” : List of Contributors :
Preface: The Brain as a Prediction Machine, Anil Seth
Introduction, Dina Mendonça, Manuel Curado & Steven S. Gouveia
Part I: Predictive Processing: Philosophical Approaches

Predictive Processing and Representation: How Less Can Be More, Erik Myin and Thomas van Es
A Humean Challenge to Predictive Coding, Colin Klein
Are Markov Blankets Real and Does it Matter?, Richard Menary and Alexander J. Gillett
Predictive Processing and Metaphysical Views of the Self, Robert Clowes and Klaus Gärtner

Part II: Predictive Processing: Cognitive Science and Neuroscientific Approaches

From the Retina to Action: Dynamics of Predictive Processing in the Visual System, Laurent Perrinet
Predictive Processing and Consciousness: Prediction Fallacy and its Spatiotemporal Resolution, Steven S. Gouveia
The Many Faces of Attention: Why Precision Optimization is not Attention, Sina Fazelpour and Madeleine Ransom
Predictive Processing: Does it Compute?, Chris Thornton

Part III: Predictive Processing: Mental Health

The Predictive Brain, Conscious Experience and Brain-related Conditions, Lisa Feldman Barrett and Lorena Chanes
Disconnection and Diaschisis: Active Inference in Neuropsychology, Thomas Parr and Karl Friston
The Phenomenology and Predictive Processing of Time in Depression, Zachariah Neemeh and Shaun Gallagher
Why Use Predictive Processing to Explain Psychopathology? The Case of Anorexia Nervosa, Jakob Hohwy and Stephen Gadsby

Afterword, Manuel Curado

Learning dynamics in a neural network model of the primary visual cortex

Wed, 01 Jan 2020 00:00:00 +0000

See also Ladret and Perrinet, 2019

Suppressive waves disambiguate the representation of long-range apparent motion in awake monkey V1

Mon, 18 Mar 2019 00:00:00 +0000

An adaptive homeostatic algorithm for the unsupervised learning of visual features

Tue, 01 Jan 2019 00:00:00 +0000

“An adaptive algorithm for unsupervised learning”

supplementary info : https://spikeai.github.io/HULK/
Abstract
HTML
PDF
code for paper: https://github.com/SpikeAI/HULK
code for framework: https://github.com/bicv/SparseHebbianLearning/
code for figures https://github.com/SpikeAI/HULK/blob/master/Annex.ipynb (which is rendered @ https://spikeai.github.io/HULK/ )
video abstract (and the code for generating it)
previous publication :
Laurent U Perrinet (2010). Role of homeostasis in learning sparse representations. Neural Computation.
PDF Cite DOI Hal Code URL arXiv

$$f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\ 1-p_{0}^{*} & \text{if }k=0.\end{cases}$$

Orientation selectivity to synthetic natural patterns in a cortical-like model of the cat primary visual cortex

Tue, 01 Jan 2019 00:00:00 +0000

See a followup in Ladret and Perrinet, 2020

Selectivity to oriented patterns of different precisions

Mon, 01 Jan 2018 00:00:00 +0000

poster présenté au GDR vision, Paris.
program : https://gdrvision2018.sciencesconf.org/data/pages/posters_GDRVision2018.pdf
Poster (pdf)
code : https://github.com/hugoladret/InternshipM1

Push-Pull Receptive Field Organization and Synaptic Depression: Mechanisms for Reliably Encoding Naturalistic Stimuli in V1

Fri, 01 Jan 2016 00:00:00 +0000

Sparse Coding Of Natural Images Using A Prior On Edge Co-Occurences

Sat, 01 Aug 2015 00:00:00 +0000

Motion-based prediction is sufficient to solve the aperture problem

Thu, 12 Jan 2012 17:00:00 +0000

See a followup in Perrinet et al, 2012

Complex dynamics in recurrent cortical networks based on spatially realistic connectivities

Sun, 01 Jan 2012 00:00:00 +0000

Based on
Nicole Voges, Laurent U Perrinet (2010). Phase space analysis of networks based on biologically realistic parameters. Journal of Physiology-Paris.
PDF Cite DOI URL

Probabilistic models of the low-level visual system: the role of prediction in detecting motion

Fri, 17 Dec 2010 00:00:00 +0000

An event ranging “From Mathematical Image Analysis to Neurogeometry of the Brain” Ladislav Tauc & GDR MSPC neurosciences conference.

related publication from Mina Khoei @ TAUC 2012
see this more recent talk @ UCL, London

Phase space analysis of networks based on biologically realistic parameters

Wed, 10 Nov 2010 00:00:00 +0000

see follow-up :
Nicole Voges, Laurent U Perrinet (2012). Complex dynamics in recurrent cortical networks based on spatially realistic connectivities. Frontiers in Computational Neuroscience.
PDF Cite DOI URL

Functional consequences of correlated excitatory and inhibitory conductances in cortical networks

Tue, 01 Jun 2010 00:00:00 +0000

Models of low-level vision: linking probabilistic models and neural masses

Fri, 08 Jan 2010 00:00:00 +0000

see this more recent talk @ UCL, London

Dynamical emergence of a neural solution for motion integration

Fri, 01 Jan 2010 00:00:00 +0000

Based on
Laurent U Perrinet, Guillaume S Masson (2012). Motion-based prediction is sufficient to solve the aperture problem. Neural Computation.
PDF Cite Pdf arXiv Doi
see follow-up on motion extrapolation:
Mina A Khoei, Guillaume S Masson, Laurent U Perrinet (2013). Motion-based prediction explains the role of tracking in motion extrapolation. Journal of Physiology-Paris.
PDF Cite DOI URL
see follow-up on the flash-lag effect:
Mina A Khoei, Guillaume S Masson, Laurent U Perrinet (2017). The flash-lag effect as a motion-based predictive shift. PLoS Computational Biology.
Cite Slides DOI Press Code URL HAL

Dynamical emergence of a neural solution for motion integration

Fri, 01 Jan 2010 00:00:00 +0000

Phase space analysis of networks based on biologically realistic parameters

Fri, 01 Jan 2010 00:00:00 +0000

Based on
Nicole Voges, Laurent U Perrinet (2010). Phase space analysis of networks based on biologically realistic parameters. Journal of Physiology-Paris.
PDF Cite DOI URL
see follow-up :
Nicole Voges, Laurent U Perrinet (2012). Complex dynamics in recurrent cortical networks based on spatially realistic connectivities. Frontiers in Computational Neuroscience.
PDF Cite DOI URL

Probabilistic models of the low-level visual system: the role of prediction in detecting motion

Fri, 01 Jan 2010 00:00:00 +0000

See a followup in Perrinet et al, 2012

Decoding center-surround interactions in population of neurons for the ocular following response

Thu, 01 Jan 2009 00:00:00 +0000

Dynamical state spaces of cortical networks representing various horizontal connectivities

Thu, 01 Jan 2009 00:00:00 +0000

Based on
Nicole Voges, Laurent U Perrinet (2010). Phase space analysis of networks based on biologically realistic parameters. Journal of Physiology-Paris.
PDF Cite DOI URL
see follow-up :
Nicole Voges, Laurent U Perrinet (2012). Complex dynamics in recurrent cortical networks based on spatially realistic connectivities. Frontiers in Computational Neuroscience.
PDF Cite DOI URL

Dynamics of cortical networks including long-range patchy connections

Thu, 01 Jan 2009 00:00:00 +0000

Based on
Nicole Voges, Laurent U Perrinet (2010). Phase space analysis of networks based on biologically realistic parameters. Journal of Physiology-Paris.
PDF Cite DOI URL
see follow-up :
Nicole Voges, Laurent U Perrinet (2012). Complex dynamics in recurrent cortical networks based on spatially realistic connectivities. Frontiers in Computational Neuroscience.
PDF Cite DOI URL

Analyzing cortical network dynamics with respect to different connectivity assumptions

Wed, 01 Oct 2008 00:00:00 +0000

Based on
Nicole Voges, Laurent U Perrinet (2010). Phase space analysis of networks based on biologically realistic parameters. Journal of Physiology-Paris.
PDF Cite DOI URL
see follow-up :
Nicole Voges, Laurent U Perrinet (2012). Complex dynamics in recurrent cortical networks based on spatially realistic connectivities. Frontiers in Computational Neuroscience.
PDF Cite DOI URL

Decoding the population dynamics underlying ocular following response using a probabilistic framework

Tue, 01 Jan 2008 00:00:00 +0000

Dynamics of cortical networks based on patchy connectivity patterns

Tue, 01 Jan 2008 00:00:00 +0000

Based on
Nicole Voges, Laurent U Perrinet (2010). Phase space analysis of networks based on biologically realistic parameters. Journal of Physiology-Paris.
PDF Cite DOI URL
see follow-up :
Nicole Voges, Laurent U Perrinet (2012). Complex dynamics in recurrent cortical networks based on spatially realistic connectivities. Frontiers in Computational Neuroscience.
PDF Cite DOI URL

Modeling spatial integration in the ocular following response to center-surround stimulation using a probabilistic framework

Tue, 01 Jan 2008 00:00:00 +0000

Dynamical Neural Networks: modeling low-level vision at short latencies

Thu, 01 Mar 2007 00:00:00 +0000

Dynamical Neural Networks (DyNNs) are a class of models for networks of neurons where particular focus is put on the role of time in the emergence of functional computational properties. The definition and study of these models involves the cooperation of a large range of scientific fields from statistical physics, probabilistic modelling, neuroscience and psychology to control theory. It focuses on the mechanisms that may be relevant for studying cognition by hypothesizing that information is distributed in the activity of the neurons in the system and that the timing helps in maintaining this information to lastly form decisions or actions. The system responds at best to the constraints of the outside world and learning strategies tune this internal dynamics to achieve optimal performance. This chapter introduces the book. See also:

Bruno Cessac, Emmanuel Daucé, Laurent U Perrinet, Manuel Samuelides (2007). Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision. Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision.
Cite
Bruno Cessac, Emmanuel Daucé, Laurent U Perrinet, Manuel Samuelides (2007). Introduction to Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision. Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision.
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Modeling spatial integration in the ocular following response using a probabilistic framework

Mon, 01 Jan 2007 00:00:00 +0000

On efficient sparse spike coding schemes for learning natural scenes in the primary visual cortex

Mon, 01 Jan 2007 00:00:00 +0000

Sparse Gabor wavelets by local operations

Wed, 29 Jun 2005 00:00:00 +0000

relies on log-Gabor filters:
Sylvain Fischer, Filip Šroubek, Laurent U Perrinet, Rafael Redondo, Gabriel Cristóbal (2007). Self-Invertible 2D Log-Gabor Wavelets. International Journal of Computer Vision.
PDF Cite DOI Code URL

Schematic structure of the primary visual cortex implemented in the present study. Simple cortical cells are modeled through log-Gabor functions. They are organized in pairs in quadrature of phase (dark-gray circles). For each position the set of different orientations compose a pinwheel (large light-gray circles). The retinotopic organization induces that adjacent spatial positions are arranged in adjacent pinwheels. Inhibition interactions occur towards the closest adjacent positions which are in the direc-tions perpendicular to the cell preferred orientation and toward adjacent orientations (light-red connections). Facilitation occurs to-wards co-aligned cells up to a larger distance (dark-blue connections).

Finding Independent Components using spikes : a natural result of Hebbian learning in a sparse spike coding scheme

Thu, 01 Jan 2004 00:00:00 +0000

Comment déchiffrer le code impulsionnel de la vision ? Étude du flux parallèle, asynchrone et épars dans le traitement visuel ultra-rapide

Wed, 01 Jan 2003 00:00:00 +0000

Le jury était consistué (de gauche à droite) de Jeanny Hérault (Rapporteur), Michel Imbert (Président), Yves Burnod (Rapporteur, absent de la photo), Manuel Samuelides (Directeur de thèse) et Simon Thorpe (Co-directeur de thèse).

Emergence of filters from natural scenes in a sparse spike coding scheme

Wed, 01 Jan 2003 00:00:00 +0000

Sparse Image Coding Using an Asynchronous Spiking Neural Network

Tue, 01 Jan 2002 00:00:00 +0000

Progressive reconstruction of a static image using spikes in a Laplacian pyramid.

Network of integrate-and-fire neurons using Rank Order Coding A: how to implement spike timing dependant plasticity

Mon, 01 Jan 2001 00:00:00 +0000