<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Behavioural Neuroscience | Laurent Perrinet</title><link>https://laurentperrinet.github.io/category/behavioural-neuroscience/</link><atom:link href="https://laurentperrinet.github.io/category/behavioural-neuroscience/index.xml" rel="self" type="application/rss+xml"/><description>Behavioural Neuroscience</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en</language><copyright>This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License Please note that multiple distribution, publication or commercial usage of copyrighted papers included in this website would require submission of a permission request addressed to the journal in which the paper appeared.</copyright><lastBuildDate>Sat, 11 Apr 2026 00:00:00 +0000</lastBuildDate><image><url>https://laurentperrinet.github.io/media/icon_hu_f2990a9a83ba401.png</url><title>Behavioural Neuroscience</title><link>https://laurentperrinet.github.io/category/behavioural-neuroscience/</link></image><item><title>Dans l’intelligence du regard : l’art révèle la diversité de notre vision</title><link>https://laurentperrinet.github.io/talk/2026-04-11-intelligence-du-regard/</link><pubDate>Sat, 11 Apr 2026 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2026-04-11-intelligence-du-regard/</guid><description>&lt;p&gt;Cette présentation (dans le cadre du &lt;em&gt;Forum des Sciences Cognitives&lt;/em&gt;) explore la collaboration avec Étienne Rey, notamment le travail exposé lors de l’exposition &lt;em&gt;La vibration des apparences&lt;/em&gt;, qui a eu lieu au musée Granet :&lt;/p&gt;
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;span class="article-metadata li-cite-author"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/%C3%A9tienne-rey/"&gt;Étienne Rey&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;
&lt;/span&gt;
(2024).
&lt;a href="https://laurentperrinet.github.io/post/2024-11-07_vibration-apparences/"&gt;La vibration des apparences&lt;/a&gt;.
&lt;p&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://laurentperrinet.github.io/project/art-science/"&gt;
Project
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://laurentperrinet.github.io/sciblog/posts/2025-01-18_la-vibration-des-apparences.html" target="_blank" rel="noopener"&gt;
Code&lt;/a&gt;
&lt;/p&gt;
&lt;/div&gt;
&lt;p&gt;Cette présentation (dans le cadre du &lt;em&gt;Forum des Sciences Cognitives&lt;/em&gt;) explore la collaboration avec Étienne Rey, notamment le travail exposé lors de l’exposition &lt;em&gt;La vibration des apparences&lt;/em&gt;, qui a eu lieu au musée Granet :&lt;/p&gt;
&lt;blockquote&gt;
&lt;p&gt;La vision reste un paradoxe : comment un processus aussi complexe qu’apprendre à « faire sens de nos sens » peut-il être si simple à acquérir et à utiliser ? Pas besoin de mode d’emploi pour le nouveau-né qui ouvre les yeux pour la première fois. La démarche scientifique permet de percer certains aspects de ce mystère, notamment en révélant les failles de notre perception. Nous explorerons ensemble cette frontière entre art et sciences cognitives à travers un parcours allant des illusions visuelles jusqu’à l’art contemporain. Grâce à ma collaboration avec l’artiste plasticien Étienne Rey, je montrerai comment ces créations deviennent des outils pour décrypter certains mécanismes cachés de la vision, à l’heure où l’IA interroge notre rapport au réel.&lt;/p&gt;&lt;/blockquote&gt;
&lt;p&gt;Plus d’infos :&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;a href="https://cognivence.scicog.fr/forum-des-sciences-cognitives/" target="_blank" rel="noopener"&gt;Site de l’association (Forum des Sciences Cognitives)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.linkedin.com/posts/cognivence_forum-ateliers-science-activity-7447730430671372288-atJq" target="_blank" rel="noopener"&gt;Ateliers - publication 1 (LinkedIn)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.linkedin.com/posts/forum-ateliers-science-ugcPost-7447730429412962304--n5f" target="_blank" rel="noopener"&gt;Ateliers - publication 2 (LinkedIn)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.instagram.com/forum_sciences_cognitives" target="_blank" rel="noopener"&gt;Instagram&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.linkedin.com/company/cognivence/posts/" target="_blank" rel="noopener"&gt;LinkedIn Cognivence&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;
&lt;figure id="figure-étienne-rey-variations--adagp-paris-2024-crédit-image--étienne-rey"&gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img src="https://laurentperrinet.github.io/post/2024-11-07_vibration-apparences/variations.jpg" alt="Étienne Rey, *Variations* © ADAGP, Paris, 2024. Crédit image : Étienne Rey" loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;figcaption&gt;
Étienne Rey, &lt;em&gt;Variations&lt;/em&gt; © ADAGP, Paris, 2024. Crédit image : Étienne Rey
&lt;/figcaption&gt;&lt;/figure&gt;
Plus de liens :&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;a href="https://www.instagram.com/p/DWq88aRjH0I/?utm_source=ig_web_copy_link&amp;amp;igsh=MzRlODBiNWFlZA==" target="_blank" rel="noopener"&gt;https://www.instagram.com/p/DWq88aRjH0I/?utm_source=ig_web_copy_link&amp;igsh=MzRlODBiNWFlZA==&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.linkedin.com/posts/cognivence_confaezrence-science-neurosciences-activity-7445821764468801537-Gg2F" target="_blank" rel="noopener"&gt;https://www.linkedin.com/posts/cognivence_confaezrence-science-neurosciences-activity-7445821764468801537-Gg2F&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Lab Tour for Art - Perception Collaboration</title><link>https://laurentperrinet.github.io/talk/2026-01-19-art-and-science/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2026-01-19-art-and-science/</guid><description>&lt;p&gt;👁️ Very glad to present our Art/Perception collaboration with Etienne Rey today!&lt;/p&gt;
&lt;p&gt;🔗 &lt;a href="https://laurentperrinet.github.io/talk/2026-01-19-art-and-science" target="_blank" rel="noopener"&gt;https://laurentperrinet.github.io/talk/2026-01-19-art-and-science&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;This session is part of the &lt;a href="https://www.risd.edu/academics/illustration/courses" target="_blank" rel="noopener"&gt;France: perception en Provence: French art and science&lt;/a&gt; course from the &lt;a href="https://www.risd.edu" target="_blank" rel="noopener"&gt;Rhode Island School of Design&lt;/a&gt;, organized by &lt;a href="https://www.instagram.com/cathuangart/" target="_blank" rel="noopener"&gt;Catherine Huang&lt;/a&gt; with &lt;a href="https://www.linkedin.com/in/ntolley/" target="_blank" rel="noopener"&gt;Nick Tolley&lt;/a&gt;.&lt;/p&gt;
&lt;p&gt;What happens today:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;
&lt;p&gt;We will discuss with Etienne about the emergence of our collaboration&lt;/p&gt;
&lt;/li&gt;
&lt;li&gt;
&lt;p&gt;To discover the fantastic world of neuroAI, I will give a *
scientific talk* about computational neuroscience and neuroAI - in line with:
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;span class="article-metadata li-cite-author"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;
&lt;/span&gt;
(2025).
&lt;a href="https://laurentperrinet.github.io/talk/2025-12-12-main/"&gt;A New Look for Convolutional Deep Networks&lt;/a&gt;.
&lt;em&gt;Montreal AI and Neuroscience conference, Dec 11-13th, 2025&lt;/em&gt;.
&lt;p&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/talk/2025-12-12-main/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://main2025.org" target="_blank" rel="noopener"&gt;
MAIN&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://laurentperrinet.github.io/2025-12-13_Perrinet-talk-MAIN2025" target="_blank" rel="noopener"&gt;
Slides&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://www.youtube.com/watch?v=1BUidO5GY98" target="_blank" rel="noopener"&gt;
YouTube&lt;/a&gt;
&lt;/p&gt;
&lt;/div&gt;
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;span class="article-metadata li-cite-author"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;
&lt;/span&gt;
(2025).
&lt;a href="https://laurentperrinet.github.io/talk/2025-04-18-vibration-apparences/"&gt;La vibration des apparences&lt;/a&gt;.
&lt;em&gt;Journées d’Ouverture Scientifique (JOS)&lt;/em&gt;.
&lt;p&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/talk/2025-04-18-vibration-apparences/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://laurentperrinet.github.io/project/courses/"&gt;
Project
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://laurentperrinet.github.io/slides/2025-04-18-vibration-apparences/" target="_blank"&gt;
Slides
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://laurentperrinet.github.io/talk/2025-04-18-vibration-apparences" target="_blank" rel="noopener"&gt;
URL&lt;/a&gt;
&lt;/p&gt;
&lt;/div&gt;
&lt;/p&gt;
&lt;/li&gt;
&lt;li&gt;
&lt;p&gt;We will see and discuss some actual works that emerged from that collaboration and notably this art exhibit:&lt;/p&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;span class="article-metadata li-cite-author"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/%C3%A9tienne-rey/"&gt;Étienne Rey&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;
&lt;/span&gt;
(2024).
&lt;a href="https://laurentperrinet.github.io/post/2024-11-07_vibration-apparences/"&gt;La vibration des apparences&lt;/a&gt;.
&lt;p&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://laurentperrinet.github.io/project/art-science/"&gt;
Project
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://laurentperrinet.github.io/sciblog/posts/2025-01-18_la-vibration-des-apparences.html" target="_blank" rel="noopener"&gt;
Code&lt;/a&gt;
&lt;/p&gt;
&lt;/div&gt;
&lt;p&gt;#DeepLearning #ComputerVision #AI #Research #NeuralNetworks #NeuroAI #OpenScience #artScience&lt;/p&gt;
&lt;div class="alert alert-note"&gt;
&lt;div&gt;
&lt;p&gt;The work of Etienne Rey links natural and physical phenomena with our perception. His works reveal themselves and become concrete in the personal experience of viewers. Light, the central element of his approach, activates these experiences, revealing the interactions between the material and the immaterial.&lt;/p&gt;
&lt;p&gt;Since 2011, Etienne Rey has been collaborating with Dr. Laurent Perrinet of the Timone Institute of Neuroscience. Together, they explore the domain of perception at the intersection of their respective disciplines, combining science and art to develop new perceptual approaches. Selected works: &lt;a href="https://laurentperrinet.github.io/post/2013-10-10_tropique/" target="_blank" rel="noopener"&gt;Tropiques (2013) &amp;amp; Space Odyssey (2015‑2024)&lt;/a&gt;, &lt;a href="https://laurentperrinet.github.io/post/2018-04-10_trames/" target="_blank" rel="noopener"&gt;Trame Eslasticité 2016&lt;/a&gt;, Turbulences 2018, &lt;a href="https://laurentperrinet.github.io/post/2021-10-04_interstices/" target="_blank" rel="noopener"&gt;Instabilités et Delaunay (2019)&lt;/a&gt;, &lt;a href="https://laurentperrinet.github.io/post/2024-11-07_vibration-apparences/" target="_blank" rel="noopener"&gt;La vibration des apparences (2025)&lt;/a&gt;, Azur (2028).&lt;/p&gt;
&lt;/div&gt;
&lt;/div&gt;</description></item><item><title>Physiological state Monitoring: a Riemannian Geometry based-model</title><link>https://laurentperrinet.github.io/publication/choplin-26-bci/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/choplin-26-bci/</guid><description/></item><item><title>Conférence immersive : La vision, réalité ou perception ?</title><link>https://laurentperrinet.github.io/talk/2025-06-12-explore-conference-immersive/</link><pubDate>Thu, 12 Jun 2025 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2025-06-12-explore-conference-immersive/</guid><description>&lt;ul&gt;
&lt;li&gt;
&lt;p&gt;&lt;a href="https://explore.univ-amu.fr/programme" target="_blank" rel="noopener"&gt;https://explore.univ-amu.fr/programme&lt;/a&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li&gt;
&lt;p&gt;Quoi: Conférence immersive&lt;/p&gt;
&lt;/li&gt;
&lt;li&gt;
&lt;p&gt;Quand: Jeudi 12 juin 2025 - 16h - 17h&lt;/p&gt;
&lt;/li&gt;
&lt;li&gt;
&lt;p&gt;Où: CRVM - Centre de réalité virtuelle de la Méditerranée (UMR 6233 CNRS &amp;amp; Université de la Méditerranée Faculté des Sciences du Sport, Avenue de Luminy, 13288 Marseille)&lt;/p&gt;
&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Ciné-Sciences : Ouvrez grand les yeux</title><link>https://laurentperrinet.github.io/talk/2025-06-10-explore-cine-sciences/</link><pubDate>Tue, 10 Jun 2025 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2025-06-10-explore-cine-sciences/</guid><description>&lt;p&gt;Dans le cadre de la deuxième édition du Festival EXPLORE, l&amp;rsquo;association Polly Maggoo vous propose de découvrir trois projections gratuites de courts métrages autour de thématiques de recherche de scientifiques, en leur présence.&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Programme: &lt;a href="https://pollymaggoo.org/festival-explore-2025/" target="_blank" rel="noopener"&gt;https://pollymaggoo.org/festival-explore-2025/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Le mardi 10 juin à 19h au cinéma Pathé Madeleine : 36 Avenue du Maréchal Foch, 13004 Marseille, France&lt;/li&gt;
&lt;li&gt;En présence de Laurent Perrinet, chercheur à l&amp;rsquo;Institut de Neurosciences de la Timone (INT). Spécialiste de la vision, IA, biomimétisme et biorobotique.&lt;/li&gt;
&lt;/ul&gt;
&lt;table&gt;
&lt;thead&gt;
&lt;tr&gt;
&lt;th&gt;Titre&lt;/th&gt;
&lt;th&gt;Réalisateur&lt;/th&gt;
&lt;th&gt;Pays&lt;/th&gt;
&lt;th&gt;Année&lt;/th&gt;
&lt;th&gt;Genre&lt;/th&gt;
&lt;th&gt;Durée&lt;/th&gt;
&lt;th&gt;Production&lt;/th&gt;
&lt;th&gt;Distribution&lt;/th&gt;
&lt;/tr&gt;
&lt;/thead&gt;
&lt;tbody&gt;
&lt;tr&gt;
&lt;td&gt;&lt;strong&gt;BLINKITY BLANK&lt;/strong&gt;&lt;/td&gt;
&lt;td&gt;Norman McLaren&lt;/td&gt;
&lt;td&gt;Canada&lt;/td&gt;
&lt;td&gt;1955&lt;/td&gt;
&lt;td&gt;Animation&lt;/td&gt;
&lt;td&gt;5'15&lt;/td&gt;
&lt;td&gt;Office National du film du Canada&lt;/td&gt;
&lt;td&gt;Agence du court métrage&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;&lt;td colspan="8"&gt;Court métrage expérimental explorant les possibilités de l’animation par intermittence et des images spasmodiques. Norman McLaren joue avec les lois de la persistance rétinienne dans une œuvre de pure imagination faisant penser tantôt à un feu d’artifice très nourri, puis ensuite à un dessin lent à se former et dont on ne perçoit que des touches rapides et éphémères.&lt;/td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;&lt;strong&gt;TO SEE&lt;/strong&gt;&lt;/td&gt;
&lt;td&gt;Tony Hill&lt;/td&gt;
&lt;td&gt;Royaume-Uni&lt;/td&gt;
&lt;td&gt;1982&lt;/td&gt;
&lt;td&gt;Expérimental&lt;/td&gt;
&lt;td&gt;12'10&lt;/td&gt;
&lt;td&gt;Tony Hill&lt;/td&gt;
&lt;td&gt;Light Cone&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;&lt;td colspan="8"&gt;Le film ouvre les yeux, cligne des paupières, et voit, regarde tout autour de lui dans une vision panoramique. Les formes, les lignes, les espaces, ne sont pas fixes, mais fluctuent avec le mouvement de la caméra. La vision sphérique redéfinit la géométrie, et crée une apparence presque quadri-dimensionnelle, qui donne l’impression, en définitive, de retourner l’espace sur lui-même.&lt;/td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;&lt;strong&gt;CARLOTTA’S FACE&lt;/strong&gt;&lt;/td&gt;
&lt;td&gt;Valentin Riedl &amp;amp; Frédéric Schuld&lt;/td&gt;
&lt;td&gt;Allemagne&lt;/td&gt;
&lt;td&gt;2018&lt;/td&gt;
&lt;td&gt;Expérimental&lt;/td&gt;
&lt;td&gt;5'&lt;/td&gt;
&lt;td&gt;Fabian&amp;amp;Fred&lt;/td&gt;
&lt;td&gt;Agence du court métrage&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;&lt;td colspan="8"&gt;Quand Carlotta se regarde dans le miroir, elle ne parvient pas à reconnaître l’image qui lui est renvoyée. Aveugle à son propre visage, la fillette traverse une enfance chaotique. Des années plus tard, atteinte d’une rare maladie neurologique, Carlotta découvrira son visage par la voie de l’art. En se dessinant elle-même, elle parviendra enfin à se reconnaître.&lt;/td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;&lt;strong&gt;WHEN TIME MOVES FASTER&lt;/strong&gt;&lt;/td&gt;
&lt;td&gt;Anna Vasof&lt;/td&gt;
&lt;td&gt;Autriche&lt;/td&gt;
&lt;td&gt;2016&lt;/td&gt;
&lt;td&gt;Animation&lt;/td&gt;
&lt;td&gt;6'32&lt;/td&gt;
&lt;td&gt;Anna Vasof&lt;/td&gt;
&lt;td&gt;Agence du court métrage&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;&lt;td colspan="8"&gt;Partagez notre plaisir : découvrez ces illusions qui ne sont rendues possibles qu’au moyen du cinéma !&lt;/td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;&lt;strong&gt;LIGHT LEAK&lt;/strong&gt;&lt;/td&gt;
&lt;td&gt;Nate Dorr&lt;/td&gt;
&lt;td&gt;États-Unis&lt;/td&gt;
&lt;td&gt;2021&lt;/td&gt;
&lt;td&gt;Animation fiction expérimental&lt;/td&gt;
&lt;td&gt;8'20&lt;/td&gt;
&lt;td&gt;Nate Dorr&lt;/td&gt;
&lt;td&gt;Nate Dorr&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;&lt;td colspan="8"&gt;La lumière est une information, un signal plus fort que le souvenir. S’il y a quelqu’un pour recevoir le message. Seul dans un appartement scellé jusqu’à ce que le temps perde son sens, le monde extérieur irréel ou inaccessible, les illuminations archaïques rampant sur les murs, les connexions s’effilochant. Que se passe-t-il ici ? Un film-essai de science-fiction, ou son inverse. Un film de rupture sur l’optique, la mémoire, les données. Les fins et ce qui y survit ou non.&lt;/td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;&lt;strong&gt;TONDO&lt;/strong&gt;&lt;/td&gt;
&lt;td&gt;Jérémie Van Quynh&lt;/td&gt;
&lt;td&gt;France&lt;/td&gt;
&lt;td&gt;2015&lt;/td&gt;
&lt;td&gt;Expérimental&lt;/td&gt;
&lt;td&gt;3'58&lt;/td&gt;
&lt;td&gt;Jérémie Van Quynh&lt;/td&gt;
&lt;td&gt;Jérémie Van Quynh&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;&lt;td colspan="8"&gt;Composé à partir de prises de vues réelles, Tondo se veut une expérience visuelle et sonore où chaque spectateur se laisse emporter, au gré des paréidolies, dans un voyage hypnotique. Le détournement de sons du quotidien travaille à une expérience synesthétique pour favoriser le surgissement d’images mentales.&lt;/td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;&lt;strong&gt;LIGHT MATTER&lt;/strong&gt;&lt;/td&gt;
&lt;td&gt;Virgil Widrich&lt;/td&gt;
&lt;td&gt;Autriche&lt;/td&gt;
&lt;td&gt;2018&lt;/td&gt;
&lt;td&gt;Expérimental&lt;/td&gt;
&lt;td&gt;5'&lt;/td&gt;
&lt;td&gt;Virgil Widrich&lt;/td&gt;
&lt;td&gt;Sixpackfilm&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;&lt;td colspan="8"&gt;Cinq minutes de pure irritation de la rétine de l’œil humain. Tout commence par de faibles éclairs de lumière qui s’intensifient lentement en un staccato d’obscurité et de lumière, avec des contrastes extrêmes. En stimulant les photorécepteurs de l’œil, les impulsions se dirigent vers le cerveau, créant une illusion de couleur où il n’y a rien d’autre qu’un enfer de noir et blanc. Virgil Widrich fait référence à un phénomène décrit pour la première fois par deux scientifiques au XIXe siècle. L’effet est perçu différemment par chaque spectateur. Vous apercevrez peut-être des couleurs lointaines dans les profondeurs du stroboscope, ou vous vous souviendrez peut-être de l’esthétique du jeu des années 1980 avec des visuels clignotant en rouge et vert.&lt;/td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;&lt;strong&gt;LE SYSTÈME MIROIR&lt;/strong&gt;&lt;/td&gt;
&lt;td&gt;Eva Zornio&lt;/td&gt;
&lt;td&gt;Suisse&lt;/td&gt;
&lt;td&gt;2015&lt;/td&gt;
&lt;td&gt;Documentaire fiction&lt;/td&gt;
&lt;td&gt;17'16&lt;/td&gt;
&lt;td&gt;Elefantfilms&lt;/td&gt;
&lt;td&gt;Elefantfilms&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;&lt;td colspan="8"&gt;Des images s’échappent d’un rêve. Une étrange forêt de neurones où la jeune réalisatrice se perd chaque nuit. Un rêve qui rapproche cinéma et cerveau, souvenirs et expériences scientifiques. Elle cherche à comprendre le lien qui se tisse entre neurosciences et films. Un voyage poétique au cœur du système miroir, le mécanisme par lequel nous ressentons ou non de l’empathie pour autrui, qu’il soit réel ou sur un écran.&lt;/td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;&lt;strong&gt;CAPRICE EN COULEURS&lt;/strong&gt;&lt;/td&gt;
&lt;td&gt;Norman McLaren &amp;amp; Evelyn Lambart&lt;/td&gt;
&lt;td&gt;Canada&lt;/td&gt;
&lt;td&gt;1949&lt;/td&gt;
&lt;td&gt;Animation&lt;/td&gt;
&lt;td&gt;7'48&lt;/td&gt;
&lt;td&gt;Office National du film du Canada&lt;/td&gt;
&lt;td&gt;Agence du court métrage&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;&lt;td colspan="8"&gt;Caprice en couleurs (Begone Dull Care) est un film abstrait d’animation canadien des réalisateurs Norman McLaren et Evelyn Lambart sorti en 1949. Utilisant la technique du film direct, McLaren et Lambart peignirent et grattèrent directement sur la pellicule 35 mm pour créer une musique visuelle à l’aide de la trame sonore extraite du répertoire du jazzman canadien Oscar Peterson. Le film est produit par l’Office national du film du Canada. En 2005, il est désigné comme une « Œuvre magistrale » par le Trust pour la préservation de l’audiovisuel du Canada.&lt;/td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;/tbody&gt;
&lt;/table&gt;</description></item><item><title>Retinotopy in CNN's implements Efficient Visual Search</title><link>https://laurentperrinet.github.io/publication/jeremie-24-ccn/</link><pubDate>Tue, 08 Aug 2023 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/jeremie-24-ccn/</guid><description>&lt;ul&gt;
&lt;li&gt;Read the corresponding paper
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;span class="article-metadata li-cite-author"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/jean-nicolas-j%C3%A9r%C3%A9mie/"&gt;Jean-Nicolas Jérémie&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/emmanuel-dauc%C3%A9/"&gt;Emmanuel Daucé&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;
&lt;/span&gt;
(2026).
&lt;a href="https://laurentperrinet.github.io/publication/jeremie-25/"&gt;Foveated Retinotopy Improves Classification and Localization in CNNs&lt;/a&gt;.
&lt;em&gt;Vision&lt;/em&gt;.
&lt;p&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://laurentperrinet.github.io/publication/jeremie-25/jeremie-25.pdf" target="_blank" rel="noopener"&gt;
PDF
&lt;/a&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publication/jeremie-25/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.3390/vision10020017" target="_blank" rel="noopener"&gt;
DOI
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://www.mdpi.com/2411-5150/10/2/17" target="_blank" rel="noopener"&gt;
URL&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://arxiv.org/abs/2402.15480" target="_blank" rel="noopener"&gt;
arXiv&lt;/a&gt;
&lt;/p&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Resilience to sensory uncertainty in the primary visual cortex</title><link>https://laurentperrinet.github.io/publication/ladret-23-cosyne/</link><pubDate>Thu, 09 Mar 2023 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/ladret-23-cosyne/</guid><description>&lt;ul&gt;
&lt;li&gt;This poster is presented in the following paper (published in Nature Comm Biology):
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;span class="article-metadata li-cite-author"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/hugo-ladret/"&gt;Hugo Ladret&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/nelson-cortes/"&gt;Nelson Cortes&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/lamyae-ikan/"&gt;Lamyae Ikan&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/fr%C3%A9d%C3%A9ric-chavane/"&gt;Frédéric Chavane&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/christian-casanova/"&gt;Christian Casanova&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;
&lt;/span&gt;
(2023).
&lt;a href="https://laurentperrinet.github.io/publication/ladret-23/"&gt;Cortical recurrence supports resilience to sensory variance in the primary visual cortex&lt;/a&gt;.
&lt;em&gt;Nature Communications Biology&lt;/em&gt;.
&lt;p&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://laurentperrinet.github.io/publication/ladret-23/ladret-23.pdf" target="_blank" rel="noopener"&gt;
PDF
&lt;/a&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publication/ladret-23/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1038/s42003-023-05042-3" target="_blank" rel="noopener"&gt;
DOI
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://figshare.com/articles/dataset/Data_for_Ladret_et_al_2023_Cortical_recurrence_supports_resilience_to_sensory_variance_in_the_primary_visual_cortex_/23366588" target="_blank" rel="noopener"&gt;
Dataset&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://github.com/hugoladret/variance-processing-V1" target="_blank" rel="noopener"&gt;
Code&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://www.nature.com/articles/s42003-023-05042-3" target="_blank" rel="noopener"&gt;
URL&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://www.biorxiv.org/content/10.1101/2021.03.30.437692" target="_blank" rel="noopener"&gt;
bioRxiv&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://hal.science/hal-04142490" target="_blank" rel="noopener"&gt;
HAL&lt;/a&gt;
&lt;/p&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Ultra-rapid visual search in natural images using active deep learning</title><link>https://laurentperrinet.github.io/publication/jeremie-22-areadne/</link><pubDate>Wed, 29 Jun 2022 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/jeremie-22-areadne/</guid><description>&lt;ul&gt;
&lt;li&gt;This work extends to natural scenes a previous work on visual search on a simplified task formulated in
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;span class="article-metadata li-cite-author"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/emmanuel-dauc%C3%A9/"&gt;Emmanuel Daucé&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/pierre-albig%C3%A8s/"&gt;Pierre Albigès&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;
&lt;/span&gt;
(2020).
&lt;a href="https://laurentperrinet.github.io/publication/dauce-20/"&gt;A dual foveal-peripheral visual processing model implements efficient saccade selection&lt;/a&gt;.
&lt;em&gt;Journal of Vision&lt;/em&gt;.
&lt;p&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publication/dauce-20/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1167/jov.20.8.22" target="_blank" rel="noopener"&gt;
DOI
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1101/725879" target="_blank" rel="noopener"&gt;
Pdf&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://github.com/laurentperrinet/WhereIsMyMNIST" target="_blank" rel="noopener"&gt;
Code&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://laurentperrinet.github.io/publication/dauce-20/" target="_blank" rel="noopener"&gt;
URL&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://www.biorxiv.org/content/10.1101/725879" target="_blank" rel="noopener"&gt;
bioRxiv&lt;/a&gt;
&lt;/p&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;li&gt;It is based on a first work on transfer learning and its application to a natural task :
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;span class="article-metadata li-cite-author"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/jean-nicolas-j%C3%A9r%C3%A9mie/"&gt;Jean-Nicolas Jérémie&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;
&lt;/span&gt;
(2023).
&lt;a href="https://laurentperrinet.github.io/publication/jeremie-23-ultra-fast-cat/"&gt;Ultra-Fast Image Categorization in biology and in neural models&lt;/a&gt;.
&lt;em&gt;Vision&lt;/em&gt;.
&lt;p&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publication/jeremie-23-ultra-fast-cat/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.3390/vision7020029" target="_blank" rel="noopener"&gt;
DOI
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://arxiv.org/abs/2205.03635" target="_blank" rel="noopener"&gt;
arXiv&lt;/a&gt;
&lt;/p&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;li&gt;in particular, we found retinotopic mapping to be adapted to that extension :
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;span class="article-metadata li-cite-author"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/jean-nicolas-j%C3%A9r%C3%A9mie/"&gt;Jean-Nicolas Jérémie&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/emmanuel-dauc%C3%A9/"&gt;Emmanuel Daucé&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;
&lt;/span&gt;
(2022).
&lt;a href="https://laurentperrinet.github.io/talk/2022-06-19-neuro-vision-retinotopic/"&gt;Retinotopic mapping improves the reliability of image classification&lt;/a&gt;.
&lt;em&gt;NeuroVision Workshop in conjunction with CVPR 2022&lt;/em&gt;.
&lt;p&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://laurentperrinet.github.io/talk/2022-06-19-neuro-vision-retinotopic/2022-06-19-neuro-vision-retinotopic.pdf" target="_blank" rel="noopener"&gt;
PDF
&lt;/a&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/talk/2022-06-19-neuro-vision-retinotopic/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://sites.google.com/uci.edu/neurovision2022/schedule" target="_blank" rel="noopener"&gt;
URL&lt;/a&gt;
&lt;/p&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;li&gt;Read the corresponding paper
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;span class="article-metadata li-cite-author"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/jean-nicolas-j%C3%A9r%C3%A9mie/"&gt;Jean-Nicolas Jérémie&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/emmanuel-dauc%C3%A9/"&gt;Emmanuel Daucé&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;
&lt;/span&gt;
(2026).
&lt;a href="https://laurentperrinet.github.io/publication/jeremie-25/"&gt;Foveated Retinotopy Improves Classification and Localization in CNNs&lt;/a&gt;.
&lt;em&gt;Vision&lt;/em&gt;.
&lt;p&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://laurentperrinet.github.io/publication/jeremie-25/jeremie-25.pdf" target="_blank" rel="noopener"&gt;
PDF
&lt;/a&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publication/jeremie-25/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.3390/vision10020017" target="_blank" rel="noopener"&gt;
DOI
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://www.mdpi.com/2411-5150/10/2/17" target="_blank" rel="noopener"&gt;
URL&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://arxiv.org/abs/2402.15480" target="_blank" rel="noopener"&gt;
arXiv&lt;/a&gt;
&lt;/p&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Des illusions aux hallucinations visuelles: une porte sur la perception</title><link>https://laurentperrinet.github.io/talk/2022-01-12-neuro-cercle/</link><pubDate>Wed, 12 Jan 2022 18:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2022-01-12-neuro-cercle/</guid><description>&lt;blockquote&gt;
&lt;p&gt;Nous aurons le plaisir d’échanger avec notre conférencier Laurent Perrinet et nous vous espérons nombreux. Pour situer le conférencier : &lt;a href="https://laurentperrinet.github.io/2019-05_illusions-visuelles/" target="_blank" rel="noopener"&gt;https://laurentperrinet.github.io/2019-05_illusions-visuelles/&lt;/a&gt;
« C&amp;rsquo;est toujours fascinant de voir ou de revoir des illusions visuelles. C&amp;rsquo;est encore plus fascinant de plonger dans leurs explications. »&lt;/p&gt;&lt;/blockquote&gt;</description></item><item><title>A Behavioral Receptive Field for Ocular Following in Monkeys: Spatial Summation and Its Spatial Frequency Tuning</title><link>https://laurentperrinet.github.io/publication/barthelemy-22/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/barthelemy-22/</guid><description/></item><item><title>Le jeu du cerveau et du hasard</title><link>https://laurentperrinet.github.io/publication/perrinet-21-hasard/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/perrinet-21-hasard/</guid><description>&lt;ul&gt;
&lt;li&gt;Ce texte est disponible dans cet article de &lt;a href="https://theconversation.com/le-jeu-du-cerveau-et-du-hasard-159388" target="_blank" rel="noopener"&gt;The Conversation&lt;/a&gt;.&lt;/li&gt;
&lt;li&gt;Une &lt;a href="https://laurentperrinet.github.io/2021_theconversation_hasard/" target="_blank" rel="noopener"&gt;version longue&lt;/a&gt; (et son &lt;a href="https://github.com/laurentperrinet/2021_theconversation_hasard" target="_blank" rel="noopener"&gt;code&lt;/a&gt;) sont aussi disponibles.&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Des illusions aux hallucinations visuelles: une porte sur la perception</title><link>https://laurentperrinet.github.io/talk/2020-01-20-atelier-sciences-cinema/</link><pubDate>Mon, 20 Jan 2020 10:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2020-01-20-atelier-sciences-cinema/</guid><description>&lt;p&gt;
&lt;figure &gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="" srcset="
/talk/2020-01-20-atelier-sciences-cinema/@laurentperrinet_1284791644240347138_tweetcapture_hu_bc36551843981bd2.webp 400w,
/talk/2020-01-20-atelier-sciences-cinema/@laurentperrinet_1284791644240347138_tweetcapture_hu_f2f8c246e495c5b6.webp 760w,
/talk/2020-01-20-atelier-sciences-cinema/@laurentperrinet_1284791644240347138_tweetcapture_hu_a72f7b0e8ee204fa.webp 1200w"
src="https://laurentperrinet.github.io/talk/2020-01-20-atelier-sciences-cinema/@laurentperrinet_1284791644240347138_tweetcapture_hu_bc36551843981bd2.webp"
width="598"
height="357"
loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;/figure&gt;
&lt;div
style="position: relative; padding-bottom: 56.25%; height: 0; overflow: hidden;"&gt;
&lt;iframe
src="https://player.vimeo.com/video/398661322?dnt=0"
style="position: absolute; top: 0; left: 0; width: 100%; height: 100%; border:0;" allow="fullscreen"&gt;
&lt;/iframe&gt;
&lt;/div&gt;
&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;ÇA TOURNE a été sélectionné pour participer à la compétition du « Alexandre Trauner ART/Film Festival » (Szolnok, Hongrie) : &lt;a href="http://www.ataff.hu/" target="_blank" rel="noopener"&gt;http://www.ataff.hu/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;visible aux Soirée Courts Métrages Ciné Rencontre de la Ville de Berre l&amp;rsquo;Étang &lt;a href="https://www.berreletang.fr/soiree-courts-metrages?periode=2021-04-30%2017%3A23%3A26" target="_blank" rel="noopener"&gt;https://www.berreletang.fr/soiree-courts-metrages?periode=2021-04-30%2017%3A23%3A26&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;ÇA TOURNE de Camille Goujon, a été sélectionné au 27ème Festival national du film d&amp;rsquo;animation de Rennes Métropole, dans la catégorie Autoproductions du 7 au 11 octobre 2021 &lt;a href="http://festival-film-animation.fr/" target="_blank" rel="noopener"&gt;http://festival-film-animation.fr/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;le court-métrage a été sélectionné pour participer au « Happy Valley Animation Festival » (Pennsylvanie, USA) : &lt;a href="https://happyvalleyanimationfestival.org/" target="_blank" rel="noopener"&gt;https://happyvalleyanimationfestival.org/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Le film &amp;ldquo;ÇA TOURNE&amp;rdquo; a été sélectionné pour faire partie de la compétition catégorie «FILMS SCOLAIRES&amp;quot; diffusée du 4 au 7 novembre 2020 dans le cadre du festival &amp;ldquo;7ème Art Jeunes Talent! : &lt;a href="http://www.festivaltournezjeunesse.com" target="_blank" rel="noopener"&gt;http://www.festivaltournezjeunesse.com&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Dans le cadre d&amp;rsquo;un projet Région (APERLA) les élèves de seconde Bac Pro Menuisiers agenceurs ont conçu ce film sous la direction de leur professeur Mme Bomont et sous la direction artistique de &lt;a href="https://www.youtube.com/user/camillegoujon1/videos" target="_blank" rel="noopener"&gt;Camille Goujon&lt;/a&gt;, artiste et cinéaste d&amp;rsquo;animation &lt;a href="https://www.domaine-eguilles.fr/realisation-collective-de-lyceens-sous-la-direction-artistique-de-camille-goujon-artiste-et-cineaste-d-animation" target="_blank" rel="noopener"&gt;https://www.domaine-eguilles.fr/realisation-collective-de-lyceens-sous-la-direction-artistique-de-camille-goujon-artiste-et-cineaste-d-animation&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Ce court métrage fait partie des 7 films réalisés dans le cadre des « Ateliers de réalisation Cinésciences » proposés par l’association Polly Maggoo &lt;a href="http://festivalrisc.org/films-dateliers/" target="_blank" rel="noopener"&gt;http://festivalrisc.org/films-dateliers/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;ref sur &lt;a href="http://www.lussasdoc.org/film-ca_tourne-1,53288.html" target="_blank" rel="noopener"&gt;http://www.lussasdoc.org/film-ca_tourne-1,53288.html&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Le texte de cette présentation est reprise dans cet article de &lt;a href="https://laurentperrinet.github.io/publication/perrinet-19-temps/" target="_blank" rel="noopener"&gt;The Conversation&lt;/a&gt; (&lt;a href="https://theconversation.com/temps-et-cerveau-comment-notre-perception-nous-fait-voyager-dans-le-temps-127567" target="_blank" rel="noopener"&gt;lien direct&lt;/a&gt;).&lt;/li&gt;
&lt;li&gt;Voir la @ &lt;a href="https://laurentperrinet.github.io/post/2019-10-07_neurostories/"&gt;présentation au NeuroStories&lt;/a&gt; sur un thème similaire&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Should I stay or should I go? Humans adapt to the volatility of visual motion properties, and know about it</title><link>https://laurentperrinet.github.io/talk/2019-05-23-neurofrance/</link><pubDate>Thu, 23 May 2019 01:30:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2019-05-23-neurofrance/</guid><description>&lt;ul&gt;
&lt;li&gt;See the final publication @
&lt;div class="view-list view-list-item"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;a href="https://laurentperrinet.github.io/publication/pasturel-montagnini-perrinet-20/" &gt;Humans adapt their anticipatory eye movements to the volatility of visual motion properties&lt;/a&gt;
&lt;div class="article-metadata"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/chlo%C3%A9-pasturel/"&gt;Chloé Pasturel&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/anna-montagnini/"&gt;Anna Montagnini&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;
&lt;/div&gt;
&lt;div class="btn-links"&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publication/pasturel-montagnini-perrinet-20/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1371/journal.pcbi.1007438" target="_blank" rel="noopener"&gt;
DOI
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://www.insb.cnrs.fr/fr/cnrsinfo/la-reponse-du-cerveau-aux-changements-de-lenvironnement-sensoriel" target="_blank" rel="noopener"&gt;
Press&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://www.biorxiv.org/content/10.1101/784116v3.full.pdf" target="_blank" rel="noopener"&gt;
Pdf&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://github.com/laurentperrinet/PasturelMontagniniPerrinet2020" target="_blank" rel="noopener"&gt;
Code&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://hal.science/hal-02394142" target="_blank" rel="noopener"&gt;
URL&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://www.biorxiv.org/content/10.1101/784116" target="_blank" rel="noopener"&gt;
bioRxiv&lt;/a&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;li&gt;This is part of the &lt;a href="https://laurentperrinet.github.io/post/2019-05-23-neurofrance/"&gt;Active Inference symposium&lt;/a&gt; @ &lt;a href="https://www.neurosciences.asso.fr/V2/colloques/SN19/" target="_blank" rel="noopener"&gt;NeuroFrance&lt;/a&gt; SYMPOSIUM, Room 7
23.05.2019, 11:00 &amp;ndash; 13:00&lt;/li&gt;
&lt;li&gt;in french: Principes et psychophysique de l´Inférence Active dans l&amp;rsquo;estimation d&amp;rsquo;un biais dynamique et volatile de probabilité&lt;/li&gt;
&lt;li&gt;previous talk @ &lt;a href="https://laurentperrinet.github.io/talk/2016-10-13-law/"&gt;LAW, Lyon&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;previous talk @ &lt;a href="https://laurentperrinet.github.io/talk/2018-02-01-bcp-invibe-fest/"&gt;INVIBE FEST, Paris&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;previous talk @ &lt;a href="https://laurentperrinet.github.io/talk/2018-04-05-bcp-talk/"&gt;Brain workshop, Marseille&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;previous talk @ &lt;a href="https://laurentperrinet.github.io/talk/2019-01-18-laconeu/"&gt;LACONEU, Chile&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;previous talk @ &lt;a href="https://laurentperrinet.github.io/talk/2019-04-05-bbcp-causal-kickoff/"&gt;CAUSAL Kick-off, Marseille&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Des illusions aux hallucinations visuelles: une porte sur la perception</title><link>https://laurentperrinet.github.io/talk/2019-04-18-jnlf/</link><pubDate>Thu, 18 Apr 2019 13:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2019-04-18-jnlf/</guid><description>&lt;ul&gt;
&lt;li&gt;Le texte de cette présentation est reprise dans cet article de &lt;a href="https://laurentperrinet.github.io/publication/perrinet-19-temps/" target="_blank" rel="noopener"&gt;The Conversation&lt;/a&gt; (&lt;a href="https://theconversation.com/temps-et-cerveau-comment-notre-perception-nous-fait-voyager-dans-le-temps-127567" target="_blank" rel="noopener"&gt;lien direct&lt;/a&gt;).&lt;/li&gt;
&lt;li&gt;Voir la @ &lt;a href="https://laurentperrinet.github.io/post/2019-10-07_neurostories/"&gt;présentation au NeuroStories&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Should I stay or should I go? Adaption of human observers to the volatility of visual inputs</title><link>https://laurentperrinet.github.io/talk/2019-04-05-bbcp-causal-kickoff/</link><pubDate>Fri, 05 Apr 2019 15:45:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2019-04-05-bbcp-causal-kickoff/</guid><description>&lt;ul&gt;
&lt;li&gt;See the final publication @
&lt;div class="view-list view-list-item"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;a href="https://laurentperrinet.github.io/publication/pasturel-montagnini-perrinet-20/" &gt;Humans adapt their anticipatory eye movements to the volatility of visual motion properties&lt;/a&gt;
&lt;div class="article-metadata"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/chlo%C3%A9-pasturel/"&gt;Chloé Pasturel&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/anna-montagnini/"&gt;Anna Montagnini&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;
&lt;/div&gt;
&lt;div class="btn-links"&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publication/pasturel-montagnini-perrinet-20/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1371/journal.pcbi.1007438" target="_blank" rel="noopener"&gt;
DOI
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://www.insb.cnrs.fr/fr/cnrsinfo/la-reponse-du-cerveau-aux-changements-de-lenvironnement-sensoriel" target="_blank" rel="noopener"&gt;
Press&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://www.biorxiv.org/content/10.1101/784116v3.full.pdf" target="_blank" rel="noopener"&gt;
Pdf&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://github.com/laurentperrinet/PasturelMontagniniPerrinet2020" target="_blank" rel="noopener"&gt;
Code&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://hal.science/hal-02394142" target="_blank" rel="noopener"&gt;
URL&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://www.biorxiv.org/content/10.1101/784116" target="_blank" rel="noopener"&gt;
bioRxiv&lt;/a&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;li&gt;previous talk @ &lt;a href="https://laurentperrinet.github.io/talk/2016-10-13-law/"&gt;LAW, Lyon&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;previous talk @ &lt;a href="https://laurentperrinet.github.io/talk/2018-02-01-bcp-invibe-fest/"&gt;INVIBE FEST, Paris&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;previous talk @ &lt;a href="https://laurentperrinet.github.io/talk/2018-04-05-bcp-talk/"&gt;Brain workshop, Marseille&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;previous talk @ &lt;a href="https://laurentperrinet.github.io/talk/2019-01-18-laconeu/"&gt;LACONEU, Chile&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;next talk @ &lt;a href="https://laurentperrinet.github.io/talk/2019-05-23-neurofrance/"&gt;NeuroFrance, Marseille&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Effet de La Variabilité de La Vitesse Sur Le Mouvement de Poursuite Oculaire Lente et Sur La Perception de La Vitesse</title><link>https://laurentperrinet.github.io/publication/mansour-pour-19-thesis/</link><pubDate>Mon, 01 Apr 2019 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/mansour-pour-19-thesis/</guid><description/></item><item><title>Should I stay or should I go? Adaption of human observers to the volatility of visual inputs</title><link>https://laurentperrinet.github.io/talk/2019-01-18-laconeu/</link><pubDate>Fri, 18 Jan 2019 10:45:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2019-01-18-laconeu/</guid><description>&lt;ul&gt;
&lt;li&gt;See the final publication @
&lt;div class="view-list view-list-item"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;a href="https://laurentperrinet.github.io/publication/pasturel-montagnini-perrinet-20/" &gt;Humans adapt their anticipatory eye movements to the volatility of visual motion properties&lt;/a&gt;
&lt;div class="article-metadata"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/chlo%C3%A9-pasturel/"&gt;Chloé Pasturel&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/anna-montagnini/"&gt;Anna Montagnini&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;
&lt;/div&gt;
&lt;div class="btn-links"&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publication/pasturel-montagnini-perrinet-20/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1371/journal.pcbi.1007438" target="_blank" rel="noopener"&gt;
DOI
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://www.insb.cnrs.fr/fr/cnrsinfo/la-reponse-du-cerveau-aux-changements-de-lenvironnement-sensoriel" target="_blank" rel="noopener"&gt;
Press&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://www.biorxiv.org/content/10.1101/784116v3.full.pdf" target="_blank" rel="noopener"&gt;
Pdf&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://github.com/laurentperrinet/PasturelMontagniniPerrinet2020" target="_blank" rel="noopener"&gt;
Code&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://hal.science/hal-02394142" target="_blank" rel="noopener"&gt;
URL&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://www.biorxiv.org/content/10.1101/784116" target="_blank" rel="noopener"&gt;
bioRxiv&lt;/a&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;li&gt;previous talk @ &lt;a href="https://laurentperrinet.github.io/talk/2016-10-13-law/"&gt;LAW, Lyon&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;previous talk @ &lt;a href="https://laurentperrinet.github.io/talk/2018-02-01-bcp-invibe-fest/"&gt;INVIBE FEST, Paris&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;previous talk @ &lt;a href="https://laurentperrinet.github.io/talk/2018-04-05-bcp-talk/"&gt;Brain workshop, Marseille&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;next talk @ &lt;a href="https://laurentperrinet.github.io/talk/2019-04-05-bbcp-causal-kickoff/"&gt;CAUSAL Kick-off, Marseille&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Illusions et hallucinations visuelles : une porte sur la perception</title><link>https://laurentperrinet.github.io/publication/perrinet-19-illusions/</link><pubDate>Tue, 01 Jan 2019 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/perrinet-19-illusions/</guid><description>&lt;ul&gt;
&lt;li&gt;Ce texte est disponible dans cet article de &lt;a href="https://theconversation.com/temps-et-cerveau-comment-notre-perception-nous-fait-voyager-dans-le-temps-127567" target="_blank" rel="noopener"&gt;The Conversation&lt;/a&gt;.&lt;/li&gt;
&lt;li&gt;Voir la @ &lt;a href="https://laurentperrinet.github.io/post/2019-10-07_neurostories/"&gt;présentation au NeuroStories&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>La modélisation biomorphique de la perception visuelle</title><link>https://laurentperrinet.github.io/talk/2018-10-11-bio-morphisme/</link><pubDate>Thu, 11 Oct 2018 18:30:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2018-10-11-bio-morphisme/</guid><description>&lt;h2 id="in-la-modélisation-de-la-genèse-physico-mathématique-du-vivant"&gt;in &amp;ldquo;La modélisation de la genèse physico-mathématique du vivant&amp;rdquo;&lt;/h2&gt;
&lt;h2 id="biomorphisme-et-creation-artistique-session-3"&gt;BIOMORPHISME ET CREATION ARTISTIQUE – Session 3&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;Date&lt;br&gt;
11 Octobre 2018&lt;/li&gt;
&lt;li&gt;Atelier&lt;br&gt;
Séminaire/workshop organisé dans le cadre du projet Biomorphisme.
Approches sensibles et conceptuelles des formes du vivant
&lt;a href="http://lesa.univ-amu.fr/?q=node/391" target="_blank" rel="noopener"&gt;http://lesa.univ-amu.fr/?q=node/391&lt;/a&gt; &lt;a href="http://centregranger.cnrs.fr" target="_blank" rel="noopener"&gt;http://centregranger.cnrs.fr&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Location&lt;br&gt;
Bâtiment Egger, dans la salle E 215 (2ème étage côté voie ferrée) -
3 avenue R. Schuman - Aix-en-Provence&lt;/li&gt;
&lt;li&gt;Visuels&lt;br&gt;
&lt;a href="https://laurentperrinet.github.io/sciblog/files/2018-10-11_BioMorphisme.html" target="_blank" rel="noopener"&gt;HTML&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Organisation&lt;br&gt;
Jean Arnaud, PR arts plastiques au LESA-AMU ; Julien Bernard, MCF
philosophe des sciences au Centre GG Granger-AMU ; Sylvie Pic,
artiste&lt;/li&gt;
&lt;li&gt;Résumé&lt;br&gt;
La vision utilise un faisceau d&amp;rsquo;informations de différentes qualités
pour atteindre une perception unifiée du monde environnant. Elle
interagit avec lui en créant son propre modèle génératif de sa
structure physico-mathématique. Avec &lt;a href="https://laurentperrinet.github.io/author/etienne-rey/" target="_blank" rel="noopener"&gt;Etienne
Rey&lt;/a&gt; de l&amp;rsquo;atelier Ondes Parallèles,
nous avons utilisé lors de plusieurs projets art-science (voir
&lt;a href="https://github.com/NaturalPatterns" target="_blank" rel="noopener"&gt;https://github.com/NaturalPatterns&lt;/a&gt;) des installations permettant
de manipuler explicitement des composantes de ce flux d&amp;rsquo;information
et de révéler des ambiguités dans notre perception. Dans
l&amp;rsquo;installation
&lt;a href="https://github.com/NaturalPatterns/Tropique" target="_blank" rel="noopener"&gt;Tropique&lt;/a&gt;, des
faisceaux de lames lumineuses sont arrangés dans l&amp;rsquo;espace assombri
de l&amp;rsquo;installation. Les spectateurs les observent grâce à leur
interaction avec une brume invisible qui est diffusée dans l&amp;rsquo;espace.
L&amp;rsquo;ensemble des faisceaux évolue comme autant de lames lumineuses à
partir de 6 video-projecteurs placés dans l&amp;rsquo;espace de
l&amp;rsquo;installation, suivant une dynamique autonome. En même temps, la
position des spectateurs est captée et permet d&amp;rsquo;alterner entre une
vision de ces sculptures d&amp;rsquo;un point de vue introceptif à un point de
vue exteroceptif. Dans «&lt;a href="https://github.com/NaturalPatterns/elasticite" target="_blank" rel="noopener"&gt;Trame
Élasticité&lt;/a&gt;», 25
parallélépipèdes de miroirs (3m de haut) sont arrangés verticalement
sur une ligne horizontale. Ces lames sont rotatives et leurs
mouvements est synchronisé. Suivant la dyamique qui est imposé à ces
lames, la perception de l’espace environnent fluctue conduisant à
recomposer l’espace de la concentration à l’expansion, ou encore à
générer un surface semblant transparente ou inverser la visons de
ce qui est située devant et derrière l’observateur. Enfin, dans
«&lt;a href="https://github.com/NaturalPatterns/TRAMES" target="_blank" rel="noopener"&gt;Trames&lt;/a&gt;», nous
explorons l&amp;rsquo;interaction de séries périodiques de points placées sur
des surfaces transparentes. À partir de premières expérimentations
utilisant une technique novatrice de sérigraphie, ces trames de
points sont placées afin de faire émerger des structures selon le
point de vue du spectateur. Ce qui est en jeu ici c’est l’émergence
de l’apparition de motifs virtuels résultat de la relation entre une
réalité physique, la grandeur et l’ordonnancement de trames et notre
physiologie qui conduit à cette état de perception. Lorsqu’on est
fasse à ces motifs ce qui saute au yeux plus que le motif réel c’est
sa résultante, instable et éphémère qui fait apparaitre une richesse
de figures géométriques qui se transforment et évoluent en fonction
du temps d’observation et du point de vue. Sur ce principe de
dispositif optique, le travail de chacun des motifs, lié à un
séquençage de trames conduit à faire apparaitre une composition et
des émergences de formes spécifiques. L’expérience de perception de
chacun des motifs explore les notions d’instabilité, de flux,
d’émergences … dont l’expérience donne à entrevoir des formes que
l’on retrouve dans la nature ou les phénomènes naturels: le dessin
du pelage d’un zèbre, une accumulation de bulles de savons, ou plus
généralement dans les compositions chimiques issue de la théorie de
la morphogénèse de Turing. De manière générale, nous montrerons ici
les différentes méthodes utilisées, comme l&amp;rsquo;utilisation des limites
perceptives, et aussi les résultats apportés par une telle
collaboration.&lt;/li&gt;
&lt;li&gt;Mots-Clés&lt;br&gt;
art cinétique ; science ; vision ; perception ; modèle interne&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Smooth Pursuit Eye Movements and Learning : Role of Motion Probability and Reinforcement Contingencies</title><link>https://laurentperrinet.github.io/publication/damasse-18-thesis/</link><pubDate>Mon, 11 Jun 2018 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/damasse-18-thesis/</guid><description/></item><item><title>ANEMO: Quantitative tools for the ANalysis of Eye MOvements</title><link>https://laurentperrinet.github.io/publication/pasturel-18-anemo/</link><pubDate>Mon, 01 Jan 2018 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/pasturel-18-anemo/</guid><description>&lt;ul&gt;
&lt;li&gt;see a write-up in &amp;ldquo;&lt;a href="https://laurentperrinet.github.io/publication/pasturel-montagnini-perrinet-20/" target="_blank" rel="noopener"&gt;Humans adapt their anticipatory eye movements to the volatility of visual motion properties&lt;/a&gt;&amp;rdquo;&lt;/li&gt;
&lt;li&gt;as presented at &lt;a href="https://eyemovements.sciencesconf.org/" target="_blank" rel="noopener"&gt;https://eyemovements.sciencesconf.org/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;get the &lt;a href="https://github.com/invibe/ANEMO/raw/master/2018-05-04_Poster_Grenoble/Pasturel_etal2018_grenoble.pdf" target="_blank" rel="noopener"&gt;poster&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;code : &lt;a href="https://github.com/invibe/ANEMO/" target="_blank" rel="noopener"&gt;https://github.com/invibe/ANEMO/&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Estimating and anticipating a dynamic probabilistic bias in visual motion direction</title><link>https://laurentperrinet.github.io/publication/pasturel-18-grenoble/</link><pubDate>Mon, 01 Jan 2018 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/pasturel-18-grenoble/</guid><description>&lt;ul&gt;
&lt;li&gt;see a write-up in &amp;ldquo;&lt;a href="https://laurentperrinet.github.io/publication/pasturel-montagnini-perrinet-20/" target="_blank" rel="noopener"&gt;Humans adapt their anticipatory eye movements to the volatility of visual motion properties&lt;/a&gt;&amp;rdquo;&lt;/li&gt;
&lt;li&gt;as presented at &lt;a href="https://eyemovements.sciencesconf.org/" target="_blank" rel="noopener"&gt;https://eyemovements.sciencesconf.org/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;get the &lt;a href="https://github.com/chloepasturel/AnticipatorySPEM/raw/master/Poster/2018-06-05_Poster_Workshop_Grenoble/Pasturel_etal2018grenoble.pdf" target="_blank" rel="noopener"&gt;poster&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;code : &lt;a href="https://github.com/chloepasturel/AnticipatorySPEM/" target="_blank" rel="noopener"&gt;https://github.com/chloepasturel/AnticipatorySPEM/&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Estimating and anticipating a dynamic probabilistic bias in visual motion direction</title><link>https://laurentperrinet.github.io/publication/pasturel-18/</link><pubDate>Mon, 01 Jan 2018 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/pasturel-18/</guid><description>&lt;ul&gt;
&lt;li&gt;see a write-up in &amp;ldquo;&lt;a href="https://laurentperrinet.github.io/publication/pasturel-montagnini-perrinet-20/" target="_blank" rel="noopener"&gt;Humans adapt their anticipatory eye movements to the volatility of visual motion properties&lt;/a&gt;&amp;rdquo;&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Speed uncertainty and motion perception with naturalistic random textures</title><link>https://laurentperrinet.github.io/publication/mansour-18-vss/</link><pubDate>Mon, 01 Jan 2018 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/mansour-18-vss/</guid><description/></item><item><title>Estimating and anticipating a dynamic probabilistic bias in visual motion direction</title><link>https://laurentperrinet.github.io/publication/pasturel-17-gdr/</link><pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/pasturel-17-gdr/</guid><description>&lt;ul&gt;
&lt;li&gt;see a write-up in &amp;ldquo;&lt;a href="https://laurentperrinet.github.io/publication/pasturel-montagnini-perrinet-20/" target="_blank" rel="noopener"&gt;Humans adapt their anticipatory eye movements to the volatility of visual motion properties&lt;/a&gt;&amp;rdquo;&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>How the dynamics of human smooth pursuit is influenced by speed uncertainty</title><link>https://laurentperrinet.github.io/publication/mansour-17-ecvp/</link><pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/mansour-17-ecvp/</guid><description/></item><item><title>Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit</title><link>https://laurentperrinet.github.io/publication/mansour-17-gdr/</link><pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/mansour-17-gdr/</guid><description/></item><item><title>Les illusions visuelles, un révélateur du fonctionnement de notre cerveau</title><link>https://laurentperrinet.github.io/talk/2016-04-28-mejanes/</link><pubDate>Thu, 28 Apr 2016 18:30:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2016-04-28-mejanes/</guid><description>&lt;h1 id="les-illusions-visuelles-un-révélateur-du-fonctionnement-de-notre-cerveau"&gt;Les illusions visuelles, un révélateur du fonctionnement de notre cerveau&lt;/h1&gt;
&lt;h2 id="cycle-de-conférences-tous-connectés-bibliothèque-de-méjanes"&gt;Cycle de conférences &amp;ldquo;Tous connectés&amp;rdquo;, Bibliothèque de Méjanes&lt;/h2&gt;
&lt;p&gt;
&lt;figure &gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="conférence tout public à la Bibliothèque de Méjanes (Aix-en-Provence, Avril 2016)" srcset="
/talk/2016-04-28-mejanes/featured_hu_f4fea1390f66dd51.webp 400w,
/talk/2016-04-28-mejanes/featured_hu_2b7dfd23347bf2b3.webp 760w,
/talk/2016-04-28-mejanes/featured_hu_cd23bca54a13f855.webp 1200w"
src="https://laurentperrinet.github.io/talk/2016-04-28-mejanes/featured_hu_f4fea1390f66dd51.webp"
width="570"
height="760"
loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;/figure&gt;
&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Date&lt;br&gt;
28 Avril 2016&lt;/li&gt;
&lt;li&gt;Location&lt;br&gt;
Bibliothèque de Méjanes&lt;/li&gt;
&lt;li&gt;Visuels&lt;br&gt;
&lt;a href="https://laurentperrinet.github.io/sciblog/files/2016-04-28_mejanes/" target="_blank" rel="noopener"&gt;HTML&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Les illusions visuelles, un révélateur du fonctionnement de notre cerveau</title><link>https://laurentperrinet.github.io/talk/2016-04-25-polly-maggoo/</link><pubDate>Mon, 25 Apr 2016 09:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2016-04-25-polly-maggoo/</guid><description>&lt;h1 id="les-illusions-visuelles-un-révélateur-du-fonctionnement-de-notre-cerveau"&gt;Les illusions visuelles, un révélateur du fonctionnement de notre cerveau&lt;/h1&gt;
&lt;h2 id="cinésciences-collège-clair-soleil"&gt;Cinésciences, collège Clair Soleil&lt;/h2&gt;
&lt;p&gt;L&amp;rsquo;Association Polly Maggoo &lt;a href="http://www.pollymaggoo.org/" target="_blank" rel="noopener"&gt;http://www.pollymaggoo.org/&lt;/a&gt; met en place
tout le long de l’année, des actions de culture scientifique et
artistique en direction des collèges et des lycées, les &lt;em&gt;Cinésciences&lt;/em&gt;,
au cours desquelles l&amp;rsquo;association programme des films à caractère
scientifique, au sein d’établissements scolaires. Les projections se
déroulent en présence de chercheurs et/ou de cinéastes dans la
perspective d’un développement de la culture cinématographique et
scientifique en direction des publics scolaires.&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Date&lt;br&gt;
25 Avril 2016&lt;/li&gt;
&lt;li&gt;Location&lt;br&gt;
collège Clair Soleil, Marseille&lt;/li&gt;
&lt;li&gt;Visuels&lt;br&gt;
&lt;a href="https://laurentperrinet.github.io/sciblog/files/2016-04-25_pollymagoo/" target="_blank" rel="noopener"&gt;HTML&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>A dynamic model for decoding direction and orientation in macaque primary visual cortex</title><link>https://laurentperrinet.github.io/publication/taouali-15-vss/</link><pubDate>Fri, 01 Jan 2016 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/taouali-15-vss/</guid><description>&lt;ul&gt;
&lt;li&gt;see a follow-up in this &lt;a href="https://laurentperrinet.github.io/publication/taouali-16-areadne/"&gt;poster&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;This is a followup in &lt;a href="https://laurentperrinet.github.io/publication/perrinet-12-pred/"&gt;Perrinet et al, 2012&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>A dynamic model for decoding direction and orientation in macaque primary visual cortex</title><link>https://laurentperrinet.github.io/publication/taouali-16-areadne/</link><pubDate>Fri, 01 Jan 2016 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/taouali-16-areadne/</guid><description/></item><item><title>Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit</title><link>https://laurentperrinet.github.io/publication/mansour-16-ecvp/</link><pubDate>Fri, 01 Jan 2016 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/mansour-16-ecvp/</guid><description/></item><item><title>Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit</title><link>https://laurentperrinet.github.io/publication/mansour-16-gdr/</link><pubDate>Fri, 01 Jan 2016 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/mansour-16-gdr/</guid><description/></item><item><title>Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit</title><link>https://laurentperrinet.github.io/publication/mansour-16-sfn/</link><pubDate>Fri, 01 Jan 2016 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/mansour-16-sfn/</guid><description/></item><item><title>Motion-based prediction with neuromorphic hardware</title><link>https://laurentperrinet.github.io/talk/2015-11-05-chile/</link><pubDate>Thu, 05 Nov 2015 13:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2015-11-05-chile/</guid><description/></item><item><title>Visual motion processing and human tracking behavior</title><link>https://laurentperrinet.github.io/publication/montagnini-15-bicv/</link><pubDate>Sun, 01 Nov 2015 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/montagnini-15-bicv/</guid><description>&lt;ul&gt;
&lt;li&gt;Appeared in this book:
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;span class="article-metadata li-cite-author"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/gabriel-crist%C3%B3bal/"&gt;Gabriel Cristóbal&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/matthias-s-keil/"&gt;Matthias S Keil&lt;/a&gt;&lt;/span&gt;
&lt;/span&gt;
(2015).
&lt;a href="https://laurentperrinet.github.io/publication/cristobal-perrinet-keil-15-bicv/"&gt;Biologically Inspired Computer Vision&lt;/a&gt;.
&lt;p&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publication/cristobal-perrinet-keil-15-bicv/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1002/9783527680863" target="_blank" rel="noopener"&gt;
DOI
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://bicv.github.io/toc/" target="_blank" rel="noopener"&gt;
Code&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://onlinelibrary.wiley.com/book/10.1002/9783527680863" target="_blank" rel="noopener"&gt;
URL&lt;/a&gt;
&lt;/p&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Motion-based prediction with neuromorphic hardware</title><link>https://laurentperrinet.github.io/talk/2015-10-07-gdr-bio-comp/</link><pubDate>Wed, 07 Oct 2015 13:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2015-10-07-gdr-bio-comp/</guid><description/></item><item><title>Sparse Coding Of Natural Images Using A Prior On Edge Co-Occurences</title><link>https://laurentperrinet.github.io/publication/perrinet-15-eusipco/</link><pubDate>Sat, 01 Aug 2015 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/perrinet-15-eusipco/</guid><description/></item><item><title>Edge co-occurrences can account for rapid categorization of natural versus animal images</title><link>https://laurentperrinet.github.io/publication/perrinet-bednar-15/</link><pubDate>Thu, 01 Jan 2015 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/perrinet-bednar-15/</guid><description>&lt;ul&gt;
&lt;li&gt;&lt;a href="https://www.nature.com/article-assets/npg/srep/2015/150622/srep11400/extref/srep11400-s1.pdf" target="_blank" rel="noopener"&gt;supplementary information&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="PerrinetBednar15supplementary.pdf"&gt;supplementary material&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
&lt;h1 id="a-study-of-how-people-can-quickly-spot-animals-by-sight-is-helping-uncover-the-workings-of-the-human-brain"&gt;A study of how people can quickly spot animals by sight is helping uncover the workings of the human brain.&lt;/h1&gt;
&lt;p&gt;Scientists examined why volunteers who were shown hundreds of pictures - some with animals and some without - were able to detect animals in as little as one-tenth of a second.
They found that one of the first parts of the brain to process visual information - the primary visual cortex - can control this fast response.
More complex parts of the brain are not required at this stage, contrary to what was previously thought.
&lt;figure &gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="" srcset="
/publication/perrinet-bednar-15/@laurentperrinet_613011086829162497_tweetcapture_hu_125d8539cd41d841.webp 400w,
/publication/perrinet-bednar-15/@laurentperrinet_613011086829162497_tweetcapture_hu_532ed384f1f0d15e.webp 760w,
/publication/perrinet-bednar-15/@laurentperrinet_613011086829162497_tweetcapture_hu_99fa4b5da7ee5119.webp 1200w"
src="https://laurentperrinet.github.io/publication/perrinet-bednar-15/@laurentperrinet_613011086829162497_tweetcapture_hu_125d8539cd41d841.webp"
width="598"
height="190"
loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;/figure&gt;
&lt;figure id="figure-edge-co-occurrences-a-an-example-image-with-the-list-of-extracted-edges-overlaid-each-edge-is-represented-by-a-red-line-segment-which-represents-its-position-center-of-segment-orientation-and-scale-length-of-segment-we-controlled-the-quality-of-the-reconstruction-from-the-edge-information-such-that-the-residual-energy-was-less-than-5-b-the-relationship-between-a-reference-edge-a-and-another-edge-b-can-be-quantified-in-terms-of-the-difference-between-their-orientations-theta-ratio-of-scale-sigma-distance-d-between-their-centers-and-difference-of-azimuth-angular-location-phi-additionally-we-define-psiphi---theta2-which-is-symmetric-with-respect-to-the-choice-of-the-reference-edge-in-particular-psi0-for-co-circular-edges--see-text-as-incitetgeisler01-edges-outside-a-central-circular-mask-are-discarded-in-the-computation-of-the-statistics-to-avoid-artifacts-image-credit-andrew-shiva-creative-commons-attribution-share-alike-30-unported-licensehttpscommonswikimediaorgwikifileelephant_28loxodonta_africana29_05jpg-this-is-used-to-compute-the-chevron-map-in-figure2"&gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="Edge co-occurrences **(A)** An example image with the list of extracted edges overlaid. Each edge is represented by a red line segment which represents its position (center of segment), orientation, and scale (length of segment). We controlled the quality of the reconstruction from the edge information such that the residual energy was less than 5%. **(B)** The relationship between a reference edge *A* and another edge *B* can be quantified in terms of the difference between their orientations $\theta$, ratio of scale $\sigma$, distance $d$ between their centers, and difference of azimuth (angular location) $\phi$. Additionally, we define $\psi=\phi - \theta/2$, which is symmetric with respect to the choice of the reference edge; in particular, $\psi=0$ for co-circular edges. % (see text). As in~\citet{Geisler01}, edges outside a central circular mask are discarded in the computation of the statistics to avoid artifacts. (Image credit: [Andrew Shiva, Creative Commons Attribution-Share Alike 3.0 Unported license](https://commons.wikimedia.org/wiki/File:Elephant_/%28Loxodonta_Africana/%29_05.jpg)). This is used to compute the chevron map in Figure~2." srcset="
/publication/perrinet-bednar-15/figure_model_hu_b59ceb4637730f86.webp 400w,
/publication/perrinet-bednar-15/figure_model_hu_88248a181d04a487.webp 760w,
/publication/perrinet-bednar-15/figure_model_hu_33d62a0a730ba187.webp 1200w"
src="https://laurentperrinet.github.io/publication/perrinet-bednar-15/figure_model_hu_b59ceb4637730f86.webp"
width="310"
height="393"
loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;figcaption data-pre="Figure&amp;nbsp;" data-post=":&amp;nbsp;" class="numbered"&gt;
Edge co-occurrences &lt;strong&gt;(A)&lt;/strong&gt; An example image with the list of extracted edges overlaid. Each edge is represented by a red line segment which represents its position (center of segment), orientation, and scale (length of segment). We controlled the quality of the reconstruction from the edge information such that the residual energy was less than 5%. &lt;strong&gt;(B)&lt;/strong&gt; The relationship between a reference edge &lt;em&gt;A&lt;/em&gt; and another edge &lt;em&gt;B&lt;/em&gt; can be quantified in terms of the difference between their orientations $\theta$, ratio of scale $\sigma$, distance $d$ between their centers, and difference of azimuth (angular location) $\phi$. Additionally, we define $\psi=\phi - \theta/2$, which is symmetric with respect to the choice of the reference edge; in particular, $\psi=0$ for co-circular edges. % (see text). As in~\citet{Geisler01}, edges outside a central circular mask are discarded in the computation of the statistics to avoid artifacts. (Image credit: &lt;a href="https://commons.wikimedia.org/wiki/File:Elephant_/%28Loxodonta_Africana/%29_05.jpg" target="_blank" rel="noopener"&gt;Andrew Shiva, Creative Commons Attribution-Share Alike 3.0 Unported license&lt;/a&gt;). This is used to compute the chevron map in Figure~2.
&lt;/figcaption&gt;&lt;/figure&gt;
&lt;figure &gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="" srcset="
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width="598"
height="190"
loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;/figure&gt;
&lt;figure id="figure-the-probability-distribution-function-ppsi-theta-represents-the-distribution-of-the-different-geometrical-arrangements-of-edges-angles-which-we-call-a-chevron-map-we-show-here-the-histogram-for-non-animal-natural-images-illustrating-the-preference-for-co-linear-edge-configurations-for-each-chevron-configuration-deeper-and-deeper-red-circles-indicate-configurations-that-are-more-and-more-likely-with-respect-to-a-uniform-prior-with-an-average-maximum-of-about-3-times-more-likely-and-deeper-and-deeper-blue-circles-indicate-configurations-less-likely-than-a-flat-prior-with-a-minimum-of-about-08-times-as-likely-conveniently-this-chevron-map-shows-in-one-graph-that-non-animal-natural-images-have-on-average-a-preference-for-co-linear-and-parallel-edges-the-horizontal-middle-axis-and-orthogonal-angles-the-top-and-bottom-rowsalong-with-a-slight-preference-for-co-circular-configurations-for-psi0-and-psipm-frac-pi-2-just-above-and-below-the-central-row-we-compare-chevron-maps-in-different-image-categories-in-figure3"&gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="The probability distribution function $p(\psi, \theta)$ represents the distribution of the different geometrical arrangements of edges&amp;#39; angles, which we call a chevron map. We show here the histogram for non-animal natural images, illustrating the preference for co-linear edge configurations. For each chevron configuration, deeper and deeper red circles indicate configurations that are more and more likely with respect to a uniform prior, with an average maximum of about $3$ times more likely, and deeper and deeper blue circles indicate configurations less likely than a flat prior (with a minimum of about $0.8$ times as likely). Conveniently, this chevron map shows in one graph that non-animal natural images have on average a preference for co-linear and parallel edges, (the horizontal middle axis) and orthogonal angles (the top and bottom rows),along with a slight preference for co-circular configurations (for $\psi=0$ and $\psi=\pm \frac \pi 2$, just above and below the central row). We compare chevron maps in different image categories in Figure~3." srcset="
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width="550"
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loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;figcaption data-pre="Figure&amp;nbsp;" data-post=":&amp;nbsp;" class="numbered"&gt;
The probability distribution function $p(\psi, \theta)$ represents the distribution of the different geometrical arrangements of edges&amp;rsquo; angles, which we call a chevron map. We show here the histogram for non-animal natural images, illustrating the preference for co-linear edge configurations. For each chevron configuration, deeper and deeper red circles indicate configurations that are more and more likely with respect to a uniform prior, with an average maximum of about $3$ times more likely, and deeper and deeper blue circles indicate configurations less likely than a flat prior (with a minimum of about $0.8$ times as likely). Conveniently, this chevron map shows in one graph that non-animal natural images have on average a preference for co-linear and parallel edges, (the horizontal middle axis) and orthogonal angles (the top and bottom rows),along with a slight preference for co-circular configurations (for $\psi=0$ and $\psi=\pm \frac \pi 2$, just above and below the central row). We compare chevron maps in different image categories in Figure~3.
&lt;/figcaption&gt;&lt;/figure&gt;
&lt;figure &gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="" srcset="
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width="598"
height="453"
loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;/figure&gt;
&lt;figure id="figure-as-for-figure-2-we-show-the-probability-of-edge-configurations-as-chevron-maps-for-two-databases-man-made-animal-here-we-show-the-ratio-of-histogram-counts-relative-to-that-of-the-non-animal-natural-image-dataset-deeper-and-deeper-red-circles-indicate-configurations-that-are-more-and-more-likely-and-blue-respectively-less-likely-with-respect-to-the-histogram-computed-for-non-animal-images-in-the-left-plot-the-animal-images-exhibit-relatively-more-circular-continuations-and-converging-angles-red-chevrons-in-the-central-vertical-axis-relative-to-non-animal-natural-images-at-the-expense-of-co-linear-parallel-and-orthogonal-configurations-blue-circles-along-the-middle-horizontal-axis-the-man-made-images-have-strikingly-more-co-linear-features-central-circle-which-reflects-the-prevalence-of-long-straight-lines-in-the-cage-images-in-that-dataset-we-use-this-representation-to-categorize-images-from-these-different-categories-in-figure4"&gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="As for Figure 2, we show the probability of edge configurations as chevron maps for two databases (man-made, animal). Here, we show the ratio of histogram counts relative to that of the non-animal natural image dataset. Deeper and deeper red circles indicate configurations that are more and more likely (and blue respectively less likely) with respect to the histogram computed for non-animal images. In the left plot, the animal images exhibit relatively more circular continuations and converging angles (red chevrons in the central vertical axis) relative to non-animal natural images, at the expense of co-linear, parallel, and orthogonal configurations (blue circles along the middle horizontal axis). The man-made images have strikingly more co-linear features (central circle), which reflects the prevalence of long, straight lines in the cage images in that dataset. We use this representation to categorize images from these different categories in Figure~4." srcset="
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width="760"
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loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;figcaption data-pre="Figure&amp;nbsp;" data-post=":&amp;nbsp;" class="numbered"&gt;
As for Figure 2, we show the probability of edge configurations as chevron maps for two databases (man-made, animal). Here, we show the ratio of histogram counts relative to that of the non-animal natural image dataset. Deeper and deeper red circles indicate configurations that are more and more likely (and blue respectively less likely) with respect to the histogram computed for non-animal images. In the left plot, the animal images exhibit relatively more circular continuations and converging angles (red chevrons in the central vertical axis) relative to non-animal natural images, at the expense of co-linear, parallel, and orthogonal configurations (blue circles along the middle horizontal axis). The man-made images have strikingly more co-linear features (central circle), which reflects the prevalence of long, straight lines in the cage images in that dataset. We use this representation to categorize images from these different categories in Figure~4.
&lt;/figcaption&gt;&lt;/figure&gt;
&lt;figure id="figure-classification-results-to-quantify-the-difference-in-low-level-feature-statistics-across-categories-see-figure3-we-used-a-standard-support-vector-machine-svm-classifier-to-measure-how-each-representation-affected-the-classifiers-reliability-for-identifying-the-image-category-for-each-individual-image-we-constructed-a-vector-of-features-as-either-fo-the-histogram-of-first-order-statistics-as-the-histogram-of-edges-orientations-cm-the-chevron-map-subset-of-the-second-order-statistics-ie-the-two-dimensional-histogram-of-relative-orientation-and-azimuth-see-figure-2--or-so-the-full-four-dimensional-histogram-of-second-order-statistics-ie-all-parameters-of-the-edge-co-occurrences-we-gathered-these-vectors-for-each-different-class-of-images-and-report-here-the-results-of-the-svm-classifier-using-an-f1-score-50-represents-chance-level-while-it-was-expected-that-differences-would-be-clear-between-non-animal-natural-images-versus-laboratory-man-made-images-results-are-still-quite-high-for-classifying-animal-images-versus-non-animal-natural-images-and-are-in-the-range-reported-bycitetserre07-f1-score-of-80-for-human-observers-and-82-for-their-model-even-using-the-cm-features-alone-we-further-extend-this-results-to-the-psychophysical-results-of-serre-et-al-2007-in-figure-5"&gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="Classification results. To quantify the difference in low-level feature statistics across categories (see Figure~3, we used a standard Support Vector Machine (SVM) classifier to measure how each representation affected the classifier&amp;#39;s reliability for identifying the image category. For each individual image, we constructed a vector of features as either (FO) the histogram of first-order statistics as the histogram of edges&amp;#39; orientations, (CM) the chevron map subset of the second-order statistics, (i.e., the two-dimensional histogram of relative orientation and azimuth; see Figure 2 ), or (SO) the full, four-dimensional histogram of second-order statistics (i.e., all parameters of the edge co-occurrences). We gathered these vectors for each different class of images and report here the results of the SVM classifier using an F1 score (50\% represents chance level). While it was expected that differences would be clear between non-animal natural images versus laboratory (man-made) images, results are still quite high for classifying animal images versus non-animal natural images, and are in the range reported by~\citet{Serre07} (F1 score of 80\% for human observers and 82\% for their model), even using the CM features alone. We further extend this results to the psychophysical results of Serre et al. (2007) in Figure 5." srcset="
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width="476"
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loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;figcaption data-pre="Figure&amp;nbsp;" data-post=":&amp;nbsp;" class="numbered"&gt;
Classification results. To quantify the difference in low-level feature statistics across categories (see Figure&lt;del&gt;3, we used a standard Support Vector Machine (SVM) classifier to measure how each representation affected the classifier&amp;rsquo;s reliability for identifying the image category. For each individual image, we constructed a vector of features as either (FO) the histogram of first-order statistics as the histogram of edges&amp;rsquo; orientations, (CM) the chevron map subset of the second-order statistics, (i.e., the two-dimensional histogram of relative orientation and azimuth; see Figure 2 ), or (SO) the full, four-dimensional histogram of second-order statistics (i.e., all parameters of the edge co-occurrences). We gathered these vectors for each different class of images and report here the results of the SVM classifier using an F1 score (50% represents chance level). While it was expected that differences would be clear between non-animal natural images versus laboratory (man-made) images, results are still quite high for classifying animal images versus non-animal natural images, and are in the range reported by&lt;/del&gt;\citet{Serre07} (F1 score of 80% for human observers and 82% for their model), even using the CM features alone. We further extend this results to the psychophysical results of Serre et al. (2007) in Figure 5.
&lt;/figcaption&gt;&lt;/figure&gt;
&lt;figure id="figure-to-see-whether-the-patterns-of-errors-made-by-humans-are-consistent-with-our-model-we-studied-the-second-order-statistics-of-the-50-non-animal-images-that-human-subjects-in-serre-et-al-2007-most-commonly-falsely-reported-as-having-an-animal-we-call-this-set-of-images-the-false-alarm-image-dataset-left-this-chevron-map-plot-shows-the-ratio-between-the-second-order-statistics-of-the-false-alarm-images-and-the-full-non-animal-natural-image-dataset-computed-as-in-figure-3-left-just-as-for-the-images-that-actually-do-contain-animals-figure3-left-the-images-falsely-reported-as-having-animals-have-more-co-circular-and-converging-red-chevrons-and-fewer-collinear-and-orthogonal-configurations-blue-chevrons-right-to-quantify-this-similarity-we-computed-the-kullback-leibler-distance-between-the-histogram-of-each-of-these-images-from-the-false-alarm-image-dataset-and-the-average-histogram-of-each-class-the-difference-between-these-two-distances-gives-a-quantitative-measure-of-how-close-each-image-is-to-the-average-histograms-for-each-class-consistent-with-the-idea-that-humans-are-using-edge-co-occurences-to-do-rapid-image-categorization-the-50-non-animal-images-that-were-worst-classified-are-biased-toward-the-animal-histogram-d--104-while-the-550-best-classified-non-animal-images-are-closer-to-the-non-animal-histogram"&gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="To see whether the patterns of errors made by humans are consistent with our model, we studied the second-order statistics of the 50 non-animal images that human subjects in Serre et al. (2007) most commonly falsely reported as having an animal. We call this set of images the false-alarm image dataset. (Left) This chevron map plot shows the ratio between the second-order statistics of the false-alarm images and the full non-animal natural image dataset, computed as in Figure 3 (left). Just as for the images that actually do contain animals (Figure~3, left), the images falsely reported as having animals have more co-circular and converging (red chevrons) and fewer collinear and orthogonal configurations (blue chevrons). (Right) To quantify this similarity, we computed the Kullback-Leibler distance between the histogram of each of these images from the false-alarm image dataset, and the average histogram of each class. The difference between these two distances gives a quantitative measure of how close each image is to the average histograms for each class. Consistent with the idea that humans are using edge co-occurences to do rapid image categorization, the 50 non-animal images that were worst classified are biased toward the animal histogram ($d&amp;#39; = 1.04$), while the 550 best classified non-animal images are closer to the non-animal histogram. " srcset="
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width="760"
height="470"
loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;figcaption data-pre="Figure&amp;nbsp;" data-post=":&amp;nbsp;" class="numbered"&gt;
To see whether the patterns of errors made by humans are consistent with our model, we studied the second-order statistics of the 50 non-animal images that human subjects in Serre et al. (2007) most commonly falsely reported as having an animal. We call this set of images the false-alarm image dataset. (Left) This chevron map plot shows the ratio between the second-order statistics of the false-alarm images and the full non-animal natural image dataset, computed as in Figure 3 (left). Just as for the images that actually do contain animals (Figure~3, left), the images falsely reported as having animals have more co-circular and converging (red chevrons) and fewer collinear and orthogonal configurations (blue chevrons). (Right) To quantify this similarity, we computed the Kullback-Leibler distance between the histogram of each of these images from the false-alarm image dataset, and the average histogram of each class. The difference between these two distances gives a quantitative measure of how close each image is to the average histograms for each class. Consistent with the idea that humans are using edge co-occurences to do rapid image categorization, the 50 non-animal images that were worst classified are biased toward the animal histogram ($d&amp;rsquo; = 1.04$), while the 550 best classified non-animal images are closer to the non-animal histogram.
&lt;/figcaption&gt;&lt;/figure&gt;
&lt;/p&gt;
&lt;h2 id="communiqué-de-presse-insb--comment-nait-la-première-impression-dune-scène-visuelle"&gt;Communiqué de presse INSB : Comment nait la première impression d&amp;rsquo;une scène visuelle&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;&lt;a href="https://www.techno-science.net/actualite/comment-nait-premiere-impression-scene-visuelle-N14337.html" target="_blank" rel="noopener"&gt;communiqué de presse&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
&lt;blockquote&gt;
&lt;p&gt;En modélisant notre capacité à distinguer un animal dans une scène visuelle, des chercheurs de l’Institut de Neurosciences de la Timone et de l’Université d&amp;rsquo;Edinburgh lèvent le voile sur certains des mystères de la perception visuelle. Ils démontrent que la classification très rapide par le cerveau d’une image contenant ou non un animal, est possible à un niveau de représentation relativement primitif à partir de régularités statistiques simples, et non, comme cela est généralement admis, après une longue série d&amp;rsquo;analyses visuelles de plus en plus abstraites. Cette étude est publiée dans la revue Scientific Reports.&lt;/p&gt;&lt;/blockquote&gt;
&lt;p&gt;Classifier une image, par exemple en décidant si elle contient ou non un animal, est une des fonctions de base du cerveau. Dans le royaume animal, on comprend aisément qu’elle constitue une fonction vitale aussi bien pour des prédateurs que pour leurs proies. Les mécanismes sous-jacents sont de plus en plus étudiés aussi bien dans le domaine des systèmes d&amp;rsquo;intelligence artificielle que dans celui des Neurosciences, mais ils restent encore bien mystérieux pour les chercheurs. En effet, si les réseaux d&amp;rsquo;ordinateurs les plus avancés peuvent aujourd&amp;rsquo;hui aisément calculer numériquement des quantités phénoménales de données à partir de bases de données pharaoniques, même les systèmes les plus avancés de classification d&amp;rsquo;images n&amp;rsquo;égalent pas encore les capacités d&amp;rsquo;un jeune enfant!&lt;/p&gt;
&lt;p&gt;Laurent Perrinet de l’Institut de Neurosciences de la Timone à Marseille et James Bednar de l’université d&amp;rsquo;Edinburgh en Écosse, ont modélisé la façon dont nous pouvons classer différentes catégories d&amp;rsquo;images. Leur l&amp;rsquo;objectif initial était de différencier des scènes visuelles naturelles de scènes d&amp;rsquo;intérieur, mais ils ont pu montrer que ce système simple de classification permettait aussi de détecter en une fraction de seconde des animaux dans une image. En effet, ils ont mis en évidence qu&amp;rsquo;un niveau de performance comparable à celui d’observateurs humains est atteignable tout en utilisant un niveau de représentation très primitif, et non, comme cela est généralement admis, après une longue série d&amp;rsquo;analyses visuelles de plus en plus abstraites (détection des yeux et des membres, puis de la tête et du corps, etc&amp;hellip;).&lt;/p&gt;
&lt;p&gt;Cette représentation primitive se base sur les modèles existants de représentation des images dans les aires visuelles de bas niveau des primates. On estime en effet que dans le cortex visuel primaire les images visuelles sont représentées dans l&amp;rsquo;activité neurale comme l&amp;rsquo;organisation de contours élémentaires, à la manière d’un peintre qui dessine une silhouette en une série de coups de pinceau. Une des innovations majeures dans cette étude consiste à simplement utiliser la fréquence des configurations entre des paires de contours élémentaires comme représentation d&amp;rsquo;entrée utilisée pour le classificateur.&lt;/p&gt;
&lt;p&gt;Pour arriver à ce résultat, les chercheurs ont utilisé des modèles mathématiques de la représentation des images dans le cortex visuel primaire et en particulier les inter-relations entre des éléments de contours voisins. En étudiant les résultats de l&amp;rsquo;analyse, on note que dans les images naturelles, des contours parallèles sont observés majoritairement, signe que les contours et textures présents dans les images contiennent en majorité des alignements. C&amp;rsquo;est encore plus vrai dans les environnements artificiels comme dans une scène d&amp;rsquo;intérieur (par exemple un bureau) où les bords francs dominent. On montre aussi que les objets co-circulaires (c&amp;rsquo;est-à-dire des configurations symétriques) sont aussi relativement plus présents que des configurations aléatoires.&lt;/p&gt;
&lt;p&gt;La principale nouveauté de cette étude est de montrer que les images contenant un animal (quelle que soit son espèce ou sa position dans l&amp;rsquo;image) contiennent sensiblement plus de configurations symétriques. Cette différence suffit pour expliquer le niveau de performance de classification chez les humains quand on leur présente de telles scènes de façon très brève.&lt;/p&gt;
&lt;p&gt;Pour valider cette hypothèse, les chercheurs ont alors utilisé des données précédemment enregistrées dans lesquelles des volontaires regardaient et classifiaient des centaines d&amp;rsquo;images. En utilisant cette représentation primitive, ils ont mis en évidence qu&amp;rsquo;un programme très simple pouvait facilement classifier les images comme contenant ou non un animal, sans avoir besoin d’une connaissance plus élaborée sur les caractéristiques de l’animal comme sa position, sa taille ou son orientation sur l’image.&lt;/p&gt;
&lt;p&gt;Cette découverte peut accélérer le développement de requêtes via des images dans les moteurs de recherche, comme Google et Facebook, car elle permet une classification simple et robuste grâce à des caractéristiques statistiques de bas niveau basées sur la géométrie des objets. Elle pourrait ainsi améliorer l&amp;rsquo;efficacité de tels algorithmes. Toutefois, et comme cela a été mis en évidence dans la psychophysique humaine, les catégories visuelles doivent être visuellement assez distinctes: ce traitement rapide ne permet pas, par exemple, de distinguer une scène de montagne d&amp;rsquo;une scène de mer. De manière surprenante, les chercheurs ont montré que lorsque les humains se trompent en classifiant de manière erronée une image comme contenant un animal, le programme a tendance à se tromper de la même façon! En utilisant des modèles mathématiques, on peut donc imaginer synthétiser des images d&amp;rsquo;animaux qui en fait, n&amp;rsquo;en contiendraient pas. Ces &amp;ldquo;chimères&amp;rdquo; seront sûrement très utiles pour percer encore plus les mystères du système visuel.&lt;/p&gt;
&lt;p&gt;Dans le futur, l&amp;rsquo;extension de cette représentation calculée sur l&amp;rsquo;ensemble de l&amp;rsquo;image pourrait être améliorée en la couplant à des processus de classification locaux permettant de déterminer par exemple la position de l&amp;rsquo;objet à classifier et de segmenter progressivement la figure du fond afin de diminuer ainsi les distractions.&lt;/p&gt;
&lt;p&gt;
&lt;figure id="figure-tà-partir-dune-image-naturelle-en-haut-à-gauche-les-chercheurs-ont-déterminé-la-façon-la-plus-efficace-de-la-représenter-comme-une-succession-de-contours-élémentaires-orientés-sur-cet-exemple-limage-est-décomposée-en-contours-élémentaires-marqués-en-rouge-et-limage-correspond-à-sa-reconstruction-à-partir-de-cette-représentation-gage-dune-représentation-correcte-de-limage-le-schéma-en-bas-à-gauche-décrit-alors-les-relations-géométriques-pour-chaque-paire-de-contours-élémentaires-dénotés-ici-a-et-b-et-en-particulier-la-différence-entre-leurs-orientations-cette-différence-est-nulle-pour-des-contours-parallèles-ainsi-que-leur-différence-dazimuth-une-valeur-nulle-de-cette-dernière-indiquant-une-symétrie-cest-à-dire-que-ces-contours-sont-co-circulaires-on-peut-alors-compiler-les-statistiques-des-différentes-configurations-possibles-sur-des-bases-de-données-de-600-images-contenant-ou-ne-contenant-pas-danimal-on-voit-alors-que-les-images-contenant-un-animal-présentent-relativement-moins-de-configurations-parallèles-disques-bleus-jusquà-50-de-moins-et-plus-de-configurations-co-circulaires-cest-à-dire-le-long-de-laxe-vertical-médian-disques-rouges-jusquà-20-doccurences-en-plus-cette-différence-aussi-tenue-soit-elle-permet-alors-de-classifier-une-image-pour-permettre-de-deviner-si-elle-contient-ou-non-un-animal"&gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="TÀ partir d&amp;#39;une image naturelle (en haut à gauche), les chercheurs ont déterminé la façon la plus efficace de la représenter comme une succession de contours élémentaires orientés. Sur cet exemple, l&amp;#39;image est décomposée en contours élémentaires (marqués en rouge) et l&amp;#39;image correspond à sa reconstruction à partir de cette représentation, gage d&amp;#39;une représentation correcte de l&amp;#39;image. Le schéma (en bas à gauche) décrit alors les relations géométriques pour chaque paire de contours élémentaires (dénotés ici A et B) et en particulier la différence entre leurs orientations (cette différence est nulle pour des contours parallèles) ainsi que leur différence d&amp;#39;azimuth. Une valeur nulle de cette dernière indiquant une symétrie, c&amp;#39;est-à-dire que ces contours sont co-circulaires. On peut alors compiler les statistiques des différentes configurations possibles sur des bases de données de 600 images contenant ou ne contenant pas d&amp;#39;animal. On voit alors que les images contenant un animal présentent relativement moins de configurations parallèles (disques bleus, jusqu&amp;#39;à 50% de moins) et plus de configurations co-circulaires, c&amp;#39;est à dire le long de l&amp;#39;axe vertical médian (disques rouges, jusqu&amp;#39;à 20% d&amp;#39;occurences en plus). Cette différence, aussi tenue soit elle, permet alors de classifier une image pour permettre de deviner si elle contient ou non un animal." srcset="
/publication/perrinet-bednar-15/figure_synthesis_FR_hu_630c64ea5e907ef9.webp 400w,
/publication/perrinet-bednar-15/figure_synthesis_FR_hu_4a0fdd04f8407a2a.webp 760w,
/publication/perrinet-bednar-15/figure_synthesis_FR_hu_fc10817d8c0e51ed.webp 1200w"
src="https://laurentperrinet.github.io/publication/perrinet-bednar-15/figure_synthesis_FR_hu_630c64ea5e907ef9.webp"
width="760"
height="460"
loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;figcaption data-pre="Figure&amp;nbsp;" data-post=":&amp;nbsp;" class="numbered"&gt;
TÀ partir d&amp;rsquo;une image naturelle (en haut à gauche), les chercheurs ont déterminé la façon la plus efficace de la représenter comme une succession de contours élémentaires orientés. Sur cet exemple, l&amp;rsquo;image est décomposée en contours élémentaires (marqués en rouge) et l&amp;rsquo;image correspond à sa reconstruction à partir de cette représentation, gage d&amp;rsquo;une représentation correcte de l&amp;rsquo;image. Le schéma (en bas à gauche) décrit alors les relations géométriques pour chaque paire de contours élémentaires (dénotés ici A et B) et en particulier la différence entre leurs orientations (cette différence est nulle pour des contours parallèles) ainsi que leur différence d&amp;rsquo;azimuth. Une valeur nulle de cette dernière indiquant une symétrie, c&amp;rsquo;est-à-dire que ces contours sont co-circulaires. On peut alors compiler les statistiques des différentes configurations possibles sur des bases de données de 600 images contenant ou ne contenant pas d&amp;rsquo;animal. On voit alors que les images contenant un animal présentent relativement moins de configurations parallèles (disques bleus, jusqu&amp;rsquo;à 50% de moins) et plus de configurations co-circulaires, c&amp;rsquo;est à dire le long de l&amp;rsquo;axe vertical médian (disques rouges, jusqu&amp;rsquo;à 20% d&amp;rsquo;occurences en plus). Cette différence, aussi tenue soit elle, permet alors de classifier une image pour permettre de deviner si elle contient ou non un animal.
&lt;/figcaption&gt;&lt;/figure&gt;
&lt;figure &gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="" srcset="
/publication/perrinet-bednar-15/@emulenews_612988348400070656_tweetcapture_hu_5a26a1e584714236.webp 400w,
/publication/perrinet-bednar-15/@emulenews_612988348400070656_tweetcapture_hu_e537c3d5e6ec4ac1.webp 760w,
/publication/perrinet-bednar-15/@emulenews_612988348400070656_tweetcapture_hu_9343f9bc247a4dd4.webp 1200w"
src="https://laurentperrinet.github.io/publication/perrinet-bednar-15/@emulenews_612988348400070656_tweetcapture_hu_5a26a1e584714236.webp"
width="598"
height="453"
loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;/figure&gt;
&lt;/p&gt;</description></item><item><title>Active inference, eye movements and oculomotor delays</title><link>https://laurentperrinet.github.io/publication/perrinet-adams-friston-14/</link><pubDate>Tue, 16 Dec 2014 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/perrinet-adams-friston-14/</guid><description>&lt;h1 id="active-inference-tracking-eye-movements-and-oculomotor-delays"&gt;Active Inference, tracking eye movements and oculomotor delays&lt;/h1&gt;
&lt;p&gt;Tracking eye movements face a difficult task: they have to be fast while they suffer inevitable delays. If we focus on area MT of humans for instance as it is crucial for detecting the motion of visual objects, sensory information coming to this area is already lagging some 35 milliseconds behind operational time – that is, it reflects some past information. Still the fastest action that may be done there is only able to reach the effector muscles of the eyes some 40 milliseconds later – that is, in the future. The tracking eye movement system is however able to respond swiftly and even to anticipate repetitive movements (e.g. Barnes et al, 2000 – refs in manuscript). In that case, it means that information in a cortical area is both predicted from the past sensory information but also anticipated to give an optimal response in the future. Even if numerous models have been described to model different mechanisms to account for delays, no theoretical approach has tackled the whole problem explicitly. In several areas of vision research, authors have proposed models at different levels of abstractions from biomechanical models, to neurobiological implementations (e.g. Robinson, 1986) or Bayesian models. This study is both novel and important because – using a neurobiologically plausible hierarchical Bayesian model – it demonstrates that using generalized coordinates to finesse the prediction of a target&amp;rsquo;s motion, the model can reproduce characteristic properties of tracking eye movements in the presence of delays. Crucially, the different refinements to the model that we propose – pursuit initiation, smooth pursuit eye movements, and anticipatory response – are consistent with the different types of tracking eye movements that may be observed experimentally.
&lt;figure id="figure-a-this-figure-reports-the-response-of-predictive-processing-during-the-simulation-of-pursuit-initiation-using-a-single-sweep-of-a-visual-target-while-compensating-for-sensory-motor-delays-here-we-see-horizontal-excursions-of-oculomotor-angle-red-line-one-can-see-clearly-the-initial-displacement-of-the-target-that-is-suppressed-by-action-after-a-few-hundred-milliseconds-additionally-we-illustrate-the-effects-of-assuming-wrong-sensorimotor-delays-on-pursuit-initiation-under-pure-sensory-delays-blue-dotted-line-one-can-see-clearly-the-delay-in-sensory-predictions-in-relation-to-the-true-inputs-with-pure-motor-delays-blue-dashed-line-and-with-combined-sensorimotor-delays-blue-line-there-is-a-failure-of-optimal-control-with-oscillatory-fluctuations-in-oculomotor-trajectories-which-may-become-unstable-b-this-figure-reports-the-simulation-of-smooth-pursuit-when-the-target-motion-is-hemi-sinusoidal-as-would-happen-for-a-pendulum-that-would-be-stopped-at-each-half-cycle-left-of-the-vertical-broken-black-lines-in-the-lower-right-panel-we-report-the-horizontal-excursions-of-oculomotor-angle-the-generative-model-used-here-has-been-equipped-with-a-second-hierarchical-level-that-contains-hidden-states-modeling-latent-periodic-behavior-of-the-hidden-causes-of-target-motion-with-this-addition-the-improvement-in-pursuit-accuracy-apparent-at-the-onset-of-the-second-cycle-of-motion-is-observed-pink-shaded-area-similar-to-psychophysical-experimentss"&gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="**(A)** This figure reports the response of predictive processing during the simulation of pursuit initiation, using a single sweep of a visual target, while compensating for sensory motor delays. Here, we see horizontal excursions of oculomotor angle (red line). One can see clearly the initial displacement of the target that is suppressed by action after a few hundred milliseconds. Additionally, we illustrate the effects of assuming wrong sensorimotor delays on pursuit initiation. Under pure sensory delays (blue dotted line), one can see clearly the delay in sensory predictions, in relation to the true inputs. With pure motor delays (blue dashed line) and with combined sensorimotor delays (blue line) there is a failure of optimal control with oscillatory fluctuations in oculomotor trajectories, which may become unstable. **(B)** This figure reports the simulation of smooth pursuit when the target motion is hemi-sinusoidal, as would happen for a pendulum that would be stopped at each half cycle left of the vertical (broken black lines in the lower-right panel). We report the horizontal excursions of oculomotor angle. The generative model used here has been equipped with a second hierarchical level that contains hidden states, modeling latent periodic behavior of the (hidden) causes of target motion. With this addition, the improvement in pursuit accuracy apparent at the onset of the second cycle of motion is observed (pink shaded area), similar to psychophysical experimentss." srcset="
/publication/perrinet-adams-friston-14/featured_hu_4977ce748e3aef8a.webp 400w,
/publication/perrinet-adams-friston-14/featured_hu_d5ac3484bd10f79.webp 760w,
/publication/perrinet-adams-friston-14/featured_hu_7b849bfdc41f9431.webp 1200w"
src="https://laurentperrinet.github.io/publication/perrinet-adams-friston-14/featured_hu_4977ce748e3aef8a.webp"
width="760"
height="405"
loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;figcaption&gt;
&lt;strong&gt;(A)&lt;/strong&gt; This figure reports the response of predictive processing during the simulation of pursuit initiation, using a single sweep of a visual target, while compensating for sensory motor delays. Here, we see horizontal excursions of oculomotor angle (red line). One can see clearly the initial displacement of the target that is suppressed by action after a few hundred milliseconds. Additionally, we illustrate the effects of assuming wrong sensorimotor delays on pursuit initiation. Under pure sensory delays (blue dotted line), one can see clearly the delay in sensory predictions, in relation to the true inputs. With pure motor delays (blue dashed line) and with combined sensorimotor delays (blue line) there is a failure of optimal control with oscillatory fluctuations in oculomotor trajectories, which may become unstable. &lt;strong&gt;(B)&lt;/strong&gt; This figure reports the simulation of smooth pursuit when the target motion is hemi-sinusoidal, as would happen for a pendulum that would be stopped at each half cycle left of the vertical (broken black lines in the lower-right panel). We report the horizontal excursions of oculomotor angle. The generative model used here has been equipped with a second hierarchical level that contains hidden states, modeling latent periodic behavior of the (hidden) causes of target motion. With this addition, the improvement in pursuit accuracy apparent at the onset of the second cycle of motion is observed (pink shaded area), similar to psychophysical experimentss.
&lt;/figcaption&gt;&lt;/figure&gt;
&lt;/p&gt;</description></item><item><title>Edge co-occurrences are sufficient to categorize natural versus animal images</title><link>https://laurentperrinet.github.io/publication/perrinet-bednar-14-vss/</link><pubDate>Fri, 22 Aug 2014 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/perrinet-bednar-14-vss/</guid><description>&lt;ul&gt;
&lt;li&gt;see a follow-up:
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;span class="article-metadata li-cite-author"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/james-a-bednar/"&gt;James A Bednar&lt;/a&gt;&lt;/span&gt;
&lt;/span&gt;
(2015).
&lt;a href="https://laurentperrinet.github.io/publication/perrinet-bednar-15/"&gt;Edge co-occurrences can account for rapid categorization of natural versus animal images&lt;/a&gt;.
&lt;em&gt;Scientific Reports&lt;/em&gt;.
&lt;p&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publication/perrinet-bednar-15/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1038/srep11400" target="_blank" rel="noopener"&gt;
DOI
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://github.com/laurentperrinet/PerrinetBednar15" target="_blank" rel="noopener"&gt;
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&lt;/p&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Axonal delays and on-time control of eye movements</title><link>https://laurentperrinet.github.io/talk/2014-01-10-int-fest/</link><pubDate>Fri, 10 Jan 2014 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2014-01-10-int-fest/</guid><description/></item><item><title>How and why do image frequency properties influence perceived speed?</title><link>https://laurentperrinet.github.io/publication/meso-13-vss/</link><pubDate>Tue, 01 Jan 2013 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/meso-13-vss/</guid><description/></item><item><title>Measuring speed of moving textures: Different pooling of motion information for human ocular following and perception</title><link>https://laurentperrinet.github.io/publication/simoncini-13-vss/</link><pubDate>Tue, 01 Jan 2013 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/simoncini-13-vss/</guid><description/></item><item><title>Edge statistics in natural images versus laboratory animal environments: implications for understanding lateral connectivity in V1</title><link>https://laurentperrinet.github.io/talk/2012-05-10-itwist/</link><pubDate>Thu, 10 May 2012 13:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2012-05-10-itwist/</guid><description>&lt;ul&gt;
&lt;li&gt;See a followup in
&lt;div class="view-list view-list-item"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;a href="https://laurentperrinet.github.io/publication/perrinet-bednar-15/" &gt;Edge co-occurrences can account for rapid categorization of natural versus animal images&lt;/a&gt;
&lt;div class="article-metadata"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/james-a-bednar/"&gt;James A Bednar&lt;/a&gt;&lt;/span&gt;
&lt;/div&gt;
&lt;div class="btn-links"&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
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&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://github.com/laurentperrinet/PerrinetBednar15" target="_blank" rel="noopener"&gt;
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&lt;/div&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>The behavioral receptive field underlying motion integration for primate tracking eye movements</title><link>https://laurentperrinet.github.io/publication/masson-12/</link><pubDate>Wed, 21 Mar 2012 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/masson-12/</guid><description>&lt;p&gt;
&lt;figure &gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="header" srcset="
/publication/masson-12/masson-12_hu_ad4f294cb24e88cb.webp 400w,
/publication/masson-12/masson-12_hu_1e8c0fb3cfe2c0d0.webp 760w,
/publication/masson-12/masson-12_hu_4ded32aa07b66afa.webp 1200w"
src="https://laurentperrinet.github.io/publication/masson-12/masson-12_hu_ad4f294cb24e88cb.webp"
width="760"
height="168"
loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;/figure&gt;
&lt;/p&gt;</description></item><item><title>Grabbing, tracking and sniffing as models for motion detection and eye movements</title><link>https://laurentperrinet.github.io/talk/2012-01-27-fil/</link><pubDate>Fri, 27 Jan 2012 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2012-01-27-fil/</guid><description/></item><item><title>Edge statistics in natural images versus laboratory animal environments: implications for understanding lateral connectivity in V1</title><link>https://laurentperrinet.github.io/talk/2012-01-24-edinburgh/</link><pubDate>Tue, 24 Jan 2012 13:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2012-01-24-edinburgh/</guid><description>&lt;ul&gt;
&lt;li&gt;See a followup in
&lt;div class="view-list view-list-item"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;a href="https://laurentperrinet.github.io/publication/perrinet-bednar-15/" &gt;Edge co-occurrences can account for rapid categorization of natural versus animal images&lt;/a&gt;
&lt;div class="article-metadata"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/james-a-bednar/"&gt;James A Bednar&lt;/a&gt;&lt;/span&gt;
&lt;/div&gt;
&lt;div class="btn-links"&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
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&lt;/div&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Effect of image statistics on fixational eye movements</title><link>https://laurentperrinet.github.io/publication/simoncini-12-vss/</link><pubDate>Sun, 01 Jan 2012 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/simoncini-12-vss/</guid><description/></item><item><title>Measuring speed of moving textures: Different pooling of motion information for human ocular following and perception.</title><link>https://laurentperrinet.github.io/publication/simoncini-12-coding/</link><pubDate>Sun, 01 Jan 2012 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/simoncini-12-coding/</guid><description/></item><item><title>More is not always better: dissociation between perception and action explained by adaptive gain control</title><link>https://laurentperrinet.github.io/publication/simoncini-12/</link><pubDate>Sun, 01 Jan 2012 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/simoncini-12/</guid><description>&lt;p&gt;
&lt;figure &gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="header" srcset="
/publication/simoncini-12/simoncini-12_hu_3e217c49bb50a664.webp 400w,
/publication/simoncini-12/simoncini-12_hu_eba065b209371ba5.webp 760w,
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src="https://laurentperrinet.github.io/publication/simoncini-12/simoncini-12_hu_3e217c49bb50a664.webp"
width="760"
height="318"
loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;/figure&gt;
&lt;figure id="figure-band-pass-motion-stimuli-for-perception-and-action-tasks-a-in-the-space-representing-temporal-against-spatial-frequency-each-line-going-through-the-origin-corresponds-to-stimuli-moving-at-the-same-speed-a-simple-drifting-grating-is-a-single-point-in-this-space-our-moving-texture-stimuli-had-their-energy-distributed-within-an-ellipse-elongated-along-a-given-speed-line-keeping-constant-the-mean-spatial-and-temporal-frequencies-the-spatio-temporal-bandwidth-was-manipulated-by-co-varying-bsf-and-btf-as-illustrated-by-the-xyt-examples-human-performance-was-measured-for-two-different-tasks-run-in-parallel-blocks-b-for-ocular-tracking-motion-stimuli-were-presented-for-a-short-duration-200ms-in-the-wake-of-a-centering-saccade-to-control-both-attention-and-fixation-states-c-for-speed-discrimination-test-and-reference-stimuli-were-presented-successively-for-the-same-duration-and-subjects-were-instructed-to-indicate-whether-the-test-stimulus-was-perceived-as-slower-or-faster-than-reference"&gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="*Band-pass motion stimuli for perception and action tasks.* (a) In the space representing temporal against spatial frequency, each line going through the origin corresponds to stimuli moving at the same speed. A simple drifting grating is a single point in this space. Our moving texture stimuli had their energy distributed within an ellipse elongated along a given speed line, keeping constant the mean spatial and temporal frequencies. The spatio-temporal bandwidth was manipulated by co-varying Bsf and Btf as illustrated by the (x,y,t) examples. Human performance was measured for two different tasks, run in parallel blocks. (b) For ocular tracking, motion stimuli were presented for a short duration (200ms) in the wake of a centering saccade to control both attention and fixation states. (c) For speed discrimination, test and reference stimuli were presented successively for the same duration and subjects were instructed to indicate whether the test stimulus was perceived as slower or faster than reference. "
src="https://laurentperrinet.github.io/publication/simoncini-12/grating.gif"
loading="lazy" data-zoomable width="80%" /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;figcaption&gt;
&lt;em&gt;Band-pass motion stimuli for perception and action tasks.&lt;/em&gt; (a) In the space representing temporal against spatial frequency, each line going through the origin corresponds to stimuli moving at the same speed. A simple drifting grating is a single point in this space. Our moving texture stimuli had their energy distributed within an ellipse elongated along a given speed line, keeping constant the mean spatial and temporal frequencies. The spatio-temporal bandwidth was manipulated by co-varying Bsf and Btf as illustrated by the (x,y,t) examples. Human performance was measured for two different tasks, run in parallel blocks. (b) For ocular tracking, motion stimuli were presented for a short duration (200ms) in the wake of a centering saccade to control both attention and fixation states. (c) For speed discrimination, test and reference stimuli were presented successively for the same duration and subjects were instructed to indicate whether the test stimulus was perceived as slower or faster than reference.
&lt;/figcaption&gt;&lt;/figure&gt;
&lt;/p&gt;</description></item><item><title>Perceptions as Hypotheses: Saccades as Experiments</title><link>https://laurentperrinet.github.io/publication/friston-12/</link><pubDate>Sun, 01 Jan 2012 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/friston-12/</guid><description>&lt;p&gt;
&lt;figure &gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="header" srcset="
/publication/friston-12/friston-12_hu_cf9a7e1a737af253.webp 400w,
/publication/friston-12/friston-12_hu_9fe38d1f94e50f96.webp 760w,
/publication/friston-12/friston-12_hu_93295e9992143a1f.webp 1200w"
src="https://laurentperrinet.github.io/publication/friston-12/friston-12_hu_cf9a7e1a737af253.webp"
width="760"
height="196"
loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;/figure&gt;
&lt;figure id="figure-this-schematic-shows-the-dependencies-among-various-quantities-that-are-assumed-when-modeling-the-exchanges-of-a-self-organizing-system-like-the-brain-with-the-environment-the-top-panel-describes-the-states-of-the-environment-and-the-system-or-agent-in-terms-of-a-probabilistic-dependency-graph-where-connections-denote-directed-dependencies-the-quantities-are-described-within-the-nodes-of-this-graph-with-exemplar-forms-for-their-dependencies-on-other-variables-see-main-text-here-hidden-and-internal-states-are-separated-by-action-and-sensory-states-both-action-and-internal-states-encoding-a-conditional-density-minimize-free-energy-while-internal-states-encoding-prior-beliefs-maximize-salience-both-free-energy-and-salience-are-defined-in-terms-of-a-generative-model-that-is-shown-as-fictive-dependency-graph-in-the-lower-panel-note-that-the-variables-in-the-real-world-and-the-form-of-their-dynamics-are-different-from-that-assumed-by-the-generative-model-this-is-why-external-states-are-in-bold-furthermore-note-that-action-is-a-state-in-the-model-of-the-brain-but-is-replaced-by-hidden-controls-in-the-brains-model-of-its-world-this-means-that-the-agent-is-not-aware-of-action-but-has-beliefs-about-hidden-causes-in-the-world-that-action-can-fulfill-through-minimizing-free-energy-these-beliefs-correspond-to-prior-expectations-that-sensory-states-will-be-sampled-in-a-way-that-optimizes-conditional-confidence-or-salience"&gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img src="https://www.frontiersin.org/files/Articles/21922/fpsyg-03-00151-r4/image_m/fpsyg-03-00151-g001.jpg" alt="**This schematic shows the dependencies among various quantities that are assumed when modeling the exchanges of a self organizing system like the brain with the environment.** The top panel describes the states of the environment and the system or agent in terms of a probabilistic dependency graph, where connections denote directed dependencies. The quantities are described within the nodes of this graph with exemplar forms for their dependencies on other variables (see main text). Here, hidden and internal states are separated by action and sensory states. Both action and internal states encoding a conditional density minimize free energy, while internal states encoding prior beliefs maximize salience. Both free energy and salience are defined in terms of a generative model that is shown as fictive dependency graph in the lower panel. Note that the variables in the real world and the form of their dynamics are different from that assumed by the generative model; this is why external states are in bold. Furthermore, note that action is a state in the model of the brain but is replaced by hidden controls in the brain’s model of its world. This means that the agent is not aware of action but has beliefs about hidden causes in the world that action can fulfill through minimizing free energy. These beliefs correspond to prior expectations that sensory states will be sampled in a way that optimizes conditional confidence or salience." loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;figcaption&gt;
&lt;strong&gt;This schematic shows the dependencies among various quantities that are assumed when modeling the exchanges of a self organizing system like the brain with the environment.&lt;/strong&gt; The top panel describes the states of the environment and the system or agent in terms of a probabilistic dependency graph, where connections denote directed dependencies. The quantities are described within the nodes of this graph with exemplar forms for their dependencies on other variables (see main text). Here, hidden and internal states are separated by action and sensory states. Both action and internal states encoding a conditional density minimize free energy, while internal states encoding prior beliefs maximize salience. Both free energy and salience are defined in terms of a generative model that is shown as fictive dependency graph in the lower panel. Note that the variables in the real world and the form of their dynamics are different from that assumed by the generative model; this is why external states are in bold. Furthermore, note that action is a state in the model of the brain but is replaced by hidden controls in the brain’s model of its world. This means that the agent is not aware of action but has beliefs about hidden causes in the world that action can fulfill through minimizing free energy. These beliefs correspond to prior expectations that sensory states will be sampled in a way that optimizes conditional confidence or salience.
&lt;/figcaption&gt;&lt;/figure&gt;
&lt;/p&gt;</description></item><item><title>Edge statistics in natural images versus laboratory animal environments: implications for understanding lateral connectivity in V1</title><link>https://laurentperrinet.github.io/talk/2011-11-15-sfn/</link><pubDate>Tue, 15 Nov 2011 08:45:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2011-11-15-sfn/</guid><description>&lt;ul&gt;
&lt;li&gt;Abstract Control Number: 17671&lt;/li&gt;
&lt;li&gt;Presentation Number: 530.04&lt;/li&gt;
&lt;li&gt;Presentation Time: 8:45am - 9:00am&lt;/li&gt;
&lt;li&gt;session:&lt;/li&gt;
&lt;li&gt;Session Type: Nanosymposium&lt;/li&gt;
&lt;li&gt;Session Number: 530&lt;/li&gt;
&lt;li&gt;Session Title: Development of Motor and Sensory Systems&lt;/li&gt;
&lt;li&gt;See a followup in
&lt;div class="view-list view-list-item"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;a href="https://laurentperrinet.github.io/publication/perrinet-bednar-15/" &gt;Edge co-occurrences can account for rapid categorization of natural versus animal images&lt;/a&gt;
&lt;div class="article-metadata"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/james-a-bednar/"&gt;James A Bednar&lt;/a&gt;&lt;/span&gt;
&lt;/div&gt;
&lt;div class="btn-links"&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publication/perrinet-bednar-15/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1038/srep11400" target="_blank" rel="noopener"&gt;
DOI
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://github.com/laurentperrinet/PerrinetBednar15" target="_blank" rel="noopener"&gt;
Code&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://www.nature.com/articles/srep11400" target="_blank" rel="noopener"&gt;
URL&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://hal.science/hal-01202447" target="_blank" rel="noopener"&gt;
HAL&lt;/a&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Edge statistics in natural images versus laboratory animal environments: implications for understanding lateral connectivity in V1</title><link>https://laurentperrinet.github.io/talk/2011-09-28-ermites/</link><pubDate>Wed, 28 Sep 2011 13:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2011-09-28-ermites/</guid><description>&lt;ul&gt;
&lt;li&gt;See a followup in
&lt;div class="view-list view-list-item"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;a href="https://laurentperrinet.github.io/publication/perrinet-bednar-15/" &gt;Edge co-occurrences can account for rapid categorization of natural versus animal images&lt;/a&gt;
&lt;div class="article-metadata"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/james-a-bednar/"&gt;James A Bednar&lt;/a&gt;&lt;/span&gt;
&lt;/div&gt;
&lt;div class="btn-links"&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publication/perrinet-bednar-15/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1038/srep11400" target="_blank" rel="noopener"&gt;
DOI
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://github.com/laurentperrinet/PerrinetBednar15" target="_blank" rel="noopener"&gt;
Code&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://www.nature.com/articles/srep11400" target="_blank" rel="noopener"&gt;
URL&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://hal.science/hal-01202447" target="_blank" rel="noopener"&gt;
HAL&lt;/a&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Edge statistics in natural images versus laboratory animal environments: implications for understanding lateral connectivity in V1</title><link>https://laurentperrinet.github.io/publication/perrinet-11-sfn/</link><pubDate>Sat, 01 Jan 2011 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/perrinet-11-sfn/</guid><description>&lt;ul&gt;
&lt;li&gt;See a followup in
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;span class="article-metadata li-cite-author"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/james-a-bednar/"&gt;James A Bednar&lt;/a&gt;&lt;/span&gt;
&lt;/span&gt;
(2015).
&lt;a href="https://laurentperrinet.github.io/publication/perrinet-bednar-15/"&gt;Edge co-occurrences can account for rapid categorization of natural versus animal images&lt;/a&gt;.
&lt;em&gt;Scientific Reports&lt;/em&gt;.
&lt;p&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publication/perrinet-bednar-15/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1038/srep11400" target="_blank" rel="noopener"&gt;
DOI
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://github.com/laurentperrinet/PerrinetBednar15" target="_blank" rel="noopener"&gt;
Code&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://www.nature.com/articles/srep11400" target="_blank" rel="noopener"&gt;
URL&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://hal.science/hal-01202447" target="_blank" rel="noopener"&gt;
HAL&lt;/a&gt;
&lt;/p&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Peut-on parler d'intelligence mécanique?</title><link>https://laurentperrinet.github.io/talk/2009-11-24-intelligence-mecanique/</link><pubDate>Tue, 24 Nov 2009 18:30:00 +0000</pubDate><guid>https://laurentperrinet.github.io/talk/2009-11-24-intelligence-mecanique/</guid><description>&lt;p&gt;Nous parlerons de cette partie &amp;ldquo;mécanique&amp;rdquo; du cerveau animal ou humain qui permet de percevoir les mouvements et de &amp;hellip; survivre au sein de l&amp;rsquo;environnement. On verra, par exemple, que notre cerveau peut-être &lt;a href="http://interstices.info/classificateur" target="_blank" rel="noopener"&gt;plus rapide que nous&lt;/a&gt;, qu&amp;rsquo;il y a des solutions &amp;ldquo;stupides&amp;rdquo; qui marchent remarquablement bien pour &lt;a href="http://interstices.info/generation-trajectoires" target="_blank" rel="noopener"&gt;sortir d&amp;rsquo;un labyrinthe&lt;/a&gt;, et qui si la grenouille sait &lt;a href="http://interstices.info/grenouille" target="_blank" rel="noopener"&gt;gober une mouche bien mieux qu&amp;rsquo;un robot&lt;/a&gt; &amp;hellip; elle n&amp;rsquo;est pas plus maligne ! Parce que ce qu&amp;rsquo;il ne faut pas confondre ici c&amp;rsquo;est &lt;a href="https://interstices.info/calculer-penser/" target="_blank" rel="noopener"&gt;la différence entre calculer et penser&lt;/a&gt;, entre &lt;a href="http://interstices.info/algo-mode-emploi" target="_blank" rel="noopener"&gt;intelligence et algorithmes&lt;/a&gt;. En comprenant cela, avec &lt;a href="http://fr.wikipedia.org/wiki/Alan_Turing" target="_blank" rel="noopener"&gt;Alan Mathison Turing&lt;/a&gt;, le Gutenberg du XXème siècle, l&amp;rsquo;humanité a basculé des temps modernes à l&amp;rsquo;ère du numérique.&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;(!) visitez le &lt;a href="https://interstices.info/" target="_blank" rel="noopener"&gt;site d&amp;rsquo;interstices&lt;/a&gt;!&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Introduction to Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision</title><link>https://laurentperrinet.github.io/publication/cessac-07-a/</link><pubDate>Thu, 01 Mar 2007 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/cessac-07-a/</guid><description>&lt;p&gt;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:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;span class="article-metadata li-cite-author"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/bruno-cessac/"&gt;Bruno Cessac&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/emmanuel-dauc%C3%A9/"&gt;Emmanuel Daucé&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/manuel-samuelides/"&gt;Manuel Samuelides&lt;/a&gt;&lt;/span&gt;
&lt;/span&gt;
(2007).
&lt;a href="https://laurentperrinet.github.io/publication/cessac-07/"&gt;Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision&lt;/a&gt;.
&lt;em&gt;Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision&lt;/em&gt;.
&lt;p&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publication/cessac-07/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;/p&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;li&gt;
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;span class="article-metadata li-cite-author"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;
&lt;/span&gt;
(2007).
&lt;a href="https://laurentperrinet.github.io/publication/perrinet-07/"&gt;Dynamical Neural Networks: modeling low-level vision at short latencies&lt;/a&gt;.
&lt;em&gt;Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision&lt;/em&gt;.
&lt;p&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://laurentperrinet.github.io/publication/perrinet-07/perrinet-07.pdf" target="_blank" rel="noopener"&gt;
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&lt;/a&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publication/perrinet-07/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1140/epjst/e2007-00061-7" target="_blank" rel="noopener"&gt;
DOI
&lt;/a&gt;
&lt;/p&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision</title><link>https://laurentperrinet.github.io/publication/cessac-07/</link><pubDate>Thu, 01 Mar 2007 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/cessac-07/</guid><description>&lt;p&gt;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:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;span class="article-metadata li-cite-author"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/bruno-cessac/"&gt;Bruno Cessac&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/emmanuel-dauc%C3%A9/"&gt;Emmanuel Daucé&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/manuel-samuelides/"&gt;Manuel Samuelides&lt;/a&gt;&lt;/span&gt;
&lt;/span&gt;
(2007).
&lt;a href="https://laurentperrinet.github.io/publication/cessac-07-a/"&gt;Introduction to Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision&lt;/a&gt;.
&lt;em&gt;Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision&lt;/em&gt;.
&lt;p&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publication/cessac-07-a/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1140/epjst/e2007-00057-3" target="_blank" rel="noopener"&gt;
DOI
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://www.springerlink.com/index/10.1140/epjst/e2007-00057-3" target="_blank" rel="noopener"&gt;
URL&lt;/a&gt;
&lt;/p&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;li&gt;
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;span class="article-metadata li-cite-author"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;
&lt;/span&gt;
(2007).
&lt;a href="https://laurentperrinet.github.io/publication/perrinet-07/"&gt;Dynamical Neural Networks: modeling low-level vision at short latencies&lt;/a&gt;.
&lt;em&gt;Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision&lt;/em&gt;.
&lt;p&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://laurentperrinet.github.io/publication/perrinet-07/perrinet-07.pdf" target="_blank" rel="noopener"&gt;
PDF
&lt;/a&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publication/perrinet-07/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1140/epjst/e2007-00061-7" target="_blank" rel="noopener"&gt;
DOI
&lt;/a&gt;
&lt;/p&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Dynamic inference for motion tracking</title><link>https://laurentperrinet.github.io/publication/montagnini-07-a/</link><pubDate>Mon, 01 Jan 2007 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/montagnini-07-a/</guid><description/></item><item><title>Visual tracking of ambiguous moving objects: A recursive Bayesian model</title><link>https://laurentperrinet.github.io/publication/montagnini-07-b/</link><pubDate>Mon, 01 Jan 2007 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/montagnini-07-b/</guid><description/></item><item><title>Modeling of simple cells through a sparse overcomplete gabor wavelet representation based on local inhibition and facilitation</title><link>https://laurentperrinet.github.io/publication/redondo-05/</link><pubDate>Mon, 01 Aug 2005 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/redondo-05/</guid><description>&lt;ul&gt;
&lt;li&gt;relies on log-Gabor filters:
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;span class="article-metadata li-cite-author"&gt;
&lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/sylvain-fischer/"&gt;Sylvain Fischer&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/filip-%C5%A1roubek/"&gt;Filip Šroubek&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/laurent-u-perrinet/"&gt;Laurent U Perrinet&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/rafael-redondo/"&gt;Rafael Redondo&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/gabriel-crist%C3%B3bal/"&gt;Gabriel Cristóbal&lt;/a&gt;&lt;/span&gt;
&lt;/span&gt;
(2007).
&lt;a href="https://laurentperrinet.github.io/publication/fischer-07-cv/"&gt;Self-Invertible 2D Log-Gabor Wavelets&lt;/a&gt;.
&lt;em&gt;International Journal of Computer Vision&lt;/em&gt;.
&lt;p&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://laurentperrinet.github.io/publication/fischer-07-cv/fischer-07-cv.pdf" target="_blank" rel="noopener"&gt;
PDF
&lt;/a&gt;
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publication/fischer-07-cv/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1007/s11263-006-0026-8" target="_blank" rel="noopener"&gt;
DOI
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://github.com/bicv/LogGabor" target="_blank" rel="noopener"&gt;
Code&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1007/s11263-006-0026-8" target="_blank" rel="noopener"&gt;
URL&lt;/a&gt;
&lt;/p&gt;
&lt;/div&gt;
&lt;figure id="figure-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"&gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img src="https://laurentperrinet.github.io/publication/fischer-07/figure2.png" alt="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). " loading="lazy" data-zoomable width="80%" /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;figcaption&gt;
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).
&lt;/figcaption&gt;&lt;/figure&gt;
&lt;/li&gt;
&lt;/ul&gt;</description></item></channel></rss>