<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Large-Scale-Networks | Laurent Perrinet</title><link>https://laurentperrinet.github.io/tag/large-scale-networks/</link><atom:link href="https://laurentperrinet.github.io/tag/large-scale-networks/index.xml" rel="self" type="application/rss+xml"/><description>Large-Scale-Networks</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>Tue, 17 Sep 2013 00:00:00 +0000</lastBuildDate><image><url>https://laurentperrinet.github.io/media/icon_hu_f2990a9a83ba401.png</url><title>Large-Scale-Networks</title><link>https://laurentperrinet.github.io/tag/large-scale-networks/</link></image><item><title>Anisotropic connectivity implements motion-based prediction in a spiking neural network</title><link>https://laurentperrinet.github.io/publication/kaplan-13/</link><pubDate>Tue, 17 Sep 2013 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/kaplan-13/</guid><description>&lt;ul&gt;
&lt;li&gt;Based on
&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/guillaume-s-masson/"&gt;Guillaume S Masson&lt;/a&gt;&lt;/span&gt;
&lt;/span&gt;
(2012).
&lt;a href="https://laurentperrinet.github.io/publication/perrinet-12-pred/"&gt;Motion-based prediction is sufficient to solve the aperture problem&lt;/a&gt;.
&lt;em&gt;Neural Computation&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-12-pred/perrinet-12-pred.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-12-pred/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3472550/" target="_blank" rel="noopener"&gt;
Pdf&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://arxiv.org/abs/1208.6471" target="_blank" rel="noopener"&gt;
arXiv&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1162/NECO_a_00332" target="_blank" rel="noopener"&gt;
Doi&lt;/a&gt;
&lt;/p&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;li&gt;see follow-up on motion extrapolation:
&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/mina-a-khoei/"&gt;Mina A Khoei&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/guillaume-s-masson/"&gt;Guillaume S Masson&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;
(2013).
&lt;a href="https://laurentperrinet.github.io/publication/khoei-13-jpp/"&gt;Motion-based prediction explains the role of tracking in motion extrapolation&lt;/a&gt;.
&lt;em&gt;Journal of Physiology-Paris&lt;/em&gt;.
&lt;p&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://laurentperrinet.github.io/publication/khoei-13-jpp/khoei-13-jpp.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/khoei-13-jpp/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1016/j.jphysparis.2013.08.001" target="_blank" rel="noopener"&gt;
DOI
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://laurentperrinet.github.io/publication/khoei-13-jpp/" target="_blank" rel="noopener"&gt;
URL&lt;/a&gt;
&lt;/p&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;li&gt;see follow-up on the flash-lag effect:
&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/mina-a-khoei/"&gt;Mina A Khoei&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
&lt;a href="https://laurentperrinet.github.io/author/guillaume-s-masson/"&gt;Guillaume S Masson&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;
(2017).
&lt;a href="https://laurentperrinet.github.io/publication/khoei-masson-perrinet-17/"&gt;The flash-lag effect as a motion-based predictive shift&lt;/a&gt;.
&lt;em&gt;PLoS Computational Biology&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/khoei-masson-perrinet-17/cite.bib"&gt;
Cite
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://laurentperrinet.github.io/slides/2022-11-21_flash-lag-effect/" target="_blank"&gt;
Slides
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1371/journal.pcbi.1005068" target="_blank" rel="noopener"&gt;
DOI
&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="hhttps://www.insb.cnrs.fr/fr/cnrsinfo/illusions-visuelles-leur-origine-est-dans-la-prediction" &gt;
Press&lt;/a&gt;
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://github.com/laurentperrinet/Khoei_2017_PLoSCB" 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/khoei-masson-perrinet-17/" 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-01771125" target="_blank" rel="noopener"&gt;
HAL&lt;/a&gt;
&lt;/p&gt;
&lt;/div&gt;
lication/khoei-13-jpp&amp;quot; view=&amp;ldquo;4&amp;rdquo; &amp;gt;}}&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Functional consequences of correlated excitation and inhibition on single neuron integration and signal propagation through synfire chains</title><link>https://laurentperrinet.github.io/publication/kremkow-09-gns/</link><pubDate>Thu, 01 Jan 2009 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/kremkow-09-gns/</guid><description>&lt;ul&gt;
&lt;li&gt;see this subsequent paper in the &lt;a href="https://laurentperrinet.github.io/publication/kremkow-10-jcns/"&gt;Journal of Computational Neuroscience&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Functional properties of feed-forward inhibition</title><link>https://laurentperrinet.github.io/publication/kremkow-08-neurocomp/</link><pubDate>Wed, 01 Oct 2008 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/kremkow-08-neurocomp/</guid><description>&lt;ul&gt;
&lt;li&gt;see this subsequent paper in the &lt;a href="https://laurentperrinet.github.io/publication/kremkow-10-jcns/"&gt;Journal of Computational Neuroscience&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Control of the temporal interplay between excitation and inhibition by the statistics of visual input: a V1 network modelling study</title><link>https://laurentperrinet.github.io/publication/kremkow-08-sfn/</link><pubDate>Tue, 01 Jan 2008 00:00:00 +0000</pubDate><guid>https://laurentperrinet.github.io/publication/kremkow-08-sfn/</guid><description>&lt;ul&gt;
&lt;li&gt;see this subsequent paper in the &lt;a href="https://laurentperrinet.github.io/publication/kremkow-10-jcns/"&gt;Journal of Computational Neuroscience&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;</description></item></channel></rss>