{"id":428,"date":"2012-06-14T13:04:44","date_gmt":"2012-06-14T11:04:44","guid":{"rendered":"http:\/\/project.inria.fr\/keops\/?page_id=428"},"modified":"2014-01-08T10:40:03","modified_gmt":"2014-01-08T09:40:03","slug":"outcomes-analysing","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/keops\/outcomes\/outcomes-analysing\/","title":{"rendered":"Outcomes : Analysing"},"content":{"rendered":"<p><strong>Principal investigator : <\/strong><a title=\"\" href=\"http:\/\/www-sop.inria.fr\/members\/Bruno.Cessac\/\" rel=\"nofollow\">Bruno CESSAC<\/a>.<\/p>\n<p><a href=\"http:\/\/project.inria.fr\/keops\/files\/2012\/06\/keopsan.png\"><img loading=\"lazy\" decoding=\"async\" class=\"size-medium wp-image-586 alignleft\" title=\"keopsan\" alt=\"\" src=\"http:\/\/project.inria.fr\/keops\/files\/2012\/06\/keopsan-300x182.png\" width=\"300\" height=\"182\" \/><\/a><strong>On going work : <\/strong>Analyze, at the local network level, the statistical properties of ganglion cells output spike trains, including adaptation mechanisms :<\/p>\n<ul>\n<li>Biological high-order statistics analysis<\/li>\n<li>Algorithm performance improving<\/li>\n<li>Parametric network model estimation<\/li>\n<li>Analyze, at the local network level, the statistical properties of ganglion cells output spike trains, including adaptation mechanisms<\/li>\n<\/ul>\n<p><strong>Publications and outcomes :<\/strong><\/p>\n<ul style=\"font-family: Liberation Sans; color: #3333ff; text-align: justify;\">\n<li><a href=\"http:\/\/lanl.arxiv.org\/pdf\/1309.5873\">Rodrigo Cofr\u00e9, Bruno Cessac, &#8220;Hearing the Maximum Entropy Potential of a spike-generating Markov process&#8221;, submitted to Phys. Rev. Letters.<\/a><\/li>\n<\/ul>\n<ul style=\"font-family: Liberation Sans; color: #3333ff; text-align: justify;\">\n<li><a href=\"http:\/\/arxiv.org\/abs\/1302.5007\">Bruno Cessac and Rodrigo Cofr\u00e9, Spike train statistics and Gibbs distributions,\u00a0 J. Physiol. Paris, Volume 107, Issue 5, Pages 360-368 (November 2013). Special issue: Neural Coding and Natural Image Statistics.<\/a><\/li>\n<\/ul>\n<ul style=\"font-family: Liberation Sans; color: #3333ff; text-align: justify;\">\n<li><a href=\"http:\/\/lanl.arxiv.org\/abs\/1212.3577\">Rodrigo Cofr\u0013\u00e9 and Bruno Cessac Dynamics and spike trains statistics in conductance-based Integrate-and-Fire neural networks with chemical and electric synapses,<i> Chaos, Solitons &amp; Fractals<\/i>, <i>Volume 50<\/i>, <i>May 2013<\/i>, <i>Pages 13-31<\/i>.<\/a><\/li>\n<\/ul>\n<ul style=\"font-family: Liberation Sans; color: #3333ff; text-align: justify;\">\n<li><a href=\"http:\/\/lanl.arxiv.org\/abs\/1209.3886\">Hassan Nasser, Olivier Marre, and Bruno Cessac. Spike trains analysis using gibbs distributions and monte-carlo method&#8221;, <em>J. Stat. Mech.<\/em> (2013) P03006.<\/a><\/li>\n<\/ul>\n<ul>\n<li><a href=\"ftp:\/\/ftp-sop.inria.fr\/neuromathcomp\/team\/bruno.cessac\/Papers\/author.pdf\">Bruno Cessac and Adrian Palacios. Spike train statistics from empirical facts to theory: the case of the retina,<\/a><a href=\"ftp:\/\/ftp-sop.inria.fr\/neuromathcomp\/team\/bruno.cessac\/Papers\/author.pdf\"> in &#8220;Modeling in Computational Biology and Biomedicine: A Multidisciplinary Endeavor&#8221;, F. CAZALS, P.<\/a><a href=\"ftp:\/\/ftp-sop.inria.fr\/neuromathcomp\/team\/bruno.cessac\/Papers\/author.pdf\"> KORNPROBST (editors), Lectures Notes in Mathematical and Computational Biology (LNMCB), Springer-Verlag, 2013.<\/a><\/li>\n<\/ul>\n<ul>\n<li><a href=\"http:\/\/www.mathematical-neuroscience.com\/content\/1\/1\/8\">Cessac, B. (2011) Statistics of spike trains in conductance-based neural networks: Rigorous results, The Journal of Mathematical Neuroscience 2011, 1:8 (25 August 2011).<\/a><\/li>\n<li><a href=\"http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0928425711000441\">J.C. Vasquez, A. Palacios, O. Marre, M.J. Berry II, B. Cessac, Gibbs distribution analysis of temporal correlation structure on multicell spike trains from retina ganglion cells, J. Physiol. Paris, <\/a><a href=\"http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0928425711000441\">, <\/a><a title=\"Go to table of contents for this volume\/issue\" href=\"http:\/\/www.sciencedirect.com\/science\/journal\/09284257\/106\/3\">Volume 106, Issues 3\u20134<\/a>, May\u2013August 2012, Pages 120\u2013127,<\/li>\n<\/ul>\n<p>Event Neural Assembly Simulation (Enas) C++ open-source middleware (interoperationable with Matlab, Java, Python, with Gtk GUI) providing :<br \/>\n\u2013 Statistical methods and numerical tools to analyse and simulate the statistics of spike trains obtained from retina MEA recordings.<br \/>\n\u2013 Variational methods and estimation tools to estimate and optimize the parameters of a neural assembly.<\/p>\n<p><a href=\"http:\/\/enas.gforge.inria.fr\"> http:\/\/enas.gforge.inria.fr<\/a><\/p>","protected":false},"excerpt":{"rendered":"<p>Principal investigator : Bruno CESSAC. On going work : Analyze, at the local network level, the statistical properties of ganglion cells output spike trains, including adaptation mechanisms : Biological high-order statistics analysis Algorithm performance improving Parametric network model estimation Analyze, at the local network level, the statistical properties of ganglion cells output spike trains, including &hellip; <\/p>\n<p><a class=\"more-link btn\" href=\"https:\/\/project.inria.fr\/keops\/outcomes\/outcomes-analysing\/\">Continue reading<\/a><\/p>\n","protected":false},"author":36,"featured_media":586,"parent":413,"menu_order":3,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-428","page","type-page","status-publish","has-post-thumbnail","hentry","nodate","item-wrap"],"_links":{"self":[{"href":"https:\/\/project.inria.fr\/keops\/wp-json\/wp\/v2\/pages\/428","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/project.inria.fr\/keops\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/project.inria.fr\/keops\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/keops\/wp-json\/wp\/v2\/users\/36"}],"replies":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/keops\/wp-json\/wp\/v2\/comments?post=428"}],"version-history":[{"count":37,"href":"https:\/\/project.inria.fr\/keops\/wp-json\/wp\/v2\/pages\/428\/revisions"}],"predecessor-version":[{"id":863,"href":"https:\/\/project.inria.fr\/keops\/wp-json\/wp\/v2\/pages\/428\/revisions\/863"}],"up":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/keops\/wp-json\/wp\/v2\/pages\/413"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/keops\/wp-json\/wp\/v2\/media\/586"}],"wp:attachment":[{"href":"https:\/\/project.inria.fr\/keops\/wp-json\/wp\/v2\/media?parent=428"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}