{"id":1339,"date":"2016-11-22T08:00:20","date_gmt":"2016-11-22T07:00:20","guid":{"rendered":"https:\/\/cio.umh.es\/?p=1339"},"modified":"2016-11-22T08:00:20","modified_gmt":"2016-11-22T07:00:20","slug":"conferencia-del-prof-dr-thiago-mosqueiro","status":"publish","type":"post","link":"https:\/\/cio.umh.es\/en\/2016\/11\/22\/conferencia-del-prof-dr-thiago-mosqueiro\/","title":{"rendered":"Conferencia del Prof. Dr. Thiago Mosqueiro"},"content":{"rendered":"

[:es]T\u00edtulo: <\/strong>Fast and stable discrimination in divergent-convergent neural networks: from Deep Learning back to Neuroscience<\/span>
\nPonente: <\/strong>Thiago Mosqueiro<\/span>
\nDate:<\/strong> 28\/11\/2016 12:30 h<\/span>
\nLugar:<\/strong> Sala de Seminarios, Edificio Torretamarit<\/span>
\nResumen:<\/strong><\/span>
\nLa toma de decisiones en el cerebro se modeliza a menudo mediante una red neuronal divergente-convergente en la que la informaci\u00f3n sensorial atraviesa primero una capa en la que se realiza el reconocimiento de patrones y la codificaci\u00f3n del est\u00edmulo (capa de conectividades divergentes), seguida de una capa en la cual se filtra la informaci\u00f3n (capa de conectividades convergentes), antes de llegar a la capa o capas motoras en las que se toma la decisi\u00f3n. Este mecanismo introduce un retardo entre el input sensorial y la respuesta que es inconsistente con la rapidez necesaria en un comportamiento adaptativo. En la charla se hablar\u00e1 de un reciente mecanismo de retroalimentaci\u00f3n que es robusto, favorece codificaci\u00f3n dispersa (sparse coding<\/em>) y acelera la transferencia de informaci\u00f3n a trav\u00e9s de las capas. Tambi\u00e9n se discutir\u00e1n varios ejemplos que ilustran este mecanismo.<\/em><\/span>
\nBreve Bio:<\/strong><\/span>
\nThiago Mosqueiro obtuvo el t\u00edtulo de doctor en Ciencias F\u00edsicas en 2015 por la Universidad de Sao Paulo. Sus intereses cient\u00edficos se centran en la f\u00edsica estad\u00edstica, modelos matem\u00e1ticos, neurociencias computacionales y machine learning<\/em>. Actualmente compagina su labor investigadora con la docencia en el Rady School of Management de UCSD, y colabora con grupos de investigaci\u00f3n de la Universidad de California en Los Angeles y de la Arizona State University.<\/span>[:en]Title: <\/strong>Fast and stable discrimination in divergent-convergent neural networks: from Deep Learning back to Neuroscience
\nSpeaker:<\/strong> Thiago Mosqueiro
\nDate:<\/strong> 28\/11\/2016 12:30 h
\nLocation:<\/strong> Sala de Seminarios, Edificio Torretamarit
\nAbstract:<\/strong>
\nThe coding basis for decision making is often provided by a minimal number of higher-order neurons. Before reaching premotor decision layers, sensory information travels through several neural layers. This multi-layered organization is often composed of (i) divergent connectivities, which are essential for pattern recognition and stimulus codification, and (ii) convergent connectivities, which filter down information. However, this architecture based on multiple neuronal layers induces a time lag between peripheral input and adaptive behavior (output), which is inconsistent with the need for speed. Furthermore, an accentuated divergent-convergent architecture may also amplify noise and generate unstable dynamics, which impairs the sensory representation of external stimuli. In this talk, we discuss a recent feedback mechanism that presents robust gain-control, sustains sparse coding, and accelerates the information transfer through layers. An example of such synaptic organization is the early olfactory processing stage of all insects, the Mushroom Bodies (MBs), where a strong divergence from 2k to 300k neurons is followed by a convergence to only 400 neurons. The stability analysis of this system provided an analytical formula for the gain-control maintenance. In addition to in-vivo recordings, we used data from gas-sensor arrays to show that this architecture learns more complex spatio-temporal patterns. Moreover, we will use data from gas-sensor arrays to motivate pre-training of such network. Thus, because such connectivities are ubiquitous to many brains, we believe divergent-convergent networks play a central role in stable and fast decision-making processes in the brain.
\nBrief Bio:<\/strong>
\nDr. Mosqueiro graduated in Physics (2008) and got his PhD in Physics (2015) from the University of S\u00e3o Paulo, with an internship (2014) at the BioCircuits Institute and Rady School of Management, both part of the University of California San Diego. His main areas of interest are statistical physics, mathematical modeling, computational neuroscience, and machine learning. Thiago is currently a Post Doc at the BioCircuits Institute, working in collaboration with the University of California Los Angeles and the Arizona State University, and a lecturer at the Rady School of Management.[:]<\/p>","protected":false},"excerpt":{"rendered":"

[:es]T\u00edtulo: Fast and stable discrimination in divergent-convergent neural networks: from Deep Learning back to Neuroscience
\nPonente: Thiago Mosqueiro
\nFecha: 28\/11\/2016 12:30 h
\nLugar: Sala de Seminarios, Edificio Torretamarit
\nResumen:
\nLa toma de decisiones en el cerebro se modeliza a menudo mediante una red neuronal divergente-convergente en la que la informaci\u00f3n sensorial atraviesa primero una capa en la que se realiza […]<\/p>","protected":false},"author":3477,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_links_to":"","_links_to_target":""},"categories":[4,873],"tags":[],"_links":{"self":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts\/1339"}],"collection":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/users\/3477"}],"replies":[{"embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/comments?post=1339"}],"version-history":[{"count":0,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts\/1339\/revisions"}],"wp:attachment":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/media?parent=1339"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/categories?post=1339"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/tags?post=1339"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}