{"id":929,"date":"2023-06-02T11:54:44","date_gmt":"2023-06-02T14:54:44","guid":{"rendered":"https:\/\/csan2023.saneurociencias.org.ar\/?page_id=929"},"modified":"2023-12-01T11:16:13","modified_gmt":"2023-12-01T14:16:13","slug":"premeeting-courses","status":"publish","type":"page","link":"https:\/\/csan2023.saneurociencias.org.ar\/index.php\/premeeting-courses\/","title":{"rendered":"Premeeting Courses"},"content":{"rendered":"<section class=\"wpb-content-wrapper\"><p>[vc_row full_width=&#8221;stretch_row&#8221; parallax=&#8221;content-moving&#8221; parallax_image=&#8221;532&#8243; css=&#8221;.vc_custom_1685649063726{margin-top: -50px !important;}&#8221;][vc_column]<section id=\"about-header\" class=\"bg-scroll header-info-section division \">\n\t\t\t<div class=\"container white-color\">\t\t\t\t\n\t\t\t\t<div class=\"row\">\t\t\t\t\t\t\n\t\t\t\t\t<div class=\"col-md-8 col-lg-6 col-md-offset-2 col-lg-offset-3 text-center\">\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t<h2 class=\"h2-medium\">Premeeting Courses<\/h2>\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t<p class=\"p-huge\"><\/p>\n\t\t\t\t\t\t\n\t\t\t\t\t<\/div>\t\t\t\t\t\t\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/section>[\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]<\/p>\n<h4><b>C1.- Data Analysis of Calcium Imaging Signals in Neural Circuits:<\/b><\/h4>\n<p>Supported by:<b><br \/>\n<img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-822\" src=\"https:\/\/csan2023.saneurociencias.org.ar\/wp-content\/uploads\/2023\/05\/isn.jpg\" alt=\"\" width=\"170\" height=\"90\" \/><\/b><\/p>\n<p><b>Organizers<\/b><b>: Germ\u00e1n Sumbre<\/b><span style=\"font-weight: 400;\"> (Institut de Biologie de l \u0301\u00c9cole Normale Superieure, CNRS, INSERM) and <\/span><b>Violeta Medan<\/b><span style=\"font-weight: 400;\"> (IFIBYNE-UBA\/CONICET y FCEN-UBA).<\/span><\/p>\n<p><b>Instructors:<\/b><b> Sebasti\u00e1n Romano <\/b><span style=\"font-weight: 400;\">(Instituto de Investigaci\u00f3n en Biomedicina de Buenos Aires, IBIOBA-MPSP, CONICET)<\/span> <span style=\"font-weight: 400;\">and<\/span><b> Emiliano Marachlian<\/b><span style=\"font-weight: 400;\"> (Institut de Biologie de l \u0301\u00c9cole Normale Superieure, CNRS, INSERM).<\/span><\/p>\n<p><b>Teaching Assistants:<\/b> <b>Nicol\u00e1s Martorell <\/b><span style=\"font-weight: 400;\">(IFIBYNE-CONICET\/UBA) and <\/span><b>Ver\u00f3nica P\u00e9rez Schuster <\/b><span style=\"font-weight: 400;\">(iB3-FBMC y DF-FCEN, UBA).<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Audience:\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Undergraduate and graduate students in the fields of biology, physics, engineering, computer science, and related disciplines. Basic programming knowledge, especially in MATLAB and\/or Python, is desirable.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Overview:\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The main aspects of calcium imaging data analysis will be covered, from neuron detection to population-level analysis of calcium signals. The activities will be organized into classes that introduce theoretical concepts during the mornings and practical sessions with pre-collected datasets (provided by the instructors or contributed by the students) in the afternoons, to practice different analysis techniques.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Course objectives:\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">By the end of the course, students are expected to:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Understand the basic concepts of in vivo calcium signal acquisition, advantages, and limitations of different acquisition techniques.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Learn techniques for handling and preprocessing imaging data, with a focus on managing large datasets. Software, toolboxes, and analysis strategies.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Become familiar with typical pipelines for analyzing fluorescence time series.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Grasp the basic concepts of large dataset analysis techniques: topography, dimensionality reduction and clustering, linear regression, and deconvolution.<\/span><\/li>\n<\/ul>\n<h4><b>DOWNLOAD THE PROGRAM: <a href=\"https:\/\/csan2023.saneurociencias.org.ar\/wp-content\/uploads\/2023\/08\/Analisis-de-Datos-de-senales-de-Imaging-de-Calcio-de-circuitos-neuronales.pdf\">An\u00e1lisis de Datos de se\u00f1ales de Imaging de Calcio de circuitos neuronales<\/a><\/b><\/h4>\n<h4><b>C2.- Spatial filtering techniques for electroencephalography signals<\/b><\/h4>\n<p><b>Organizer:<\/b> <b>Victoria Peterson<\/b><span style=\"font-weight: 400;\"> (Instituto de Matem\u00e1tica Aplicada del Litoral, IMAL, UNL-CONICET Santa Fe, Argentina; Facultad de Ingenier\u00eda Qu\u00edmica, FIQ-UNL, Santa Fe, Argentina).<\/span><\/p>\n<p><b>Instructors:<\/b><b> Catalina Mar\u00eda G\u00e1lvan<\/b><span style=\"font-weight: 400;\"> (Instituto de Matem\u00e1tica Aplicada del Litoral, IMAL, UNL-CONICET Santa Fe, Argentina; Facultad de Ingenier\u00eda Qu\u00edmica, FIQ-UNL, Santa Fe, Argentina) and <\/span><b>Bruno Zorzet<\/b><span style=\"font-weight: 400;\"> (Instituto de Investigaci\u00f3n en Se\u00f1ales, Sistemas e Inteligencia Computacional, sinc(i), UNL-CONICET, Santa Fe, Argentina).<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Audience:<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Undergraduate and graduate students in the fields of biology, physics, engineering, computer science, and related disciplines.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Requirements:<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">Basic understanding of linear algebra, optimization and programming (preferably in Python).<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">Overview:<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Brain activity recorded through surface electroencephalography (EEG) can be thought of as the result of a linear mixture of different statistical sources. These sources can originate from the group of neurons underlying the EEG sensor location, as well as neighboring groups of neurons. Additionally, other non-brain sources may be present in the EEG recordings, which ultimately will be defined as signal artifacts.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">Statistical generative models assume that brain signals arise from the activity of uncorrelated sources, and these sources appear distorted in the recorded signal as a consequence of the linear mixing process.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In the context of spatial filtering, the objective is to transform the signal that exists in the &#8220;sensor&#8221; space to the &#8220;source&#8221; space. Spatial filtering methods can be used to improve the signal-to-noise ratio, identify the most correlated source to a specific event, find independent sources, etc. Thus, the application of spatial filters to the EEG signal could be performed for: (i) reducing the dimensionality of the input signal, (ii) feature extraction, (iii) elimination of noise sources. Throughout this course, the main spatial filtering algorithms used in EEG signal processing to enhance the signal-to-noise ratio, extract features, and remove artifacts will be reviewed.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">Course objectives:<\/span><\/p>\n<p><span style=\"font-weight: 400;\">By the end of the course, students are expected to:<\/span><\/p>\n<p><span style=\"font-weight: 400;\">&#8211; Understand the basic neurophysiological concepts underlying electroencephalography signals<\/span><\/p>\n<p><span style=\"font-weight: 400;\">&#8211; Learn basic methods of spatial filtering of time series.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">&#8211; Understand basic concepts of statistical signal processing.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0&#8211; Acquire basic implementation skills in MNE-Python of specific spatial filtering methods.<\/span><\/p>\n<h4><b>DOWNLOAD THE PROGRAM: <a href=\"https:\/\/csan2023.saneurociencias.org.ar\/wp-content\/uploads\/2023\/08\/Programa__EEG.pdf\">Programa__EEG<\/a><\/b><\/h4>\n<p>[\/vc_column_text][\/vc_column][\/vc_row]<\/p>\n<\/section>","protected":false},"excerpt":{"rendered":"<p>[vc_row full_width=&#8221;stretch_row&#8221; parallax=&#8221;content-moving&#8221; parallax_image=&#8221;532&#8243; css=&#8221;.vc_custom_1685649063726{margin-top: -50px !important;}&#8221;][vc_column][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text] C1.- Data Analysis of Calcium Imaging Signals in Neural Circuits: Supported by: Organizers: Germ\u00e1n Sumbre (Institut de Biologie de l \u0301\u00c9cole Normale Superieure, CNRS, INSERM) and Violeta Medan (IFIBYNE-UBA\/CONICET y FCEN-UBA). Instructors: Sebasti\u00e1n Romano (Instituto de Investigaci\u00f3n en Biomedicina de Buenos Aires, IBIOBA-MPSP, CONICET) and Emiliano Marachlian (Institut [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-929","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/csan2023.saneurociencias.org.ar\/index.php\/wp-json\/wp\/v2\/pages\/929","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/csan2023.saneurociencias.org.ar\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/csan2023.saneurociencias.org.ar\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/csan2023.saneurociencias.org.ar\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/csan2023.saneurociencias.org.ar\/index.php\/wp-json\/wp\/v2\/comments?post=929"}],"version-history":[{"count":6,"href":"https:\/\/csan2023.saneurociencias.org.ar\/index.php\/wp-json\/wp\/v2\/pages\/929\/revisions"}],"predecessor-version":[{"id":2435,"href":"https:\/\/csan2023.saneurociencias.org.ar\/index.php\/wp-json\/wp\/v2\/pages\/929\/revisions\/2435"}],"wp:attachment":[{"href":"https:\/\/csan2023.saneurociencias.org.ar\/index.php\/wp-json\/wp\/v2\/media?parent=929"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}