{"id":13690,"date":"2017-06-08T00:00:05","date_gmt":"2017-06-08T07:00:05","guid":{"rendered":"https:\/\/vermont.salk.edu\/?post_type=disclosure&#038;p=13690"},"modified":"2024-01-30T15:06:18","modified_gmt":"2024-01-30T23:06:18","slug":"brain-recognizes-eye-sees","status":"publish","type":"disclosure","link":"https:\/\/www.salk.edu\/zh\/news-release\/brain-recognizes-eye-sees\/","title":{"rendered":"How the brain recognizes what the eye sees"},"content":{"rendered":"<p>LA JOLLA\u2014If you think self-driving cars can\u2019t get here soon enough, you\u2019re not alone. But programming computers to recognize objects is very technically challenging, especially since scientists don\u2019t fully understand how our own brains do it.<\/p>\n<p>Now, Salk Institute researchers have analyzed how neurons in a critical part of the brain, called V2, respond to natural scenes, providing a better understanding of vision processing. The work is described in <a href=\"https:\/\/www.nature.com\/articles\/ncomms15739\" target=\"_blank\" rel=\"noopener noreferrer\"><em>Nature Communications<\/em><\/a> on June 8, 2017.<\/p>\n<div class=\"row\" style=\"\"><div class=\"col-md-10 col-md-push-1\"><div class=\"video-anchor\" id=\"video-MYu_84TEeLI\"><\/div><div class=\"embed-responsive embed-responsive-16by9\"> <iframe class=\"embed-responsive-item\" src=\"\/\/www.youtube.com\/embed\/MYu_84TEeLI?rel=0\" webkitallowfullscreen mozallowfullscreen allowfullscreen><\/iframe><\/div><!-- .embed-responsive --><\/div><!-- .col-md-*size --><\/div><!-- .\/row -->\n<p>\u201cUnderstanding how the brain recognizes visual objects is important not only for the sake of vision, but also because it provides a window on how the brain works in general,\u201d says <a href=\"https:\/\/www.salk.edu\/zh\/scientist\/tatyana-sharpee\/\" target=\"_blank\" rel=\"noopener noreferrer\">Tatyana Sharpee<\/a>, an associate professor in Salk\u2019s Computational Neurobiology Laboratory and senior author of the paper. \u201cMuch of our brain is composed of a repeated computational unit, called a cortical column. In vision especially we can control inputs to the brain with exquisite precision, which makes it possible to quantitatively analyze how signals are transformed in the brain.\u201d<\/p>\n<p>Although we often take the ability to see for granted, this ability derives from sets of complex mathematical transformations that we are not yet able to reproduce in a computer, according to Sharpee. In fact, more than a third of our brain is devoted exclusively to the task of parsing visual scenes.<\/p>\n<figure id=\"attachment_13693\"  class=\"wp-caption alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"458\" height=\"267\" class=\"img-responsive wp-image-13693 size-col-md-5\" src=\"https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/teddy-1444642_1920-combined-458x267.jpg\" alt=\"\" srcset=\"https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/teddy-1444642_1920-combined-458x267.jpg 458w, https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/teddy-1444642_1920-combined-300x175.jpg 300w, https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/teddy-1444642_1920-combined-768x448.jpg 768w, https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/teddy-1444642_1920-combined-1024x597.jpg 1024w, https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/teddy-1444642_1920-combined-147x86.jpg 147w, https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/teddy-1444642_1920-combined-585x341.jpg 585w, https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/teddy-1444642_1920-combined-553x323.jpg 553w, https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/teddy-1444642_1920-combined-750x438.jpg 750w, https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/teddy-1444642_1920-combined-945x551.jpg 945w, https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/teddy-1444642_1920-combined.jpg 1500w\" sizes=\"auto, (max-width: 458px) 100vw, 458px\" \/><figcaption class=\"wp-caption-text\">The illustration on the right shows how the brain&#8217;s V1 and V2 areas might use information about edges and textures to represent objects like the teddy bear on the left. <\/p>\n<p> <a href=\"https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/teddy-1444642_1920-combined.jpg\" target=\"_blank\" rel=\"noopener noreferrer\">Click here<\/a> for a high-resolution image. <\/p>\n<p> Credit: Salk Institute<\/figcaption><\/figure>\n<p>Our visual perception starts in the eye with light and dark pixels. These signals are sent to the back of the brain to an area called V1 where they are transformed to correspond to edges in the visual scenes. Somehow, as a result of several subsequent transformations of this information, we then can recognize faces, cars and other objects and whether they are moving. How precisely this recognition happens is still a mystery, in part because neurons that encode objects respond in complicated ways.<\/p>\n<p>Now, Sharpee and Ryan Rowekamp, a postdoctoral research associate in Sharpee\u2019s group, have developed a statistical method that takes these complex responses and describes them in interpretable ways, which could be used to help decode vision for computer-simulated vision. To develop their model, the team used publicly available data showing brain responses of primates watching movies of natural scenes (such as forest landscapes) from the Collaborative Research in Computational Neuroscience (CRCNS) database.<\/p>\n<p>\u201cWe applied our new statistical technique in order to figure out what features in the movie were causing V2 neurons to change their responses,\u201d says Rowekamp. \u201cInterestingly, we found that V2 neurons were responding to combinations of edges.\u201d<\/p>\n<figure id=\"attachment_13697\"  class=\"wp-caption alignleft\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"231\" class=\"img-responsive wp-image-13697 size-pr-300\" src=\"https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/Tatyana-Sharpee_Ryan-Rowekamp_e-300x231.jpg\" alt=\"\" srcset=\"https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/Tatyana-Sharpee_Ryan-Rowekamp_e-300x231.jpg 300w, https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/Tatyana-Sharpee_Ryan-Rowekamp_e-768x591.jpg 768w, https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/Tatyana-Sharpee_Ryan-Rowekamp_e-1024x788.jpg 1024w, https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/Tatyana-Sharpee_Ryan-Rowekamp_e-147x113.jpg 147w, https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/Tatyana-Sharpee_Ryan-Rowekamp_e-458x353.jpg 458w, https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/Tatyana-Sharpee_Ryan-Rowekamp_e-585x450.jpg 585w, https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/Tatyana-Sharpee_Ryan-Rowekamp_e-553x426.jpg 553w, https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/Tatyana-Sharpee_Ryan-Rowekamp_e-750x578.jpg 750w, https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/Tatyana-Sharpee_Ryan-Rowekamp_e-945x728.jpg 945w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><figcaption class=\"wp-caption-text\">From left: Tatyana Sharpee and Ryan Rowekamp <\/p>\n<p> <a href=\"https:\/\/www.salk.edu\/wp-content\/uploads\/2017\/06\/Tatyana-Sharpee_Ryan-Rowekamp_e.jpg\" target=\"_blank\" rel=\"noopener noreferrer\">Click here<\/a> for a high-resolution image. <\/p>\n<p> Credit: Salk Institute<\/figcaption><\/figure>\n<p>The team revealed that V2 neurons process visual information according to three principles: first, they combine edges that have similar orientations, increasing robustness of perception to small changes in the position of curves that form object boundaries. Second, if a neuron is activated by an edge of a particular orientation and position, then the orientation 90 degrees from that will be suppressive at the same location, a combination termed \u201ccross-orientation suppression.\u201d These cross-oriented edge combinations are assembled in various ways to allow us to detect various visual shapes. The team found that cross-orientation was essential for accurate shape detection. The third principle is that relevant patterns are repeated in space in ways that can help perceive textured surfaces of trees or water and boundaries between them, as in impressionist paintings.<\/p>\n<p>The researchers incorporated the three organizing principles into a model they named the Quadratic Convolutional model, which can be applied to other sets of experimental data. Visual processing is likely to be similar to how the brain processes smells, touch or sounds, the researchers say, so the work could elucidate processing of data from these areas as well.<\/p>\n<p>\u201cModels I had worked on before this weren\u2019t entirely compatible with the data, or weren\u2019t cleanly compatible,\u201d says Rowekamp. \u201cSo it was really satisfying when the idea of combining edge recognition with sensitivity to texture started to pay off as a tool to analyze and understand complex visual data.\u201d<\/p>\n<p>But the more immediate application might be to improve object-recognition algorithms for self-driving cars or other robotic devices. \u201cIt seems that every time we add elements of computation that are found in the brain to computer-vision algorithms, their performance improves,\u201d says Sharpee.<\/p>\n<p>The work was funded by the <a href=\"https:\/\/www.nsf.gov\/\" target=\"_blank\" rel=\"noopener noreferrer\">National Science Foundation<\/a>\u00a0and the <a href=\"https:\/\/www.nei.nih.gov\/\">National Eye Institute.<\/a><\/p>","protected":false},"featured_media":13691,"template":"","faculty":[66],"disease-research":[332,124,464],"class_list":["post-13690","disclosure","type-disclosure","status-publish","has-post-thumbnail","hentry","faculty-tatyana-sharpee","disease-research-computational-biology","disease-research-neuroscience-and-neurological-disorders","disease-research-perception"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>How the brain recognizes what the eye sees - Salk Institute for Biological Studies<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.salk.edu\/zh\/news-release\/brain-recognizes-eye-sees\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How the brain recognizes what the eye sees - Salk Institute for Biological Studies\" \/>\n<meta property=\"og:description\" content=\"LA JOLLA\u2014If you think self-driving cars can\u2019t get here soon enough, you\u2019re not alone. 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