{"id":216381,"date":"2025-06-21T06:24:14","date_gmt":"2025-06-21T11:24:14","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2025\/06\/all-topographic-neural-networks-more-closely-mimic-the-human-visual-system"},"modified":"2025-06-21T06:24:14","modified_gmt":"2025-06-21T11:24:14","slug":"all-topographic-neural-networks-more-closely-mimic-the-human-visual-system","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2025\/06\/all-topographic-neural-networks-more-closely-mimic-the-human-visual-system","title":{"rendered":"All-topographic neural networks more closely mimic the human visual system"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/all-topographic-neural-networks-more-closely-mimic-the-human-visual-system.jpg\"><\/a><\/p>\n<p>Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are designed to partly emulate the functioning and structure of biological neural networks. As a result, in addition to tackling various real-world computational problems, they could help neuroscientists and psychologists to better understand the underpinnings of specific sensory or cognitive processes.<\/p>\n<p>Researchers at Osnabr\u00fcck University, Freie Universit\u00e4t Berlin and other institutes recently developed a new class of artificial neural networks (ANNs) that could mimic the human visual system better than CNNs and other existing deep learning algorithms. Their newly proposed, visual system-inspired computational techniques, dubbed all-topographic neural networks (All-TNNs), are introduced in a paper <a href=\"https:\/\/www.nature.com\/articles\/s41562-025-02220-7\" target=\"_blank\">published<\/a> in Nature Human Behaviour.<\/p>\n<p>\u201cPreviously, the most powerful models for understanding how the brain processes visual information were derived off of AI vision models,\u201d Dr. Tim Kietzmann, senior author of the paper, told Tech Xplore.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are designed to partly emulate the functioning and structure of biological neural networks. As a result, in addition to tackling various real-world computational problems, they could help neuroscientists and psychologists to better understand the underpinnings of specific sensory or cognitive processes. [\u2026]<\/p>\n","protected":false},"author":427,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3,41,6],"tags":[],"class_list":["post-216381","post","type-post","status-publish","format-standard","hentry","category-biological","category-information-science","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/216381","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/427"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=216381"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/216381\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=216381"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=216381"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=216381"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}