{"id":2865,"date":"2023-05-08T11:47:59","date_gmt":"2023-05-08T01:47:59","guid":{"rendered":"https:\/\/www.cognav.net\/?p=2865"},"modified":"2023-05-08T11:47:59","modified_gmt":"2023-05-08T01:47:59","slug":"how-to-implement-an-explainable-artificial-intelligence-approach-to-spatial-navigation-based-on-hippocampal-circuitry","status":"publish","type":"post","link":"https:\/\/braininspirednavigation.com\/?p=2865","title":{"rendered":"How to implement an explainable artificial intelligence approach to spatial navigation based on hippocampal circuitry?"},"content":{"rendered":"<p style=\"text-align: justify;\">Simone Coppolino, Michele Migliore. <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S089360802300165X?via%3Dihub\"><strong>An explainable artificial intelligence approach to spatial navigation based on hippocampal circuitry<\/strong><\/a>.\u00a0Neural Networks,\u00a0Volume 163, June 2023, Pages 97-107.<\/p>\n<p style=\"text-align: justify;\">Abstract<br \/>\n&#8220;<strong><span style=\"color: #ff0000;\">Learning to navigate a complex environment is not a difficult task for a mammal.<\/span><\/strong> For example, finding the correct way to exit a maze following a sequence of cues, does not need a long training session. Just a single or a few runs through a new environment is, in most cases, sufficient to learn an exit path starting from anywhere in the maze. <strong><span style=\"color: #ff0000;\">This ability is in striking contrast with the well-known difficulty that any deep learning algorithm has in learning a trajectory through a sequence of objects<\/span><\/strong>. Being able to learn an arbitrarily long sequence of objects to reach a specific place could take, in general, prohibitively long training sessions. This is a clear indication that current artificial intelligence methods are essentially unable to capture the way in which a real brain implements a cognitive function. In previous work, <strong><span style=\"color: #ff0000;\">we have proposed a proof-of-principle model demonstrating how, using hippocampal circuitry, it is possible to learn an arbitrary sequence of known objects in a single trial<\/span><\/strong>. We called this model SLT (Single Learning Trial). In the current work, <strong><span style=\"color: #ff0000;\">we extend this model, which we will call e-STL, to introduce the capability of navigating a classic four-arms maze to learn, in a single trial, the correct path to reach an exit ignoring dead ends<\/span><\/strong>. We show the conditions under which the e-SLT network,<strong><span style=\"color: #ff0000;\"> including cells coding for places, head-direction, and objects, can robustly and efficiently implement a fundamental cognitive function<\/span><\/strong>. The results shed light on the possible circuit organization and operation of the hippocampus and <strong><span style=\"color: #ff0000;\">may represent the building block of a new generation of artificial intelligence algorithms for spatial navigation<\/span><\/strong>.&#8221;<\/p>\n<p style=\"text-align: justify;\">Simone Coppolino, Michele Migliore. <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S089360802300165X?via%3Dihub\"><strong>An explainable artificial intelligence approach to spatial navigation based on hippocampal circuitry<\/strong><\/a>.\u00a0Neural Networks,\u00a0Volume 163, June 2023, Pages 97-107.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Simone Coppolino, Michele Migliore. An explainable artificial intelligence approach to spatial navigation based on hippocampal circuitry.\u00a0Neural Networks,\u00a0Volume 163, June 2023, Pages 97-107. Abstract &#8220;Learning to navigate a complex environment is not a difficult task for a mammal. For example, finding the correct way to exit a maze following a sequence of cues, does not need [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[114,96],"tags":[396,1239,644,104,211],"_links":{"self":[{"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/posts\/2865"}],"collection":[{"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2865"}],"version-history":[{"count":1,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/posts\/2865\/revisions"}],"predecessor-version":[{"id":2866,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/posts\/2865\/revisions\/2866"}],"wp:attachment":[{"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2865"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2865"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2865"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}