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          <dc:title><![CDATA[An explainable artificial intelligence approach to spatial navigation based on hippocampal circuitry]]></dc:title>
          <dc:creator><![CDATA[Coppolino S.]]></dc:creator>
          <dc:creator><![CDATA[Migliore M.]]></dc:creator>
          <dc:language><![CDATA[eng]]></dc:language>
          <dc:description><![CDATA[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 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. 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. 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, 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. We called this model SLT (Single Learning Trial). In the current work, 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. We show the conditions under which the e-SLT network, including cells coding for places, head-direction, and objects, can robustly and efficiently implement a fundamental cognitive function. The results shed light on the possible circuit organization and operation of the hippocampus and may represent the building block of a new generation of artificial intelligence algorithms for spatial navigation.]]></dc:description>
          <dc:source><![CDATA[Neural networks 163 (2023): 97–107. doi:10.1016/j.neunet.2023.03.030]]></dc:source>
          <dc:source><![CDATA[info:cnr-pdr/source/autori:Coppolino S.; Migliore M./titolo:An explainable artificial intelligence approach to spatial navigation based on hippocampal circuitry/doi:10.1016%2Fj.neunet.2023.03.030/rivista:Neural networks/anno:2023/pagina_da:97/pagina_a:107/intervallo_pagine:97–107/volume:163]]></dc:source>
          <dc:publisher><![CDATA[Pergamon,, New York , Stati Uniti d'America]]></dc:publisher>
          <dc:date><![CDATA[2023]]></dc:date>
          <dc:identifier><![CDATA[http://www.cnr.it/prodotto/i/482557]]></dc:identifier>
          <dc:identifier><![CDATA[https://publications.cnr.it/doc/482557]]></dc:identifier>
          <dc:identifier><![CDATA[https://dx.doi.org/10.1016/j.neunet.2023.03.030]]></dc:identifier>
          <dc:identifier><![CDATA[info:doi:10.1016/j.neunet.2023.03.030]]></dc:identifier>
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          <dc:relation><![CDATA[info:cnr-pdr/author/idpersonaleesterno:35553/COPPOLINO/SIMONE]]></dc:relation>
          <dc:relation><![CDATA[info:cnr-pdr/author/matricola:13673/MIGLIORE/MICHELE]]></dc:relation>
          <dc:rights><![CDATA[info:eu-repo/semantics/openAccess]]></dc:rights>
          <dc:subject><![CDATA[Robot spatial navigation; Spike-time-dependent plasticity; Hippocampal circuitry; Spiking neurons network]]></dc:subject>
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