An explainable artificial intelligence approach to spatial navigation based on hippocampal circuitry.
In: Neural networks : the official journal of the International Neural Network Society, Jg. 163 (2023-06-01), S. 97-107
Online
academicJournal
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.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
Titel: |
An explainable artificial intelligence approach to spatial navigation based on hippocampal circuitry.
|
---|---|
Autor/in / Beteiligte Person: | Coppolino, S ; Migliore, M |
Link: | |
Zeitschrift: | Neural networks : the official journal of the International Neural Network Society, Jg. 163 (2023-06-01), S. 97-107 |
Veröffentlichung: | New York : Pergamon Press, [c1988-, 2023 |
Medientyp: | academicJournal |
ISSN: | 1879-2782 (electronic) |
DOI: | 10.1016/j.neunet.2023.03.030 |
Schlagwort: |
|
Sonstiges: |
|