Article ; Online: An explainable artificial intelligence approach to spatial navigation based on hippocampal circuitry.
Neural networks : the official journal of the International Neural Network Society
2023 Volume 163, Page(s) 97–107
Abstract: 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 ... ...
| Abstract | 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. |
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| MeSH term(s) | Animals ; Artificial Intelligence ; Spatial Navigation ; Hippocampus ; Cues ; Brain ; Maze Learning ; Mammals |
| Language | English |
| Publishing date | 2023-03-30 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 740542-x |
| ISSN | 1879-2782 ; 0893-6080 |
| ISSN (online) | 1879-2782 |
| ISSN | 0893-6080 |
| DOI | 10.1016/j.neunet.2023.03.030 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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