Neurorobots as a Means Toward Neuroethology and Explainable AI
Understanding why deep neural networks and machine learning algorithms act as they do is a difficult endeavor. Neuroscientists are faced with similar problems. One way biologists address this issue is by closely observing behavior while recording neurons or manipulating brain circuits. This has been...
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Published in | Frontiers in neurorobotics Vol. 14; p. 570308 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
Lausanne
Frontiers Research Foundation
19.10.2020
Frontiers Media S.A |
Subjects | |
Online Access | Get full text |
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Summary: | Understanding why deep neural networks and machine learning algorithms act as they do is a difficult endeavor. Neuroscientists are faced with similar problems. One way biologists address this issue is by closely observing behavior while recording neurons or manipulating brain circuits. This has been called neuroethology. In a similar way, neurorobotics can be used to explain how neural network activity leads to behavior. In real world settings, neurorobots have been shown to perform behaviors analogous to animals. Moreover, a neuroroboticist has total control over the network, and by analyzing different neural groups or studying the effect of network perturbations (e.g., simulated lesions), they may be able to explain how the robot’s behavior arises from artificial brain activity. In this paper, we review neurorobot experiments by focusing on how the robot’s behavior leads to a qualitative and quantitative explanation of neural activity, and vice versa, that is, how neural activity leads to behavior. We suggest that using neurorobots as a form of computational neuroethology can be a powerful methodology for understanding neuroscience, as well as for artificial intelligence and machine learning. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-3 content type line 23 ObjectType-Review-1 These authors have contributed equally to this work Reviewed by: Luca Leonardo Bologna, Italian National Research Council, Italy; Onofrio Gigliotta, University of Naples Federico II, Italy Edited by: Yongping Pan, National University of Singapore, Singapore |
ISSN: | 1662-5218 1662-5218 |
DOI: | 10.3389/fnbot.2020.570308 |