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Lookup NU author(s): Dr Srikanth RamaswamyORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Copyright © 2022 the authors. Biological neural networks adapt and learn in diverse behavioral contexts. Artificial neural networks (ANNs) have exploited biological properties to solve complex problems. However, despite their effectiveness for specific tasks, ANNs are yet to realize the flexibility and adaptability of biological cognition. This review highlights recent advances in computational and experimental research to advance our understanding of biological and artificial intelligence. In particular, we discuss critical mechanisms from the cellular, systems, and cognitive neuroscience fields that have contributed to refining the architecture and training algorithms of ANNs. Additionally, we discuss how recent work used ANNs to understand complex neuronal correlates of cognition and to process high throughput behavioral data.
Author(s): Cohen Y, Engel TA, Langdon C, Lindsay GW, Ott T, Peters MAK, Shine JM, Breton-Provencher V, Ramaswamy S
Publication type: Article
Publication status: Published
Journal: Journal of Neuroscience
Year: 2022
Volume: 42
Issue: 45
Pages: 8514-8523
Online publication date: 09/11/2022
Acceptance date: 03/10/2022
Date deposited: 05/03/2025
ISSN (print): 0270-6474
ISSN (electronic): 1529-2401
Publisher: Society for Neuroscience
URL: https://doi.org/10.1523/JNEUROSCI.1503-22.2022
DOI: 10.1523/JNEUROSCI.1503-22.2022
ePrints DOI: 10.57711/8s4q-g858
PubMed id: 36351830
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