Recent Advances at the Interface of Neuroscience and Artificial Neural Networks

Cohen, Yarden, Engel, Tatiana A, Langdon, Christopher, Lindsay, Grace W, Ott, Torben, Peters, Megan AK, Shine, James M, Breton-Provencher, Vincent, Ramaswamy, Srikanth (November 2022) Recent Advances at the Interface of Neuroscience and Artificial Neural Networks. The Journal of Neuroscience, 42 (45). pp. 8514-8523. ISSN 1529-2401

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Abstract

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.

Item Type:	Paper
Subjects:	bioinformatics > computational biology > algorithms organs, tissues, organelles, cell types and functions > tissues types and functions > neural networks neurobiology > neuroscience
CSHL Authors:	Engel, Tatiana Langdon, Christopher
Communities:	CSHL labs > Engel lab
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	9 November 2022
Date Deposited:	14 Nov 2022 17:26
Last Modified:	14 Nov 2022 17:26
URI:	https://repository.cshl.edu/id/eprint/40755

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