Recent Advances at the Interface of Neuroscience and Artificial Neural Networks

Yarden Cohen; Tatiana A Engel; Christopher Langdon; Grace W Lindsay; Torben Ott; Megan A K Peters; James M Shine; Vincent Breton-Provencher; Srikanth Ramaswamy

doi:10.1523/JNEUROSCI.1503-22.2022

Recent Advances at the Interface of Neuroscience and Artificial Neural Networks

J Neurosci. 2022 Nov 9;42(45):8514-8523. doi: 10.1523/JNEUROSCI.1503-22.2022.

Authors

Yarden Cohen¹, Tatiana A Engel², Christopher Langdon², Grace W Lindsay³, Torben Ott⁴, Megan A K Peters⁵, James M Shine⁶, Vincent Breton-Provencher⁷, Srikanth Ramaswamy⁸

Affiliations

¹ Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel.
² Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY 11724.
³ Department of Psychology, Center for Data Science, New York University, New York, NY 10003.
⁴ Bernstein Center for Computational Neuroscience Berlin, Institute of Biology, Humboldt University of Berlin, 10117, Berlin, Germany.
⁵ Department of Cognitive Sciences, University of California-Irvine, Irvine, CA 92697.
⁶ Brain and Mind Centre, University of Sydney, Sydney, NSW 2006, Australia.
⁷ Département de psychiatrie et neurosciences, Université Laval, Quebec City, Québec, G1J 2G3, Canada vincent.breton-provencher@cervo.ulaval.ca Srikanth.Ramaswamy@newcastle.ac.uk.
⁸ Biosciences Institute, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom vincent.breton-provencher@cervo.ulaval.ca Srikanth.Ramaswamy@newcastle.ac.uk.

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.

Keywords: artificial neural networks; behavior; cognition; neuromodulators; plasticity; vision.

Publication types

Review
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Algorithms
Artificial Intelligence*
Cognition
Neural Networks, Computer
Neurosciences*