@article{cohen_recent_2022,
	title = {Recent {Advances} at the {Interface} of {Neuroscience} and {Artificial} {Neural} {Networks}},
	volume = {42},
	copyright = {Copyright © 2022 the authors. SfN exclusive license.},
	issn = {0270-6474, 1529-2401},
	url = {https://www.jneurosci.org/content/42/45/8514},
	doi = {10.1523/JNEUROSCI.1503-22.2022},
	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.},
	language = {en},
	number = {45},
	urldate = {2023-03-18},
	journal = {Journal of Neuroscience},
	author = {Cohen, Yarden and Engel, Tatiana A. and Langdon, Christopher and Lindsay, Grace W. and Ott, Torben and Peters, Megan A. K. and Shine, James M. and Breton-Provencher, Vincent and Ramaswamy, Srikanth},
	month = nov,
	year = {2022},
	pmid = {36351830},
	note = {Publisher: Society for Neuroscience
Section: Symposia},
	keywords = {cognition, plasticity, artificial neural networks, behavior, neuromodulators, vision},
	pages = {8514--8523},
}

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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.","language":"en","number":"45","urldate":"2023-03-18","journal":"Journal of Neuroscience","author":[{"propositions":[],"lastnames":["Cohen"],"firstnames":["Yarden"],"suffixes":[]},{"propositions":[],"lastnames":["Engel"],"firstnames":["Tatiana","A."],"suffixes":[]},{"propositions":[],"lastnames":["Langdon"],"firstnames":["Christopher"],"suffixes":[]},{"propositions":[],"lastnames":["Lindsay"],"firstnames":["Grace","W."],"suffixes":[]},{"propositions":[],"lastnames":["Ott"],"firstnames":["Torben"],"suffixes":[]},{"propositions":[],"lastnames":["Peters"],"firstnames":["Megan","A.","K."],"suffixes":[]},{"propositions":[],"lastnames":["Shine"],"firstnames":["James","M."],"suffixes":[]},{"propositions":[],"lastnames":["Breton-Provencher"],"firstnames":["Vincent"],"suffixes":[]},{"propositions":[],"lastnames":["Ramaswamy"],"firstnames":["Srikanth"],"suffixes":[]}],"month":"November","year":"2022","pmid":"36351830","note":"Publisher: Society for Neuroscience Section: Symposia","keywords":"cognition, plasticity, artificial neural networks, behavior, neuromodulators, vision","pages":"8514–8523","bibtex":"@article{cohen_recent_2022,\n\ttitle = {Recent {Advances} at the {Interface} of {Neuroscience} and {Artificial} {Neural} {Networks}},\n\tvolume = {42},\n\tcopyright = {Copyright © 2022 the authors. SfN exclusive license.},\n\tissn = {0270-6474, 1529-2401},\n\turl = {https://www.jneurosci.org/content/42/45/8514},\n\tdoi = {10.1523/JNEUROSCI.1503-22.2022},\n\tabstract = {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. 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