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Learning, fast and slow

Meister, Markus (2022) Learning, fast and slow. Current Opinion in Neurobiology, 75 . Art. No. 102555. ISSN 0959-4388. doi:10.1016/j.conb.2022.102555.

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Animals can learn efficiently from a single experience and change their future behavior in response. However, in other instances, animals learn very slowly, requiring thousands of experiences. Here, I survey tasks involving fast and slow learning and consider some hypotheses for what differentiates the underlying neural mechanisms. It has been proposed that fast learning relies on neural representations that favor efficient Hebbian modification of synapses. These efficient representations may be encoded in the genome, resulting in a repertoire of fast learning that differs across species. Alternatively, the required neural representations may be acquired from experience through a slow process of unsupervised learning from the environment.

Item Type:Article
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URLURL TypeDescription ItemCode and data
Meister, Markus0000-0003-2136-6506
Additional Information:© 2022 Elsevier. Available online 23 May 2022, Version of Record 23 May 2022. This review comes from a themed issue on Neurobiology of Behavior; Edited by Tiago Branco and Mala Murthy. MM acknowledges support from NIH (R01 NS111477), the Simons Collaboration on the Global Brain (543015) and the Tianqiao and Chrissy Chen Institute for Neuroscience. Thanks to Ralph Adolphs, Pietro Perona, Ueli Rutishauser, Doris Tsao, and Tony Zador for helpful comments and critiques. Code and data. Code and data supporting this article can be found at Conflict of interest statement: Nothing declared.
Group:Tianqiao and Chrissy Chen Institute for Neuroscience
Funding AgencyGrant Number
NIHR01 NS111477
Simons Foundation543015
Tianqiao and Chrissy Chen Institute for NeuroscienceUNSPECIFIED
Record Number:CaltechAUTHORS:20220607-425325000
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Official Citation:Markus Meister, Learning, fast and slow, Current Opinion in Neurobiology, Volume 75, 2022, 102555, ISSN 0959-4388,
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115059
Deposited By: George Porter
Deposited On:07 Jun 2022 22:17
Last Modified:07 Jun 2022 22:17

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