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The reinforcement metalearner as a biologically plausible meta-learning framework

Published online by Cambridge University Press:  23 September 2024

Tim Vriens Open the ORCID record for Tim Vriens [Opens in a new window] ,
Mattias Horan Open the ORCID record for Mattias Horan [Opens in a new window] ,
Jacqueline Gottlieb Open the ORCID record for Jacqueline Gottlieb [Opens in a new window]  and
Massimo Silvetti
Tim Vriens
Affiliation:
Institute of Cognitive Sciences and Technologies, CNR, Rome, Italy [email protected], [email protected] https://ctnlab.it/index.php/massimo-silvetti/, https://www.istc.cnr.it/en/people/massimo-silvetti
Mattias Horan
Affiliation:
Sainsbury Wellcome Centre, University College London, London, UK [email protected],
Jacqueline Gottlieb*
Affiliation:
Department of Neuroscience, Columbia University, New York, NY, USA [email protected], https://zuckermaninstitute.columbia.edu/jacqueline-gottlieb-phd Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
Massimo Silvetti
Affiliation:
Institute of Cognitive Sciences and Technologies, CNR, Rome, Italy [email protected], [email protected] https://ctnlab.it/index.php/massimo-silvetti/, https://www.istc.cnr.it/en/people/massimo-silvetti
*
*Corresponding author.
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Abstract

We argue that the type of meta-learning proposed by Binz et al. generates models with low interpretability and falsifiability that have limited usefulness for neuroscience research. An alternative approach to meta-learning based on hyperparameter optimization obviates these concerns and can generate empirically testable hypotheses of biological computations.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 47 , 2024 , e168
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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