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The geometry of abstraction in hippocampus and pre-frontal cortex

Silvia Bernardi, Marcus K. Benna, Mattia Rigotti, Jérôme Munuera, Stefano Fusi, C. Daniel Salzman
doi: https://doi.org/10.1101/408633
Silvia Bernardi
2Department of Psychiatry, Columbia University
3Research Foundation for Mental Hygiene
8New York State Psychiatric Institute
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Marcus K. Benna
1Department of Neuroscience, Columbia University
4Center for Theoretical Neuroscience, Columbia University
5Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University
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Mattia Rigotti
7IBM Research AI
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Jérôme Munuera
1Department of Neuroscience, Columbia University
9Current address: Institut du Cerveau et de la Moelle Epinière (UMR 7225), Institut Jean Nicod, Centre National de la Recherche Scientifique (CNRS) UMR 8129, Institut Étude de la Cognition, École normale supérieure
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Stefano Fusi
1Department of Neuroscience, Columbia University
4Center for Theoretical Neuroscience, Columbia University
5Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University
6Kavli Institute for Brain Sciences, Columbia University
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C. Daniel Salzman
1Department of Neuroscience, Columbia University
2Department of Psychiatry, Columbia University
5Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University
6Kavli Institute for Brain Sciences, Columbia University
8New York State Psychiatric Institute
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Abstract

Abstraction can be defined as a cognitive process that identifies common features - abstract variables, or concepts - shared by many examples. Such conceptual knowledge enables subjects to generalize upon encountering new examples, an ability that supports inferential reasoning and cognitive flexibility. To confer the ability to generalize, the brain must represent variables in a particular ‘abstract’ format. Here we show how to construct neural representations that encode multiple variables in an abstract format simultaneously, and we characterize their geometry. Neural representations conforming to this geometry were observed in dorsolateral pre-frontal cortex, anterior cingulate cortex and the hippocampus in monkeys performing a serial reversal-learning task. Similar representations are observed in a simulated multi-layer neural network trained with back-propagation. These findings provide a novel framework for characterizing how different brain areas represent abstract variables that are critical for flexible conceptual generalization.

Footnotes

  • ↵† co-senior authors

  • We asked whether a neural network trained to perform a simulated version of our experimental task would reveal a geometry similar to the one observed in the experiments. We used Deep Q-learning, a technique that uses a deep neural network representation of the state-action value function of an agent trained with a combination of temporal-difference learning and back-propagation refined and popularized by Mnih et al 2015. There are also many new analyses in the Supplementary Information.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted December 09, 2018.
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The geometry of abstraction in hippocampus and pre-frontal cortex
Silvia Bernardi, Marcus K. Benna, Mattia Rigotti, Jérôme Munuera, Stefano Fusi, C. Daniel Salzman
bioRxiv 408633; doi: https://doi.org/10.1101/408633
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The geometry of abstraction in hippocampus and pre-frontal cortex
Silvia Bernardi, Marcus K. Benna, Mattia Rigotti, Jérôme Munuera, Stefano Fusi, C. Daniel Salzman
bioRxiv 408633; doi: https://doi.org/10.1101/408633

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