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Shahidi N, Franch M, Parajuli A, Schrater P, Wright A, Pitkow X, Dragoi V. Population coding of strategic variables during foraging in freely moving macaques. Nat Neurosci 2024; 27:772-781. [PMID: 38443701 PMCID: PMC11001579 DOI: 10.1038/s41593-024-01575-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 01/09/2024] [Indexed: 03/07/2024]
Abstract
Until now, it has been difficult to examine the neural bases of foraging in naturalistic environments because previous approaches have relied on restrained animals performing trial-based foraging tasks. Here we allowed unrestrained monkeys to freely interact with concurrent reward options while we wirelessly recorded population activity in the dorsolateral prefrontal cortex. The animals decided when and where to forage based on whether their prediction of reward was fulfilled or violated. This prediction was not solely based on a history of reward delivery, but also on the understanding that waiting longer improves the chance of reward. The task variables were continuously represented in a subspace of the high-dimensional population activity, and this compressed representation predicted the animal's subsequent choices better than the true task variables and as well as the raw neural activity. Our results indicate that monkeys' foraging strategies are based on a cortical model of reward dynamics as animals freely explore their environment.
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Affiliation(s)
- Neda Shahidi
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX, USA
- Georg-Elias-Müller-Institute for Psychology, Georg August-Universität, Göttingen, Germany
- Cognitive Neuroscience Laboratory, German Primate Center, Göttingen, Germany
| | - Melissa Franch
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX, USA
| | - Arun Parajuli
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX, USA
| | - Paul Schrater
- Department of Computer Science, University of Minnesota, Minneapolis, MN, USA
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Anthony Wright
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX, USA
| | - Xaq Pitkow
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA.
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Valentin Dragoi
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX, USA.
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA.
- Neuroengineering Initiative, Rice University, Houston, TX, USA.
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