Evolving schema representations in orbitofrontal ensembles during learning
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Date
2020-12-23
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Zhou, Jingfeng; Jia, Chunying; Montesinos-Cartagena, Marlian; Gardner, Matthew P. H.; Zong, Wenhui; Schoenbaum, Geoffrey; Evolving schema representations in orbitofrontal ensembles during learning; Nature (2020); https://www.nature.com/articles/s41586-020-03061-2
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law
Public Domain Mark 1.0
This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law
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Abstract
How do we learn about what to learn about? Specifically, how do the neural elements in our brain generalize what has been learned in one situation to recognize the common structure of—and speed learning in—other, similar situations? We know this happens because we become better at solving new problems—learning and deploying schemas1,2,3,4,5—through experience. However, we have little insight into this process. Here we show that using prior knowledge to facilitate learning is accompanied by the evolution of a neural schema in the orbitofrontal cortex. Single units were recorded from rats deploying a schema to learn a succession of odour-sequence problems. With learning, orbitofrontal cortex ensembles converged onto a low-dimensional neural code across both problems and subjects; this neural code represented the common structure of the problems and its evolution accelerated across their learning. These results demonstrate the formation and use of a schema in a prefrontal brain region to support a complex cognitive operation. Our results not only reveal a role for the orbitofrontal cortex in learning but also have implications for using ensemble analyses to tap into complex cognitive functions.