The Geometry of Abstraction in the Hippocampus and Prefrontal Cortex
| Autor: | C. Daniel Salzman, Mattia Rigotti, Silvia Bernardi, Marcus K. Benna, Jérôme Munuera, Stefano Fusi |
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| Rok vydání: | 2020 |
| Předmět: |
Male
Generalization Models Neurological Prefrontal Cortex Linear classifier Geometry Biology Hippocampus Article General Biochemistry Genetics and Molecular Biology Task (project management) 03 medical and health sciences 0302 clinical medicine Task Performance and Analysis Animals Learning Reinforcement learning Computer Simulation Prefrontal cortex 030304 developmental biology Abstraction (linguistics) Neurons Brain Mapping 0303 health sciences Behavior Animal Quantitative Biology::Neurons and Cognition Artificial neural network Macaca mulatta Neural Networks Computer Reinforcement Psychology 030217 neurology & neurosurgery Curse of dimensionality |
| Zdroj: | Cell |
| ISSN: | 0092-8674 |
| Popis: | The curse of dimensionality plagues models of reinforcement learning and decision making. The process of abstraction solves this by constructing variables describing features shared by different instances, reducing dimensionality and enabling generalization in novel situations. Here, we characterized neural representations in monkeys performing a task described by different hidden and explicit variables. Abstraction was defined operationally using the generalization performance of neural decoders across task conditions not used for training, which requires a particular geometry of neural representations. Neural ensembles in prefrontal cortex, hippocampus, and simulated neural networks simultaneously represented multiple variables in a geometry reflecting abstraction but that still allowed a linear classifier to decode a large number of other variables (high shattering dimensionality). Furthermore, this geometry changed in relation to task events and performance. These findings elucidate how the brain and artificial systems represent variables in an abstract format while preserving the advantages conferred by high shattering dimensionality. |
| Databáze: | OpenAIRE |
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