Learning to predict future locations with internally generated theta sequences.
Representing past, present and future locations is key for spatial navigation. Indeed, within each cycle of the theta oscillation, the population of hippocampal place cells appears to represent trajectories starting behind the current position of the animal and sweeping ahead of it. In particular, w...
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| Principais autores: | , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Public Library of Science (PLoS)
2023-05-01
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| coleção: | PLoS Computational Biology |
| Acesso em linha: | https://doi.org/10.1371/journal.pcbi.1011101 |
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