Predictive learning as a network mechanism for extracting low-dimensional latent space representations
Neural networks trained using predictive models generate representations that recover the underlying low-dimensional latent structure in the data. Here, the authors demonstrate that a network trained on a spatial navigation task generates place-related neural activations similar to those observed in...
Guardado en:
Autores principales: | Stefano Recanatesi, Matthew Farrell, Guillaume Lajoie, Sophie Deneve, Mattia Rigotti, Eric Shea-Brown |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/411ac526db244399bdd114924abfd060 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Discovering Latent Representations of Relations for Interacting Systems
por: Dohae Lee, et al.
Publicado: (2021) -
Volumetric Representation and Sphere Packing of Indoor Space for Three-Dimensional Room Segmentation
por: Fan Yang, et al.
Publicado: (2021) -
Curves in low dimensional projective spaces with the lowest ranks
por: Ballico,Edoardo
Publicado: (2020) -
The place-cell representation of volumetric space in rats
por: Roddy M. Grieves, et al.
Publicado: (2020) -
DG–CA3 circuitry mediates hippocampal representations of latent information
por: Alexandra T. Keinath, et al.
Publicado: (2020)