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...

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Autores principales:	Stefano Recanatesi, Matthew Farrell, Guillaume Lajoie, Sophie Deneve, Mattia Rigotti, Eric Shea-Brown
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/411ac526db244399bdd114924abfd060
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spelling	oai:doaj.org-article:411ac526db244399bdd114924abfd0602021-12-02T13:32:57ZPredictive learning as a network mechanism for extracting low-dimensional latent space representations10.1038/s41467-021-21696-12041-1723https://doaj.org/article/411ac526db244399bdd114924abfd0602021-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-21696-1https://doaj.org/toc/2041-1723Neural 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 the hippocampus and show that these are related to the latent structure.Stefano RecanatesiMatthew FarrellGuillaume LajoieSophie DeneveMattia RigottiEric Shea-BrownNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Stefano Recanatesi Matthew Farrell Guillaume Lajoie Sophie Deneve Mattia Rigotti Eric Shea-Brown Predictive learning as a network mechanism for extracting low-dimensional latent space representations
description	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 the hippocampus and show that these are related to the latent structure.
format	article
author	Stefano Recanatesi Matthew Farrell Guillaume Lajoie Sophie Deneve Mattia Rigotti Eric Shea-Brown
author_facet	Stefano Recanatesi Matthew Farrell Guillaume Lajoie Sophie Deneve Mattia Rigotti Eric Shea-Brown
author_sort	Stefano Recanatesi
title	Predictive learning as a network mechanism for extracting low-dimensional latent space representations
title_short	Predictive learning as a network mechanism for extracting low-dimensional latent space representations
title_full	Predictive learning as a network mechanism for extracting low-dimensional latent space representations
title_fullStr	Predictive learning as a network mechanism for extracting low-dimensional latent space representations
title_full_unstemmed	Predictive learning as a network mechanism for extracting low-dimensional latent space representations
title_sort	predictive learning as a network mechanism for extracting low-dimensional latent space representations
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/411ac526db244399bdd114924abfd060
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Predictive learning as a network mechanism for extracting low-dimensional latent space representations

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