“Brainland” vs. “flatland”: How many dimensions do we need in brain dynamics?: Comment on the paper “The unreasonable effectiveness of small neural ensembles in high-dimensional brain” by Alexander N. Gorban et al.
Resumen: In their review article (this issue) [1], Gorban, Makarov and Tyukin develop a successful effort to show in biological, physical and mathematical problems the relevant question of how high-dimensional brain can organise reliable and fast learning in the high-dimensional world of data using reduction tools. In fact, this paper, and several recent studies, focuses on the crucial problem of how the brain manages the information it receives, how it is organized, and how mathematics can learn about this and use dimension related techniques in other fields. Moreover, the opposite problem is also relevant, that is, how we can recover high-dimensional information from low-dimensional ones, the relevant problem of embedding dimensions (the other side of reducing dimensions).

The human brain is a real open problem and a great challenge in human knowledge. The way the memory is codified is a fundamental problem in Neuroscience. As mentioned by the authors, the idea of blessing the dimensionality (and the opposite curse of dimensionality), are becoming more and more relevant in machine learning...

Idioma: Inglés
DOI: 10.1016/j.plrev.2019.02.010
Año: 2019
Publicado en: Physics of Life Reviews 29 (2019), 108-110
ISSN: 1571-0645

Factor impacto JCR: 14.789 (2019)
Categ. JCR: BIOPHYSICS rank: 1 / 71 = 0.014 (2019) - Q1 - T1
Categ. JCR: BIOLOGY rank: 1 / 93 = 0.011 (2019) - Q1 - T1

Factor impacto SCIMAGO: 2.854 - Agricultural and Biological Sciences (miscellaneous) (Q1) - Physics and Astronomy (miscellaneous) (Q1) - Artificial Intelligence (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA-FEDER/E24-17R
Financiación: info:eu-repo/grantAgreement/ES/MINECO-FEDER/MTM2015-64095-P
Financiación: info:eu-repo/grantAgreement/ES/MINECO-FEDER/PGC2018-096026-B-I00
Tipo y forma: (PostPrint)
Área (Departamento): Área Matemática Aplicada (Dpto. Matemática Aplicada)

Creative Commons Debe reconocer adecuadamente la autoría, proporcionar un enlace a la licencia e indicar si se han realizado cambios. Puede hacerlo de cualquier manera razonable, pero no de una manera que sugiera que tiene el apoyo del licenciador o lo recibe por el uso que hace. No puede utilizar el material para una finalidad comercial. Si remezcla, transforma o crea a partir del material, no puede difundir el material modificado.


Exportado de SIDERAL (2020-07-16-09:05:28)


Visitas y descargas

Este artículo se encuentra en las siguientes colecciones:
Artículos



 Registro creado el 2020-03-19, última modificación el 2020-07-16


Postprint:
 PDF
Valore este documento:

Rate this document:
1
2
3
 
(Sin ninguna reseña)