000088217 001__ 88217 000088217 005__ 20200716101449.0 000088217 0247_ $$2doi$$a10.1016/j.plrev.2019.02.010 000088217 0248_ $$2sideral$$a111280 000088217 037__ $$aART-2019-111280 000088217 041__ $$aeng 000088217 100__ $$0(orcid)0000-0002-8089-343X$$aBarrio, R.$$uUniversidad de Zaragoza 000088217 245__ $$a“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. 000088217 260__ $$c2019 000088217 5060_ $$aAccess copy available to the general public$$fUnrestricted 000088217 5203_ $$aIn 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... 000088217 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FEDER/E24-17R$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/MTM2015-64095-P$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/PGC2018-096026-B-I00 000088217 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000088217 590__ $$a14.789$$b2019 000088217 591__ $$aBIOPHYSICS$$b1 / 71 = 0.014$$c2019$$dQ1$$eT1 000088217 591__ $$aBIOLOGY$$b1 / 93 = 0.011$$c2019$$dQ1$$eT1 000088217 592__ $$a2.854$$b2019 000088217 593__ $$aAgricultural and Biological Sciences (miscellaneous)$$c2019$$dQ1 000088217 593__ $$aPhysics and Astronomy (miscellaneous)$$c2019$$dQ1 000088217 593__ $$aArtificial Intelligence$$c2019$$dQ1 000088217 655_4 $$ainfo:eu-repo/semantics/other$$vinfo:eu-repo/semantics/acceptedVersion 000088217 7102_ $$12005$$2595$$aUniversidad de Zaragoza$$bDpto. Matemática Aplicada$$cÁrea Matemática Aplicada 000088217 773__ $$g29 (2019), 108-110$$pPhysics of Life Reviews$$tPhysics of Life Reviews$$x1571-0645 000088217 8564_ $$s199589$$uhttps://zaguan.unizar.es/record/88217/files/texto_completo.pdf$$yPostprint 000088217 8564_ $$s418005$$uhttps://zaguan.unizar.es/record/88217/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000088217 909CO $$ooai:zaguan.unizar.es:88217$$particulos$$pdriver 000088217 951__ $$a2020-07-16-09:05:28 000088217 980__ $$aARTICLE