The brain and its time: intrinsic neural timescales are key for input processing

被引:82
作者
Golesorkhi, Mehrshad [1 ,2 ,3 ]
Gomez-Pilar, Javier [4 ,5 ]
Zilio, Federico [6 ]
Berberian, Nareg [2 ,3 ]
Wolff, Annemarie [2 ,3 ]
Yagoub, Mustapha C. E. [1 ]
Northoff, Georg [2 ,3 ,7 ,8 ]
机构
[1] Univ Ottawa, Sch Elect Engn & Comp Sci, Ottawa, ON, Canada
[2] Royal Ottawa Mental Hlth Ctr, Inst Mental Hlth, Mind Brain Imaging & Neuroeth Res Unit, Ottawa, ON, Canada
[3] Univ Ottawa, Ottawa, ON, Canada
[4] Univ Valladolid, Biomed Engn Grp, Valladolid, Spain
[5] Ctr Invest Biomed Red Bioingn Biomat & Nanomed CI, Madrid, Spain
[6] Univ Padua, Dept Philosophy Sociol Educ & Appl Psychol, Padua, Italy
[7] Hangzhou Normal Univ, Ctr Cognit & Brain Disorders, Hangzhou, Peoples R China
[8] Zhejiang Univ, Mental Hlth Ctr, Sch Med, Hangzhou, Zhejiang, Peoples R China
基金
欧盟地平线“2020”; 加拿大自然科学与工程研究理事会;
关键词
FUNCTIONAL CONNECTIVITY; DEFAULT MODE; HIERARCHY; SELF; CONSCIOUSNESS; DYNAMICS; INFORMATION; CORTEX; MEMORY; COMMUNICATION;
D O I
10.1038/s42003-021-02483-6
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Golesorkhi et al. discuss recent literature on intrinsic neural timescales, their potential role in input processing including computational mechanism, and how they relate to mental features, psychiatric disorders and artificial intelligence. We process and integrate multiple timescales into one meaningful whole. Recent evidence suggests that the brain displays a complex multiscale temporal organization. Different regions exhibit different timescales as described by the concept of intrinsic neural timescales (INT); however, their function and neural mechanisms remains unclear. We review recent literature on INT and propose that they are key for input processing. Specifically, they are shared across different species, i.e., input sharing. This suggests a role of INT in encoding inputs through matching the inputs' stochastics with the ongoing temporal statistics of the brain's neural activity, i.e., input encoding. Following simulation and empirical data, we point out input integration versus segregation and input sampling as key temporal mechanisms of input processing. This deeply grounds the brain within its environmental and evolutionary context. It carries major implications in understanding mental features and psychiatric disorders, as well as going beyond the brain in integrating timescales into artificial intelligence.
引用
收藏
页数:16
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