Whittington Miles A, Traub Roger D, Adams Natalie E
Hull York Medical School, University of York, Heslington, UK.
Department of Physical Sciences, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA.
Brain Neurosci Adv. 2019 Mar 1;2:2398212818794827. doi: 10.1177/2398212818794827. eCollection 2018 Jan-Dec.
Neuronal oscillations represent the most obvious feature of electrical activity in the brain. They are linked in general with global brain state (awake, asleep, etc.) and specifically with organisation of neuronal outputs during sensory perception and cognitive processing. Oscillations can be generated by individual neurons on the basis of interaction between inputs and intrinsic conductances but are far more commonly seen at the local network level in populations of interconnected neurons with diverse arrays of functional properties. It is at this level that the brain's rich and diverse library of oscillatory time constants serve to temporally organise large-scale neural activity patterns. The discipline is relatively mature at the microscopic (cell, local network) level - although novel discoveries are still commonplace - but requires a far greater understanding of mesoscopic and macroscopic brain dynamics than we currently hold. Without this, extrapolation from the temporal properties of neurons and their communication strategies up to whole brain function will remain largely theoretical. However, recent advances in large-scale neuronal population recordings and more direct, higher fidelity, non-invasive measurement of whole brain function suggest much progress is just around the corner.
神经元振荡是大脑电活动最显著的特征。一般来说,它们与大脑整体状态(清醒、睡眠等)相关,尤其与感觉感知和认知处理过程中神经元输出的组织有关。振荡可以由单个神经元基于输入与内在电导之间的相互作用产生,但在具有不同功能特性阵列的相互连接神经元群体的局部网络层面更为常见。正是在这个层面,大脑丰富多样的振荡时间常数库有助于在时间上组织大规模神经活动模式。该学科在微观(细胞、局部网络)层面相对成熟——尽管新发现仍然屡见不鲜——但需要比我们目前所掌握的对介观和宏观大脑动力学有更深入的理解。没有这一点,从神经元的时间特性及其通信策略推断到全脑功能在很大程度上仍将停留在理论层面。然而,大规模神经元群体记录以及对全脑功能更直接、更高保真度的非侵入性测量方面的最新进展表明,重大进展即将到来。