Stowell H
Int J Neurosci. 1987 May;34(1-2):117-22. doi: 10.3109/00207458708985945.
The trajectories of data and modelling from formerly divergent research efforts now seem to be converging to an unexpected region of the phase space of neuroscience. Computational network theory and simulation assume that temporal rhythm may be a significant parameter for the successful organization of nonlinear analog computation effected by hierarchical sets of biological neurons and of nonbiological circuitry alike. For neurobiology, the apparently chaotic rhythms of cerebral compound field potentials--the electroencephalogram (EEG) and slow waves of event related brain potentials (ERBP)--have long been a phenomenological embarrassment, of only marginal clinical utility. But recent data from molecular biophysics, nonlinear dynamics, artificial intelligence, and scalp-conducted human electrocorticography suggest a possible functional role in the serial gating of neural network computations for the familiar theta-alpha-beta rhythms of the EEG clinic.
以往不同研究工作中的数据轨迹和建模，如今似乎正汇聚到神经科学相空间中一个意想不到的区域。计算网络理论与模拟假定，时间节律可能是一个重要参数，对于由生物神经元层级集以及非生物电路实现的非线性模拟计算的成功组织而言。对于神经生物学来说，大脑复合场电位（脑电图（EEG）和事件相关脑电位（ERBP）的慢波）明显的混沌节律，长期以来一直是一种现象学上的尴尬，临床效用有限。但来自分子生物物理学、非线性动力学、人工智能以及头皮传导式人类脑电描记法的最新数据表明，脑电图临床中常见的θ-阿尔法-贝塔节律在神经网络计算的序列门控中可能具有功能性作用。
