Balasubramaniam Ramesh, Torre Kjerstin
McMaster University, Hamilton, Ontario, Canada.
Crit Rev Biomed Eng. 2012;40(6):459-70. doi: 10.1615/critrevbiomedeng.2013006841.
This article serves as an introduction to the themed special issue on "Complex Systems in Neurobiology." The study of complexity in neurobiology has been sensitive to the stochastic processes that dominate the micro-level architecture of neurobiological systems and the deterministic processes that govern the macroscopic behavior of these systems. A large body of research has traversed these scales of interest, seeking to determine how noise at one spatial or temporal scale influences the activity of the system at another scale. In introducing this special issue, we pay special attention to the history of inquiry in complex systems and why scientists have tended to favor linear, causally driven, reductionist approaches in Neurobiology. We follow this with an elaboration of how an alternative approach might be formulated. To illustrate our position on how the sciences of complexity and the study of noise can inform neurobiology, we use three systematic examples from the study of human motor control and learning: 1) phase transitions in bimanual coordination; 2) balance, intermittency, and discontinuous control; and 3) sensorimotor synchronization and timing. Using these examples and showing that noise is adaptively utilized by the nervous system, we make the case for the studying complexity with a perspective of understanding the macroscopic stability in biological systems by focusing on component processes at extended spatial and temporal scales. This special issue continues this theme with contributions in topics as diverse as neural network models, physical biology, motor learning, and statistical physics.
本文是关于“神经生物学中的复杂系统”这一主题特刊的引言。神经生物学中对复杂性的研究，一直关注着主导神经生物学系统微观结构的随机过程以及支配这些系统宏观行为的确定性过程。大量研究跨越了这些感兴趣的尺度，试图确定一个空间或时间尺度上的噪声如何影响另一尺度上系统的活动。在介绍本期特刊时，我们特别关注复杂系统的研究历史，以及为何科学家在神经生物学中倾向于青睐线性的、因果驱动的、还原论的方法。接下来，我们将详细阐述如何构建一种替代方法。为了说明我们对于复杂性科学和噪声研究如何为神经生物学提供信息的观点，我们使用了人类运动控制与学习研究中的三个系统实例：1）双手协调中的相变；2）平衡、间歇性和间断控制；3）感觉运动同步与定时。通过这些实例并表明噪声被神经系统适应性地利用，我们主张从通过关注扩展的空间和时间尺度上的组成过程来理解生物系统宏观稳定性的角度研究复杂性。本期特刊延续了这一主题，涵盖了神经网络模型、物理生物学、运动学习和统计物理学等不同主题的论文。
