Department of Brain & Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Department of Brain & Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Neuron. 2017 Mar 8;93(5):1003-1014. doi: 10.1016/j.neuron.2017.02.019.
The advent of powerful perturbation tools, such as optogenetics, has created new frontiers for probing causal dependencies in neural and behavioral states. These approaches have significantly enhanced the ability to characterize the contribution of different cells and circuits to neural function in health and disease. They have shifted the emphasis of research toward causal interrogations and increased the demand for more precise and powerful tools to control and manipulate neural activity. Here, we clarify the conditions under which measurements and perturbations support causal inferences. We note that the brain functions at multiple scales and that causal dependencies may be best inferred with perturbation tools that interface with the system at the appropriate scale. Finally, we develop a geometric framework to facilitate the interpretation of causal experiments when brain perturbations do or do not respect the intrinsic patterns of brain activity. We describe the challenges and opportunities of applying perturbations in the presence of dynamics, and we close with a general perspective on navigating the activity space of neurons in the search for neural codes.
强大的扰动工具(如光遗传学)的出现为探测神经和行为状态中的因果关系开辟了新的领域。这些方法极大地增强了我们描述不同细胞和回路对健康和疾病中神经功能的贡献的能力。它们将研究的重点转移到因果关系的探究上,并增加了对更精确和强大的工具的需求,以控制和操纵神经活动。在这里,我们澄清了测量和扰动支持因果推断的条件。我们注意到,大脑在多个尺度上运作,并且因果关系可能最好通过与系统在适当尺度上接口的扰动工具来推断。最后,我们开发了一个几何框架,以促进在大脑扰动遵守或不遵守大脑活动固有模式时解释因果实验。我们描述了在存在动力学的情况下应用扰动的挑战和机遇,并以在寻找神经编码时在神经元的活动空间中导航的一般观点结束。