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利用解剖学和功能连接性估计快速神经输入

Estimating Fast Neural Input Using Anatomical and Functional Connectivity.

作者信息

Eriksson David

机构信息

Center for Neuroscience, Albert Ludwig University of FreiburgFreiburg, Germany; BrainLinks-BrainTools, Albert Ludwig University of FreiburgFreiburg, Germany.

出版信息

Front Neural Circuits. 2016 Dec 20;10:99. doi: 10.3389/fncir.2016.00099. eCollection 2016.

Abstract

In the last 20 years there has been an increased interest in estimating signals that are sent between neurons and brain areas. During this time many new methods have appeared for measuring those signals. Here we review a wide range of methods for which connected neurons can be identified anatomically, by tracing axons that run between the cells, or functionally, by detecting if the activity of two neurons are correlated with a short lag. The signals that are sent between the neurons are represented by the activity in the neurons that are connected to the target population or by the activity at the corresponding synapses. The different methods not only differ in the accuracy of the signal measurement but they also differ in the type of signal being measured. For example, unselective recording of all neurons in the source population encompasses more indirect pathways to the target population than if one selectively record from the neurons that project to the target population. Infact, this degree of selectivity is similar to that of optogenetic perturbations; one can perturb selectively or unselectively. Thus it becomes possible to match a given signal measurement method with a signal perturbation method, something that allows for an exact input control to any neuronal population.

摘要

在过去20年里,人们对估计神经元与脑区之间传递的信号越来越感兴趣。在此期间,出现了许多测量这些信号的新方法。在这里,我们回顾了一系列广泛的方法,通过追踪细胞间运行的轴突,可以从解剖学上识别相连的神经元;或者通过检测两个神经元的活动是否在短延迟内相关,从功能上识别相连的神经元。神经元之间传递的信号由与目标群体相连的神经元的活动或相应突触处的活动来表示。不同的方法不仅在信号测量的准确性上有所不同,而且在所测量的信号类型上也有所不同。例如,对源群体中所有神经元进行非选择性记录,与从投射到目标群体的神经元中进行选择性记录相比,包含了更多通向目标群体的间接途径。事实上,这种选择性程度与光遗传学扰动的选择性程度相似;人们可以进行选择性或非选择性扰动。因此,将给定的信号测量方法与信号扰动方法相匹配成为可能,这使得能够对任何神经元群体进行精确的输入控制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e279/5167717/daa29f1fcb8c/fncir-10-00099-g0001.jpg

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