Statistical methods for dissecting interactions between brain areas

被引:32
作者
Semedo, Joao D. [1 ]
Gokcen, Evren [1 ]
Machens, Christian K. [2 ]
Kohn, Adam [3 ,4 ,5 ]
Yu, Byron M. [1 ,6 ]
机构
[1] Carnegie Mellon Univ, Dept Elect & Comp Engn, Pittsburgh, PA 15213 USA
[2] Champalimaud Ctr Unknown, Champalimaud Res, Lisbon, Portugal
[3] Albert Einstein Coll Med, Dominick Purpura Dept Neurosci, Bronx, NY 10467 USA
[4] Albert Einstein Coll Med, Dept Ophthalmol & Visual Sci, Bronx, NY 10467 USA
[5] Albert Einstein Coll Med, Dept Syst & Computat Biol, Bronx, NY 10467 USA
[6] Carnegie Mellon Univ, Dept Biomed Engn, Pittsburgh, PA 15213 USA
关键词
ATTENTION; SPECIFICITY; SELECTION; GAMMA; V1;
D O I
10.1016/j.conb.2020.09.009
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
The brain is composed of many functionally distinct areas. This organization supports distributed processing, and requires the coordination of signals across areas. Our understanding of how populations of neurons in different areas interact with each other is still in its infancy. As the availability of recordings from large populations of neurons across multiple brain areas increases, so does the need for statistical methods that are well suited for dissecting and interrogating these recordings. Here we review multivariate statistical methods that have been, or could be, applied to this class of recordings. By leveraging population responses, these methods can provide a rich description of inter-areal interactions. At the same time, these methods can introduce interpretational challenges. We thus conclude by discussing how to interpret the outputs of these methods to further our understanding of inter-areal interactions.
引用
收藏
页码:59 / 67
页数:9
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