Bayesian Coherence Analysis for Microcircuit Structure Learning.

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2023-01-01 DOI:10.1007/s12021-022-09608-0
Rong Chen
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Abstract

Functional microcircuits model the coordinated activity of neurons and play an important role in physiological computation and behaviors. Most existing methods to learn microcircuit structures are correlation-based and often generate dense microcircuits that cannot distinguish between direct and indirect association. We treat microcircuit structure learning as a Markov blanket discovery problem and propose Bayesian Coherence Analysis (BCA) which utilizes a Bayesian network architecture called Bayesian network with inverse-tree structure to efficiently and effectively detect Markov blankets for high-dimensional neural activity data. BCA achieved balanced sensitivity and specificity on simulated data. For the real-world anterior lateral motor cortex study, BCA identified microcircuit subtypes that predicted trial types with an accuracy of 0.92. BCA is a powerful method for microcircuit structure learning.

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微电路结构学习的贝叶斯相干分析。
功能微电路模拟神经元的协调活动,在生理计算和行为中起着重要作用。大多数现有的学习微电路结构的方法都是基于相关性的,并且经常产生不能区分直接和间接关联的密集微电路。我们将微电路结构学习视为一个马尔可夫毯子发现问题,并提出了贝叶斯相干分析(BCA),该分析利用贝叶斯网络结构(称为反树结构贝叶斯网络)高效有效地检测高维神经活动数据的马尔可夫毯子。BCA对模拟数据的敏感性和特异性达到平衡。对于真实世界的前外侧运动皮层研究,BCA识别出预测试验类型的微电路亚型,准确率为0.92。BCA是一种有效的微电路结构学习方法。
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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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