What does functional connectivity tell us about the behaviorally-functional connectivity of a multifunctional neural circuit?
What does functional connectivity tell us about the behaviorally-functional connectivity of a multifunctional neural circuit?
复制标题
关于多功能神经回路的行为功能连接，功能连接告诉我们什么？
DOI：
10.1162/isal_a_00534
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Candadai, Madhavun
中科院分区：
文献类型：
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作者：
Izquierdo, Eduardo J.;Candadai, Madhavun
对行为过程中神经元和大脑区域的时间序列记录进行统计分析，可以为行为的神经基础提供什么样的见解？随着越来越多的全脑成像数据变得可用，解决这一悬而未决的理论挑战的重要性变得越来越紧迫。我们提出了一个计算神经行为学的方法来开始，以解决这一挑战。我们进化动态递归神经网络，使其能够执行多个任务。然后，我们使用流行的网络神经科学工具分析神经活动，特别是使用Pearson相关性，互信息和传递熵的功能连接。我们将这些工具的结果与一系列信息损伤进行比较，以揭示它们与地面实况的近似程度。我们的初步分析揭示了从基于神经活动的电路功能的统计推断中获得的见解与机械干预所揭示的电路的实际功能之间的巨大差距。
What insights can statistical analysis of the time series recordings of neurons and brain regions during behavior give about the neural basis of behavior? With the increasing amount of whole-brain imaging data becoming available, the importance of addressing this unanswered theoretical challenge has become increasingly urgent. We propose a computational neuroethology approach to begin to address this challenge. We evolve dynamical recurrent neural networks to be capable of performing multiple tasks. We then analyze the neural activity using popular network neuroscience tools, specifically functional connectivity using Pearson’s correlation, mutual information, and transfer entropy. We compare the results from these tools against a series of informational lesions, as a way to reveal their degree of approximation to the ground-truth. Our initial analysis reveals an overwhelming large gap between the insights gained from statistical inference of the functionality of the circuits based on neural activity and the actual functionality of the circuits as revealed by mechanistic interventions.