Topological insights into the neural basis of flexible behavior
Topological insights into the neural basis of flexible behavior
复制标题
对灵活行为神经基础的拓扑见解
DOI：
10.1101/2021.09.24.461717
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发表时间：
2021
影响因子：
11.1
通讯作者：
Marlene R. Cohen
中科院分区：
文献类型：
--
作者：
Tevin C. Rouse;Amy M. Ni;Chengcheng Huang;Marlene R. Cohen
人们普遍认为，神经计算、生物机制和行为之间存在着不可分割的联系，但同时将三者联系起来是一个挑战。在这里，我们表明，拓扑数据分析（TDA）提供了一个重要的桥梁，这些方法来研究大脑如何调解行为。我们证明，认知过程改变了视觉神经元群体的共享活动的拓扑描述。这些拓扑结构的变化约束和区分竞争机制模型，连接到主体的视觉变化检测任务的性能，并通过网络控制理论的链接，揭示了提高敏感性微妙的视觉刺激变化和增加的机会，受试者将偏离任务之间的权衡。这些联系为使用TDA揭示认知影响健康和疾病行为的生物和计算机制提供了蓝图。随着系统、计算和认知神经科学领域努力在计算、生物学和行为之间建立联系，越来越需要一个分析框架来连接分析层次。我们证明，拓扑数据分析（TDA）的神经元群体的共享活动提供了这种联系。TDA使我们能够区分相互竞争的机械模型，并回答认知神经科学中长期存在的问题，例如为什么在视觉敏感性和保持任务之间存在权衡。这些结果和分析框架可以应用于神经科学和其他领域的许多系统。
It is widely accepted that there is an inextricable link between neural computations, biological mechanisms, and behavior, but it is challenging to simultaneously relate all three. Here, we show that topological data analysis (TDA) provides an important bridge between these approaches to studying how brains mediate behavior. We demonstrate that cognitive processes change the topological description of the shared activity of populations of visual neurons. These topological changes constrain and distinguish between competing mechanistic models, are connected to subjects’ performance on a visual change detection task, and, via a link with network control theory, reveal a tradeoff between improving sensitivity to subtle visual stimulus changes and increasing the chance that the subject will stray off task. These connections provide a blueprint for using TDA to uncover the biological and computational mechanisms by which cognition affects behavior in health and disease. Significance Statement As the fields of systems, computational, and cognitive neuroscience strive to establish links between computations, biology, and behavior, there is an increasing need for an analysis framework to bridge levels of analysis. We demonstrate that topological data analysis (TDA) of the shared activity of populations of neurons provides that link. TDA allows us to distinguish between competing mechanistic models and to answer longstanding questions in cognitive neuroscience, such as why there is a tradeoff between visual sensitivity and staying on task. These results and analysis framework have applications to many systems within neuroscience and beyond.
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DOI：
--
发表时间：
2015-07
期刊：
J. Mach. Learn. Res.
影响因子：
--
作者：
Henry Adams;T. Emerson;M. Kirby;R. Neville;C. Peterson;Patrick D. Shipman;Sofya Chepushtanova;Eric M. Hanson;Francis C. Motta;Lori Ziegelmeier
通讯作者：
Henry Adams;T. Emerson;M. Kirby;R. Neville;C. Peterson;Patrick D. Shipman;Sofya Chepushtanova;Eric M. Hanson;Francis C. Motta;Lori Ziegelmeier
影响因子：
16.2
作者：
Luo TZ;Maunsell JHR
通讯作者：
Maunsell JHR
影响因子：
1.8
作者：
G. Singh;F. Mémoli;T. Ishkhanov;G. Sapiro;G. Carlsson;D. Ringach
通讯作者：
G. Singh;F. Mémoli;T. Ishkhanov;G. Sapiro;G. Carlsson;D. Ringach
DOI：
10.1037/0096-1523.14.2.188
发表时间：
1988-05-01
影响因子：
2.1
作者：
DOWNING, CJ
通讯作者：
DOWNING, CJ
影响因子：
13.9
作者：
Ruff DA;Ni AM;Cohen MR
通讯作者：
Cohen MR