神经群体的消息相关降维
Message-Relevant Dimension Reduction of Neural Populations
AI总结：
本文提出迭代回归这一消息相关线性降维方法，并定义M-转发概念，将M-信息流框架用于真实神经数据，揭示了小鼠感觉检测中两个神经群体间的低维通信结构。
摘要：
量化神经群体之间的相关交互是高维神经记录分析中的一个突出问题。然而，现有的降维方法常常在缺乏正式框架的情况下讨论通信，而为了解决这一缺口所提出的框架在数据分析中又不切实际。本研究将M-信息流的正式框架与真实神经数据的实际分析连接起来。为此，我们提出了迭代回归，这是一种依赖消息的线性降维技术，它迭代地寻找一个标准正交基，使得每个基向量最大化投影数据与消息之间的相关性。然后，我们定义“M-转发”，以正式刻画消息从一个神经群体被转发到另一个神经群体的概念。我们将该方法应用于我们从小鼠基于胡须的感觉检测简化模型中两个神经群体收集的记录，并表明我们推断出的低维M-转发结构支持了两个原始高维群体之间存在类似结构的生物学证据。
英文摘要：
Quantifying relevant interactions between neural populations is a prominent question in the analysis of high-dimensional neural recordings. However, existing dimension reduction methods often discuss communication in the absence of a formal framework, while frameworks proposed to address this gap are impractical in data analysis. This work bridges the formal framework of M-Information Flow with practical analysis of real neural data. To this end, we propose Iterative Regression, a message-dependent linear dimension reduction technique that iteratively finds an orthonormal basis such that each basis vector maximizes correlation between the projected data and the message. We then define 'M-forwarding' to formally capture the notion of a message being forwarded from one neural population to another. We apply our methodology to recordings we collected from two neural populations in a simplified model of whisker-based sensory detection in mice, and show that the low-dimensional M-forwarding structure we infer supports biological evidence of a similar structure between the two original, high-dimensional populations.
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