DataHigh: graphical user interface for visualizing and interacting with high-dimensional neural activity.
DataHigh: graphical user interface for visualizing and interacting with high-dimensional neural activity.
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DOI：
10.1109/embc.2012.6346993
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Yu BM
中科院分区：
文献类型：
--
作者：
Cowley BR;Kaufman MT;Churchland MM;Ryu SI;Shenoy KV;Yu BM
数十到数百个神经元的活动可以通过使用降维方法提取的较少数量的潜在变量来简洁地概括。这些潜在变量定义了一个降维空间，我们可以在其中研究群体活动如何随时间、不同试验和不同实验条件而变化。理想情况下，我们希望直接在降维空间中可视化人口活动，其最佳维度（根据数据确定）通常大于 3。但是，直接绘图只能提供 2D 或 3D 视图。为了解决这个限制，我们开发了一个 Matlab 图形用户界面 (GUI)，它允许用户快速浏览降维空间的不同 2D 投影的连续体。为了展示该 GUI 的实用性和多功能性，我们应用它来可视化在执行任务期间运动前皮层和运动皮质中记录的群体活动。示例包括使用多电极阵列记录的单次试验群体活动，以及使用单电极顺序记录的试验平均群体活动。由于任何单个 2D 投影都可能会给数据带来误导性的印象，因此能够看到大量 2D 投影对于探索性数据分析期间的直觉和假设构建至关重要。 GUI 包括一套附加的交互式工具，包括以电影形式播放人口活动时间进程并显示汇总统计数据，例如协方差椭圆和平均时间进程。使用此处开发的 GUI 等可视化工具与降维方法相结合，有可能进一步加深我们对神经群体活动的理解。
The activity of tens to hundreds of neurons can be succinctly summarized by a smaller number of latent variables extracted using dimensionality reduction methods. These latent variables define a reduced-dimensional space in which we can study how population activity varies over time, across trials, and across experimental conditions. Ideally, we would like to visualize the population activity directly in the reduced-dimensional space, whose optimal dimensionality (as determined from the data) is typically greater than 3. However, direct plotting can only provide a 2D or 3D view. To address this limitation, we developed a Matlab graphical user interface (GUI) that allows the user to quickly navigate through a continuum of different 2D projections of the reduced-dimensional space. To demonstrate the utility and versatility of this GUI, we applied it to visualize population activity recorded in premotor and motor cortices during reaching tasks. Examples include single-trial population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded sequentially using single electrodes. Because any single 2D projection may provide a misleading impression of the data, being able to see a large number of 2D projections is critical for intuition- and hypothesis-building during exploratory data analysis. The GUI includes a suite of additional interactive tools, including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses. The use of visualization tools like the GUI developed here, in tandem with dimensionality reduction methods, has the potential to further our understanding of neural population activity.