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：
10.1088/1741-2560/10/6/066012
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发表时间：
2013-12
影响因子：
4
通讯作者：
Yu BM
中科院分区：
文献类型：
--
作者：
Cowley BR;Kaufman MT;Butler ZS;Churchland MM;Ryu SI;Shenoy KV;Yu BM
分析和解释神经元种群的活性可能是具有挑战性的，尤其是随着神经元，实验试验的数量和实验条件的增加。一种方法是提取一组潜在变量，这些变量简洁地捕捉了整个神经种群中的突出共透明模式。一个关键问题是，充分描述人口活动所需的潜在变量的数量通常大于三，从而阻止了潜在空间的直接可视化。通过可视化潜在空间的少量2D投影或单独的每个潜在变量，很容易错过人口活动的显着特征。
为了解决此限制，我们开发了MATLAB图形用户界面（称为Datahigh），该界面允许用户快速，平稳地通过潜在空间的不同2D投影的连续体导航。我们还实施了一套其他可视化工具（包括将人口活动时间作为电影播放，并显示摘要统计信息，例如协方差椭圆和平均时间库），以及用于降低维度的可选工具。
为了证明Datahigh的效用和多功能性，我们用它来分析使用多电极阵列记录的单次试验峰值计数和单审时间的人口活动，以及使用单电极记录的试验平均种群活动。
Datahigh的开发是为了满足探索性神经数据分析中的可视化需求，这可以提供直觉，这对于建立科学的假设和人口活动模型至关重要。
Analyzing and interpreting the activity of a heterogeneous population of neurons can be challenging, especially as the number of neurons, experimental trials, and experimental conditions increases. One approach is to extract a set of latent variables that succinctly captures the prominent co-fluctuation patterns across the neural population. A key problem is that the number of latent variables needed to adequately describe the population activity is often greater than three, thereby preventing direct visualization of the latent space. By visualizing a small number of 2-d projections of the latent space or each latent variable individually, it is easy to miss salient features of the population activity. To address this limitation, we developed a Matlab graphical user interface (called DataHigh) that allows the user to quickly and smoothly navigate through a continuum of different 2-d projections of the latent space. We also implemented a suite of additional visualization tools (including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses) and an optional tool for performing dimensionality reduction. To demonstrate the utility and versatility of DataHigh, we used it to analyze single-trial spike count and single-trial timecourse population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded using single electrodes. DataHigh was developed to fulfill a need for visualization in exploratory neural data analysis, which can provide intuition that is critical for building scientific hypotheses and models of population activity.
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影响因子：
25
作者：
Stevenson, Ian H.;Kording, Konrad P.
通讯作者：
Kording, Konrad P.
影响因子：
16.2
作者：
Broome, Bede M.;Jayaraman, Vivek;Laurent, Gilles
通讯作者：
Laurent, Gilles
DOI：
10.1109/embc.2012.6346993
发表时间：
2012
期刊：
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
影响因子：
--
作者：
Cowley BR;Kaufman MT;Churchland MM;Ryu SI;Shenoy KV;Yu BM
通讯作者：
Yu BM
DOI：
10.1111/j.1460-9568.2009.06954.x
发表时间：
2009-11
期刊：
The European journal of neuroscience
影响因子：
--
作者：
Bartho P;Curto C;Luczak A;Marguet SL;Harris KD
通讯作者：
Harris KD
影响因子：
3.6
作者：
Berk, Richard;Brown, Lawrence;Zhao, Linda
通讯作者：
Zhao, Linda