DataHigh: Graphical user interface for visualizing and interacting with high-dimensional neural activity

• Published in: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2012-01, Vol.2012, p.4607-4610
• Main Authors: Cowley, B. R., Kaufman, M. T., Churchland, M. M., Ryu, S. I., Shenoy, K. V., Yu, B. M.
• Format: Article
• Language: English
• Subjects: Data visualization; Graphical user interfaces; Neurons; Sociology; Statistics; Trajectory; Vectors
• ISSN: 1094-687X; 1558-4615; 2694-0604
• DOI: 10.1109/EMBC.2012.6346993

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.