Mentors: Anne Draelos, Ph.D., Liz O’Gorman, John Pearson, Ph.D (Department of Biostatistics and Bioinformatics)
The intersection of neuroscience and advanced computational software opens the door for increasingly complex and integrative experiments. Improv is a software platform that allows users to have flexibility in controlling experiments and provides live data analysis and visualizations. The original visualization of improv works sufficiently, however it lacks accessibility and effortless control from the user. I am developing an updated visualization that addresses these issues and integrates seamlessly with improv. Jupyter Notebook is the base for the visualization, which can easily be accessed through a web browser and gives users control over which plots and visuals are displayed. In order to create the various components of the visualization, data must be loaded in from improv. This process will be facilitated by ZMQ, a program that can communicate between Jupyter Notebook and improv’s data store and data acquirer. After efficiently loading the data, I am developing live updating plots of neural data and images. These plots are built through Python packages such as Matplotlib and jupyterplot, and they will be timed for their efficiency. Key features of the updated visualization include easy accessibility and broader control for experimentalists, thus providing insightful and more intuitive information of ongoing experiments for the user.
