NEU 445 - Analysis of Neuroscience Data - SS17
Today’s technologies enable neuroscientists to gather data in quantities previously unimagined, and the BRAIN initiative will dramatically expand these capabilities. Many experimental researchers experience a bottleneck when it comes time to analyze their hard-won data. This course is intended to help neuroscience practitioners to meet the challenges posed by the analysis of large functional datasets, and to introduce quantitative students to the wealth of problems to be addressed in big data from neuroscience.
The course will address issues and methods in data analysis for the new high-throughput technologies in neuroscience, with emphasis on statistical issues, such as estimation accuracy and testing. The course will cover pre-processing and artifact removal, linear models for activity and generalized linear models for spike trains, spectral analysis, dimension reduction techniques and network inference. The data types considered will be multi-unit recordings, local field potentials, functional MRI, and optical imaging. We will also consider how to integrate neural data with detailed behavioral data.
The workshop will proceed by seminar, demonstration and practical lab data analysis exercises supervised by the instructor. Students will learn and work in MATLAB.
Time & Location: Tu & Thu, 2:40pm-4:00pm. 1300 Engineering Building.
Instructor: M. Reimers, 326 Giltner Hall.
Text: Pascal Wallisch et al, MATLAB for Neuroscientists, 2nd edition.