Education

Case Studies in Neural Data Analysis

MATLAB (textbook)	Amazon
Python (online)	Github

As neural data becomes increasingly complex, neuroscientists require skills in computer programming, statistics, and data analysis. This website and book teach practical neural data analysis techniques by presenting example datasets and developing techniques and tools for analyzing them. Each section begins with a specific example of neural data, which motivates mathematical and statistical analysis methods that are then applied to the data. This practical, hands-on approach is unique among data analysis textbooks and guides, and equips the reader with the tools necessary for real-world neural data analysis.

Each resource begins with an introduction to programming, in either Python or MATLAB. The material covers neural field data and spike train data, spectral analysis, generalized linear models, coherence, and cross-frequency coupling. We present material as stand-alone case studies for targeted investigation. The material includes some mathematical discussion, but does not focus on mathematical or statistical theory, emphasizing the practical instead. References are included for readers who want to explore theoretical topics more deeply. The data and accompanying code are freely available in Python and MATLAB. The material is suitable for an upper-level undergraduate or introductory graduate course.

Educational Material

Mathematical Neuroscience	Graduate course at BU
Spike-field coherence	Lecture at MIT
Network Analysis in Sleep	Lecture at Computational Approaches to Signal Processing for Sleep
Network Analysis in Epilepsy	Lecture at the American Epilepsy Society Annual Meeting
Spectral Analysis for Neural Data	Lecture at the Society for Neuroscience Annual Meeting