Neural Data Science

Erik Lee Nylen received his PhD from the Center for Neural Science at New York University, and his BSE and MS in Biomedical Engineering at the University of Iowa. He did a fellowship at Insight Data Science, and has taught at the Neural Data Science summer course at Cold Spring Harbor Laboratory. He is a patented inventor and has performed with numerous musical groups. He is currently a data scientist in New York, where he also is Executive Co-Director of The Stand, the New York City Dance Marathon. Pascal Wallisch serves as a professor in the Department of Psychology at New York University where he currently teaches statistics, programming and the use of mathematical tools in neuroscience and psychology. He received his PhD in Psychology from the University of Chicago and worked as a postdoctoral fellow at the Center for Neural Science at New York University. He has a long-term commitment and is dedicated to educational excellence, which was recognized by the “Wayne C. Booth Graduate Student Prize for Excellence in teaching” at the University of Chicago and the “Golden Dozen Award” at New York University. He co-founded and co-organizes the “Neural Data Science” summer course at Cold Spring Harbor Laboratory and co-authored “Matlab for Neuroscientists”.

"Making sense of data is emerging as the limiting factor of progress in neuroscience. This book is the accessible way to learn how to do that." --Konrad Koerding, Professor, Northwestern University"This is a fun, hands-on introduction to the important emerging field of neural data science. It's at the intersection of programming, data analysis, and neuroscience -- perfect for aspiring researchers looking to learn these three in parallel. This book will help inspire a new generation to join us in finding out how the brain works with modern computational tools." --Nikolaus Kriegeskorte, Programme leader, Cognition and Brain Sciences Unit, University of Cambridge

• Part I: Foundations1. Philosophy2. From 0 to 0.01Part II: Neural Data Analysis3. Wrangling Spike Trains4. Correlating Spike Trains5. Analog Signals6. Biophysical ModelingPart III: Going Beyond the Data7. Regression8. Dimensionality Reduction9. Classification and Clustering10. Web Scraping