Neural Data Science: A Primer with Matlab(r) and Python(tm)

Neural Data Science: A Primer with Matlab(r) and Python(tm)
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Erik Lee Nylen
744 g
235x193x22 mm

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”.

A Primer with MATLAB® and PythonT present important information on the emergence of the use of Python, a more general purpose option to MATLAB, the preferred computation language for scientific computing and analysis in neuroscience.

This book addresses the snake in the room by providing a beginner's introduction to the principles of computation and data analysis in neuroscience, using both Python and MATLAB, giving readers the ability to transcend platform tribalism and enable coding versatility.

Part I: Foundations 1. Philosophy 2. From 0 to 0.01

Part II: Neural Data Analysis 3. Wrangling Spike Trains 4. Correlating Spike Trains 5. Analog Signals 6. Biophysical Modeling

Part III: Going Beyond the Data 7. Regression 8. Dimensionality Reduction 9. Classification and Clustering 10. Web Scraping

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