Neural Data Science

About the Authors

Erik Nylen

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.

Affiliations and Expertise

New York University, New York, NY, USA

Pascal Wallisch

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”.

Affiliations and Expertise

New York University, New York, NY, USA

About this Book

Description

A Primer with MATLAB® and Python™ 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.

Key Features

• Includes discussions of both MATLAB and Python in parallel

• Introduces the canonical data analysis cascade, standardizing the data analysis flow

• Presents tactics that strategically, tactically, and algorithmically help improve the organization of code

Readership

Students, researchers and instructors in Systems, Cognitive and Behavioral Neuroscience, and Cognitive Psychology