Neural Data Science - 1st Edition - ISBN: 9780128040430, 9780128040980

Neural Data Science

1st Edition

A Primer with MATLAB® and Python™

Authors: Erik Nylen Pascal Wallisch
eBook ISBN: 9780128040980
Paperback ISBN: 9780128040430
Imprint: Academic Press
Published Date: 21st March 2017
Page Count: 368
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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

Table of Contents

Part I: Foundations

Chapter 1. Philosophy

  • Abstract
  • What Is Data Science?
  • What Is Neural Data Science?
  • How Is Neural Data Science Different From Computational Neuroscience?
  • Data as Seen by Data Scientists Versus Data Seen by Neural Data Scientists
  • What Is a Neural Data Scientist?
  • Why Do I Need to be Able to Write Computer Code?
  • What Is Neural Data?
  • Can We Just Add “Neuro” to the Front of Anything?
  • Why Python?
  • Why MATLAB?
  • Why Not C/C++/R/Julia/Haskill/Java/Javascript/OCaml/Perl/Pascal/Fortran/Ruby/Groovy/Scala/etc.?
  • What Is Industrial Data Science? How Is It Different From Engineering?

Chapter 2. From 0 to 0.01

  • Abstract
  • What Is the Goal of This Chapter?
  • How Do I Get Started Coding?
  • What’s the Command Line? What’s the Environment?
  • How Are Python and MATLAB Different?
  • How Do I Display Something on the Screen?
  • How Do I Do Arithmetic in Python or MATLAB?
  • How Do I Input Exponents in Python and MATLAB?
  • What Is the Role of Blank Space in Writing Code, If Any?
  • What Is the Order of Operations in Python and MATLAB?
  • What Are Functions?
  • What Are Python Packages? What Are MATLAB Toolboxes? Are These Different From Libraries?
  • How Do I Get Help?
  • What Are Variables?
  • How Can I Access or Display What Is Contained in a Given Variable?
  • What Is “ans” in MATLAB?
  • What Can We Call Our Variables?
  • What Is a Vector? How Do I Store a Vector in POM?
  • How Do I Calculate the Sum and Mean of All Values in a Vector?
  • We Need to Talk About the Echo
  • How Do I Calculate the Length of a Vector?
  • What Are Matrices, What Are Arrays?
  • Back to Vectors: How to Vectorize a Matrix?
  • What Can We Do With All of This?
  • The Find Function
  • Adding Matrices and Dealing With Holes in Arrays
  • What Is a Normal Distribution? How Do We Draw From One, How Do We Plot One With POM?
  • How Do I Plot Something More Meaningful?
  • How Do I Save What I’m Working On so That I Can Load It Again Later?

Part II: Neural Data Analysis

Chapter 3. Wrangling Spike Trains

  • Abstract
  • Questions We Did Not Address

Chapter 4. Correlating Spike Trains

  • Abstract

Chapter 5. Analog Signals

  • Abstract

Chapter 6. Biophysical Modeling

  • Abstract
  • Biophysical Properties of Neurons
  • Modeling
  • Why Use Simulations?
  • Why Object-Oriented Programming?
  • Python Is Inherently Object-Oriented: How Does MATLAB Implement These Things?
  • Creating theclass Neuron
  • Modeling the Response Properties of This Neuron

Part III: Going Beyond the Data

Chapter 7. Regression

  • Abstract
  • Describing the Relation Between Synaptic Potentials and Spikes

Chapter 8. Dimensionality Reduction

  • Abstract
  • Calculating the Covariance Matrix Between Variables
  • Factor Extraction as an Axis Rotation
  • Determining the Number of Factors
  • Interpreting the Meaning of Factors
  • Determining the Factor Values of the Original Variables

Chapter 9. Classification and Clustering

  • Abstract
  • Predictions, Validation, and Crossvalidation
  • Clustering

Chapter 10. Web Scraping

  • Abstract
  • What Lies Beyond 1?

Appendix A. MATLAB to Python (Table of Equivalences)

  • Comments
  • Blankspace
  • Loops
  • Exponents
  • Lists and Cells
  • Indexing
  • Importing Packages Versus Default Packages
  • Random Number Generation
  • Numerical Types

Appendix B. Frequently Made Mistakes

Appendix C. Practical Considerations, Technical Issues, Tips and Tricks

  • Package Installation
  • Python List Comprehensions
  • Python Lists Versus Numpy Arrays
  • Text Editors, The Command Line, How to Go between Sublime and the Terminal
  • Python on Windows
  • Jupyter: Using It and Its Great Functions
  • The Biggest Differences Between Python 2 and 3
  • Conventions in Python
  • MATLAB Tips and Tricks
  • Vectorization
  • Practical Considerations

Glossary (Including Additional Python and MATLAB Packages and Examples)

Details

No. of pages:
368
Language:
English
Copyright:
© Academic Press 2017
Published:
Imprint:
Academic Press
eBook ISBN:
9780128040980
Paperback ISBN:
9780128040430

About the Author

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

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

Reviews

"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

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