A Primer with MATLAB(R) and Python(TM) 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.
Author(s): Pascal Wallisch; Erik Lee Nylen
Publisher: Academic Press
Year: 2017
Language: English
Pages: 368
Front Cover
Neural Data Science
Copyright Page
Dedication
Contents
Biography
Preface
How to Use This Book
I. Foundations
1 Philosophy
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?
2 From 0 to 0.01
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?
II. Neural Data Analysis
3 Wrangling Spike Trains
Questions We Did Not Address
4 Correlating Spike Trains
Step 1
Step 2
Step 3
Step 4
Step 5
5 Analog Signals
Nyquist Frequency
Fourier Transform
Euler’s Formula
6 Biophysical Modeling
Biophysical Properties of Neurons
Modeling
Why Use Simulations?
Why Object-Oriented Programming?
Python Is Inherently Object-Oriented: How Does MATLAB Implement These Things?
Creating the class Neuron
Modeling the Response Properties of This Neuron
III. Going Beyond the Data
7 Regression
Describing the Relation Between Synaptic Potentials and Spikes
Why Logistic Regression?
What Is Logistic Regression?
What Are Odds?
How About a Specific Use Case?
What Is the Logit Function?
All of This Sounds a Bit Abstract—What Does the Logit Function Look Like?
Are We Done Yet?
What Does That Look Like?
How Does This Help?
What Does That Look Like?
What Can We Do With That?
This Is Still Too Abstract. Can We Apply This to Something More Neural?
Regularization
8 Dimensionality Reduction
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
9 Classification and Clustering
Predictions, Validation, and Crossvalidation
Clustering
10 Web Scraping
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
Version Issues
Vectorization
Practical Considerations
Glossary (Including Additional Python and MATLAB Packages and Examples)
Bibliography
Index
Back Cover
