This book addresses the growing need for machine learning and data mining in neuroscience. The book offers a basic overview of the neuroscience, machine learning and the required math and programming necessary to develop reliable working models. The material is presented in a easy to follow user-friendly manner and is replete with fully working machine learning code. Machine Learning for Neuroscience: A Systematic Approach, tackles the needs of neuroscience researchers and practitioners that have very little training relevant to machine learning. The first section of the book provides an overview of necessary topics in order to delve into machine learning, including basic linear algebra and Python programming. The second section provides an overview of neuroscience and is directed to the computer science oriented readers. The section covers neuroanatomy and physiology, cellular neuroscience, neurological disorders and computational neuroscience. The third section of the book then delves into how to apply machine learning and data mining to neuroscience and provides coverage of artificial neural networks (ANN), clustering, and anomaly detection. The book contains fully working code examples with downloadable working code. It also contains lab assignments and quizzes, making it appropriate for use as a textbook. The primary audience is neuroscience researchers who need to delve into machine learning, programmers assigned neuroscience related machine learning projects and students studying methods in computational neuroscience.
Author(s): Chuck Easttom
Publisher: CRC Press
Year: 2023
Language: English
Pages: 305
City: Boca Raton
Cover
Half Title
Title
Copyright
Contents
Preface
About the Author
Section I Required Math and Programming
Chapter 1 Fundamental Concepts of Linear Algebra for Machine Learning
Introduction
Linear Algebra Basics
Matrix Addition and Multiplication
Other Matrix Operations
Determinant of a Matrix
Vectors and Vector Spaces
Vector Metrics
Vector Length
Dot Product
Tensor Product
Cross Product
Eigenvalues and Eigenvectors
How Do We Find Its Eigenvalues?
Eigendecomposition
Summary
Test Your Knowledge
Chapter 2 Overview of Statistics
Introduction
Basic Terminology
Types of Measurement Scales
Data Collection
Measures of Central Tendency
Correlation
P-Value
Z-Test
Outliers
T-Test
Linear Regression
Additional Statistics
ANOVA
The Kruskal-Wallis
Kolmogorov-Smirnov
Statistical Errors
Power of a Test
Basic Probability
What is Probability?
Basic Set Theory
Basic Probability Rules
Conditional Probability
Independent Events
Bayes Theorem
Special Forms of Bayes’ Theorem
Summary
Test Your Skills
Chapter 3 Introduction to Python Programming
Introduction
Fundamental Python Programming
Variables and Statements
Object-Oriented Programming
IDE
IDLE
Other IDEs
Python Troubleshooting
General Tips to Remember
Basic Programming Tasks
Control Statements
Working with Strings
Working with Files
A Simple Program
Basic Math
Summary
Exercises
Exercise 1: Install Python
Exercise 2: Hello World
Exercise 3: Fibonacci Sequence
Chapter 4 More with Python
Introduction
File Management
Exception Handling
Regular Expressions
Internet Programming
Installing Modules
Specific Modules
Operating System Module
NumPy
Pandas
Scikit-Learn
PyTorch
WMI
PIL
Matplotlib
TensorFlow
The Zen of Python
Advanced Topics
Data Structures
Lists
Queue
Stack
Linked List
Algorithms
Summary
Exercises
Exercise 1: Install TensorFlow
Exercise 2: Regular Expressions and Exception Handling
Exercise 3
Section II Required Neuroscience
Chapter 5 General Neuroanatomy and Physiology
Introduction
Neuroanatomy
Neuroscience Terminology
Development View
Anatomical View
Brainstem
Cerebellum
Cerebrum
Limbic System
Spinal Cord
Neurophysiology
Neurotransmitters
Metabolism
Neuroimaging
Neurofunction
Motor Control
Perception
Summary
Test Your Knowledge
Chapter 6 Cellular Neuroscience
Introduction
Basic Neuro Cellular Structure
Types of Neurons
Synapse
Electrical Synapses
Ion Channels
Neurotransmitters
Acetylcholine
Catecholamines
Serotonin
Glutamate
Intolaimines
Gamma-Aminobutyric Acid (GABA)
Glycine
Dopamine
Peptide Neurotransmitters
Epinephrine and Norepinephrine
Agonists and Antagonists
Neurotransmitter Synthesis and Packing
Neurotransmitters and Psychoactive Substances
Cannabinoids
Opioids
Nicotine
Glial Cells
Summary
Test Your Knowledge
Chapter 7 Neurological Disorders
Specific Disorders
ALS
Epilepsy
Parkinson’s
Tourette’s
Muscular Dystrophy
Encephalitis
Depression
Progressive Supranuclear Palsy
Alzheimer’s
Meningitis
Stroke
Multiple Sclerosis
Tumors
Neurological Disorders and Machine Learning
Summary
Test Your Skills
Chapter 8 Introduction to Computational Neuroscience
Introduction
Neuron Models
Nernst Equation
Goldman Equation
Electrical Input-Output Voltage Models
Hodgkin-Huxley
FitzHugh-Nagumo Model
Leaky Integrate-and-Fire
Adaptive Integrate-and-Fire
Noisy Input Model
Hindmarsh-Rose Model
Morris-Lecar Model
Graph Theory and Computational Neuroscience
Algebraic Graph Theory
Spectral Graph Theory
Graph Similarities
Information Theory and Computational Neuroscience
Complexity and Computational Neuroscience
Emergence and Computational Neuroscience
Summary
Test Your Knowledge
Section III Machine Learning
Chapter 9 Overview of Machine Learning
Introduction
Basics of Machine Learning
Supervised Algorithms
Unsupervised Algorithms
Clustering
Anomaly Detection
Specific Algorithms
K-Nearest Neighbor
Naïve Bayes
Gradient Descent
Support Vector Machines
Feature Extraction
PCA
Artificial Intelligence
General Intelligence
Synthetic Consciousness
Summary
Exercises
Lab 1: Detecting Parkinson’s
Chapter 10 Artificial Neural Networks
Introduction
Concepts
ANN Terminology
Activation Functions
Optimization Algorithms
Models
Feedforward Neural Networks
Perception
Backpropagation
Normalization
Specific Variations of Neural Networks
Recurrent Neural Networks
Convolutional Neural Networks
Autoencoder
Spiking Neural Network
Deep Neural Networks
Neuroscience Example Code
Summary
Exercises
Lab 1: Basic TensorFlow
Lab 2: Perceptron
Chapter 11 More with ANN
Introduction
More Activation Functions
SELU
SiLU
Swish
Softsign
Algorithms
Spiking Neural Networks
Liquid State Machine
Long Short-Term Memory Neural Networks
Boltzmann Machine
Radial Basis Function Network
Deep Belief Network
Summary
Exercises
Lab 1: LSTM
Lab 2: LSTM for Neuroscience
Lab 3: Experiment with Activation Functions
Chapter 12 K-Means Clustering
Introduction
K-Means Clustering
K-Means++
K-Medians Clustering
K-Medoids
Random Forest
DBSCAN
Summary
Exercises
Exercise 1: K-Means with Alzheimer’s Data
Exercise 2: K-Means++ with Neurological Data
Chapter 13 K-Nearest Neighbors
Introduction
Examining KNN
Dimensionality Reduction
Visualize KNN
Alternatives
Deeper with Scikit-Learn
Summary
Exercises
Lab 1: KNN Parkinson’s Data
Lab 2: KNN Variations with Parkinson’s Data
Chapter 14 Self-Organizing Maps
Introduction
The SOM Algorithm
SOM in More Detail
Variations
GSOM
TASOM
Elastic Maps
Growing Self-Organizing Maps
Summary
Exercises
Lab 1: SOM for Neuroscience
Lab 2: Writing Your Own Code
Index
