Machine Learning for Neuroscience: A Systematic Approach

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