Machine Learning for Neuroscience: A Systematic Approach

Description

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. 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