Data-Driven Computational Neuroscience: Machine Learning and Statistical Models

✍ Scribed by Concha Bielza, Pedro Larrañaga

• Publisher: Cambridge University Press
• Year: 2020
• Tongue: English
• Leaves: 757
• Category: Library

✦ Synopsis

Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered.

✦ Table of Contents

Copyright
Contents
Preface
List of Acronyms
Part I Introduction
1 Computational Neuroscience
1.1 The Multilevel Organization of the Brain
1.2 The Human Brain
1.3 Brain Research Initiatives
1.4 Neurotechnologies
1.5 Data-Driven Computational Neuroscience
1.6 Real Examples Discussed in This Book
Part II Statistics
2 Exploratory Data Analysis
2.1 Data Types
2.2 Univariate Data
2.3 Bivariate Data
2.4 Multivariate Data
2.5 Imputation of Missing Data
2.6 Variable Transformation
2.7 Bibliographic Notes
3 Probability Theory and Random Variables
3.1 Probability Theory
3.2 Univariate Discrete Distributions
3.3 Univariate Continuous Distributions
3.4 Multivariate Probability Distributions
3.5 Simulating Random Variates
3.6 Information Theory
3.7 Bibliographic Notes
4 Probabilistic Inference
4.1 Parameter Estimation
4.2 Hypothesis Tests
4.3 Bibliographic Notes
Part III Supervised Classification
5 Performance Evaluation
5.1 The Learning Problem
5.2 Performance Measures
5.3 Performance Estimation
5.4 Statistical Significance Testing
5.5 Imbalanced Data Sets and Anomaly Detection
5.6 Bibliographic Notes
6 Feature Subset Selection
6.1 Overview of Feature Subset Selection
6.2 Filter Approaches
6.3 Wrapper Methods
6.4 Embedded Methods
6.5 Hybrid Feature Selection
6.6 Feature Selection Stability
6.7 Example: GABAergic Interneuron Nomenclature
6.8 Bibliographic Notes
7 Non-probabilistic Classifiers
7.1 Nearest Neighbors
7.2 Classification Trees
7.3 Rule Induction
7.4 Artificial Neural Networks
7.5 Support Vector Machines
7.6 Bibliographic Notes
8 Probabilistic Classifiers
8.1 Bayes Decision Rule
8.2 Discriminant Analysis
8.3 Logistic Regression
8.4 Bayesian Network Classifiers
8.5 Bibliographic Notes
9 Metaclassifiers
9.1 Main Ideas on Metaclassifiers
9.2 Combining the Outputs of Different Classifiers
9.3 Popular Metaclassifiers
9.4 Example: Interneurons versus Pyramidal Neurons
9.5 Example: Interneurons versus Pyramidal Neurons; Comparison of All Classifiers
9.6 Bibliographic Notes
10 Multidimensional Classifiers
10.1 Multi-label and Multidimensional Classification
10.2 Equivalent Notations for Multi-label Classification
10.3 Performance Evaluation Measures
10.4 Learning Methods
10.5 Example: Quality of Life in Parkinson’s Disease
10.6 Bibliographic Notes
Part IV Unsupervised Classification
11 Non-probabilistic Clustering
11.1 Similarity/Dissimilarity between Objects
11.2 Hierarchical Clustering
11.3 Partitional Clustering
11.4 Choice of the Number of Clusters
11.5 Subspace Clustering
11.6 Cluster Ensembles
11.7 Evaluation Criteria
11.8 Example: Dendritic Spines
11.9 Bibliographic Notes
12 Probabilistic Clustering
12.1 The Expectation-Maximization Algorithm
12.2 Finite-Mixture Models for Clustering
12.3 Clustering with Bayesian Networks
12.4 Example: Dendritic Spines
12.5 Bibliographic Notes
Part V Probabilistic Graphical Models
13 Bayesian Networks
13.1 Basics of Bayesian Networks
13.2 Inference in Bayesian Networks
13.3 Learning Bayesian Networks from Data
13.4 Dynamic Bayesian Networks
13.5 Example: Basal Dendritic Trees
13.6 Bibliographic Notes
14 Markov Networks
14.1 Definition and Basic Properties
14.2 Factorization of the Joint Probability Distribution
14.3 Inference in Markov Networks
14.4 Learning Continuous Markov Networks
14.5 Learning Discrete Markov Networks
14.6 Conditional Random Fields
14.7 Example: Functional Brain Connectivity of Alzheimer’s Disease
14.8 Bibliographic Notes
Part VI Spatial Statistics
15 Spatial Statistics
15.1 Basic Concepts of Spatial Point Processes
15.2 Complete Spatial Randomness
15.3 Goodness-of-Fit Tests via Simulation
15.4 Data Collection Issues
15.5 Common Models of Spatial Point Processes
15.6 Example: Spatial Location of Synapses in the Neocortex
15.7 Bibliographic Notes
Bibliography
Subject Index