Xiao-Jing Wang. Theoretical Neuroscience: Understanding Cognition

Boca Raton: CRC Press, 2025. — 576 p.This textbook is an introduction to Systems and Theoretical/Computational Neuroscience, with a particular emphasis on cognition. It consists of three parts: Part I covers fundamental concepts and mathematical models in computational neuroscience, along with cutting-edge topics. Part II explores the building blocks of cognition, including working memory (how the brain maintains and manipulates information "online" without external input), decision making (how choices are made among multiple options under conditions of uncertainty and risk) and behavioral flexibility (how we direct attention and control actions). Part III is dedicated to frontier research, covering models of large-scale multi-regional brain systems, Computational Psychiatry and the interface with Artificial Intelligence. The author highlights the perspective of neural circuits as dynamical systems, and emphasizes a cross-level mechanistic understanding of the brain and mind, from genes and cell types to collective neural populations and behavior. Overall, this textbook provides an opportunity for readers to become well versed in this highly interdisciplinary field of the twenty-first century.
Key Features
Rooted in the most recent advances in experimental studies of basic cognitive functionsIntroduces neurobiological and mathematical concepts so that the book is self-containedHeavily illustrated with high-quality figures that help to illuminate neurobiological concepts, present experimental findings and explain mathematical modelsConcludes with a list of core cognitive behavior tasks, ten take-home messages and three open questions for future researchComputer model codes are available via GitHub for hands-on practicePrefaceUnderstanding the Cognitive Brain
Introduction
On Epistemology
The Mind-Brain Society
Cross-Level Mechanistic Theory
Layout of the Book
Neurons and Synapses
Introduction
Integrate-and-Fire Neuron
Neuronal Membrane as an RC Circuit
LIF as a Simple Spiking Neuron Model
Spiking Variability
Conductance-Based Models of Single Neurons
Hodgkin-Huxley Formalism of Action Potential
Type I and Type II Neurons
Time-Dependent Neuronal Firing Patterns
Resonance in Response to Time-Dependent Noisy Inputs
Spike Rate Adaptation
Input Decorrelation
Burst Firing
Ping-Pong Interplay between Soma and Dendrite
Postinhibitory Rebound
Clustered and Irregular Spiking
Single Synapse Models
Kick Synapses
Filter and Kinetic Models of Synaptic Transmission
NMDA Receptor-Mediated Synaptic Excitation
Short-Term Synaptic Plasticity
Short-Term Synaptic Depression
Short-Term Synaptic Facilitation
Summary
Neural Networks
Introduction
Network Dynamics of Spiking Neurons
Signal Propagation in a Feedforward Network
Excitation and Inhibition Balance and Asynchronous State in a Recurrent Network
Neuronal Correlations
Population Rate Models
Formulations of Rate Models
Neural Integrator
Inhibition-Stabilization and Balanced Amplification
Coherent Neural Circuit Oscillations
Synchronization of Neural Oscillators
Sparsely Synchronous Rhythm
At the Edge of Criticality
Network Models of Information Representation
Feedforward Continuous Network Model
Normalization
Recurrent Continuous Network Model
Computing with Spatiotemporal Dynamics
Time Integration
Spatial Navigation
Propagating Waves
Reservoir Computing
State Space, Dimensionality and Manifolds
Feedforward Random Networks
Recurrent Random Networks
Summary
Plasticity, Learning and Memory
Introduction
Supervised Learning
Reinforcement Learning
The Rescorla-Wagner Rule and Reward Prediction Error
Reward Signaling by the Dopamine System
Action Valuation and Selection
Temporal-Difference Learning
Unsupervised Learning
Hebbian Plasticity Rules
Pattern Formation during Brain Development
Spike-Timing Dependent Plasticity
A Calcium-Based Plasticity Model
Molecular Basis of Memories
Homeostasis, Non-Hebbian and Non-Synaptic Plasticity
Storage Capacity and Memory Retrieval
Ideal Observer Analysis of Memory Capacity
Hopfield Model of Associative Memory
Plasticity-Stability Dilemma
Memory Consolidation
SummaryWorking Memory
Introduction
Neural Representation of Working Memory
Delay-Dependent Task and Self-Sustained Mnemonic Activity
Three Types of Neuronal Working Memory Coding
Feedback Mechanisms of Persistent Activity
Attractor Network Model of Working Memory
A Simple Rate Model
Network Model of Stimulus-Selective Persistent Activity
How Many Parameters Does This Model Have?
Emergence of Self-Sustained Activity from a Bifurcation
Inverted U-Shape of Dopamine Dependence
Continuous Attractor Model for Spatial Working Memory
A Model of the Oculomotor Delayed Response Task
Stochastic Gamma Oscillations during Delay Period Activity
Drifts of Neural Representation across the Delay
Resistance against Distractors
Line Attractors: Parametric Working Memory
Yin and Yang of Neuronal Reverberation
The Excitation-Inhibition Balance
The Role of NMDA Receptors
The Importance of Being Slow But Not Too Slow
Cannabinoid Modulation and Cross-Trial Serial Effect
Disinhibition Motif by Three Subtypes of Inhibitory Cells
Limited Working Memory Capacity
Dynamical Nature of Mnemonic Representation
Dynamical Coding and Heterogenous Delay Activity
Self-Sustained or Decaying Transient?
Persistent Activity Is Required for Manipulation of Information in Working Memory
Summary
Decision Making
Introduction
Mathematical Models of Decision Making
Signal Detection Theory
Drift Diffusion Model
Race Models
Bayesian Modeling
Neural Circuit Mechanism of Decision Making
Neural Correlates
A Recurrent Neural Circuit Model
State-Space Trajectories of Population Dynamics
Termination Rule for a Decision Process
Ramping-to-Threshold in the Brain
Chronometric Function and Scale Invariance of Reaction Times
The Biological Substrate of a Decision Threshold
Speed-Accuracy Tradeoff
Multi-Alternative Decisions
Diverse Types of Perceptual Decisions
Detection
Comparison and Discrimination
Pattern Match Decisions
Confidence and Changes of Mind
Duality of Cognitive-Type Neural Circuits
Summary
Value-Based Economic Choice
Introduction
Neuroeconomics and Foraging Theory
Neural Circuit Mechanism for Value-Based Choice
Dopamine and Synaptic Plasticity
A Decision-Making Network Model Endowed with Reward-Dependent Learning
Computation of Returns by Synapses: Matching Law through Melioration
Valuation
Computation of Common Currency
Cost and Regret
Predictive Valuation
Multi-Attribute Choice
Probabilistic Reasoning
Social Decision Making
Random Choice Behavior in Matching Pennies Game
Volatility and Reinforcement Learning on Multiple Timescales
Cooperation
Summary
Executive Function
Introduction
Response Inhibition
Race Model and Neurophysiology of a Stop-Signal Task
A Neural Circuit Model of Countermanding
Role of Basal Ganglia in “Holding the Horse”
Pro- versus Anti-Response
Timing
Selective Attention
Biased Competition and Multiplicative Gain Modulation
An Integrative Circuit Model of Selective Attention
Attention Modulation of Network Synchrony and Noise Correlation
Task Switching
Behavioral Flexibility and Mixed Selectivity
SummaryLarge-Scale Multi-Regional Brain
Introduction
Cortex-Wide Connectivity
Connectome
Directed and Weighted Inter-Areal Cortical Connections
Exponential Distance Rule
A Generative Model of Spatially Embedded Neocortex
Cortical Hierarchy
Macroscopic Gradients
Heterogeneous Variations of a Canonical Circuit
Macroscopic Gradients of Synaptic Excitation
Macroscopic Gradient of Input- versus Output-Controlling Inhibition
A Hierarchy of Timescales
A Dynamical Model of Multi-Regional Monkey Cortex
A Spatial Localization Measure
Experimental Observations of Timescale Hierarchy
Functional Connectivity and Inter-Areal Communication
Functional Connectivity in a Resting State
Layer-Dependent Feedforward and Feedback Processes
Gating of Inter-Areal Communication
Distributed Working Memory
The Parieto-Frontal Loop
Distributed Mnemonic Activity in the Cortex
Bifurcation in Space: Emergence of Modularity
A Diversity of Spatially Distributed Persistent States
Macroscopic Gradient of Dopamine Modulation
Distributed Decision Making
Summary
Computational Psychiatry
Introduction
Mental Disorder Classification versus Dimensional Psychiatry
Reinforcement Learning Models of Behavioral Disorders
Task Design and Behavioral Quantification
Mood and Depression
Addiction
Deficits of Executive Control
Loss of Control in Addiction and Depression
Negative Bias in Anxiety and Obsessive-Compulsive Disorder
Reactive versus Proactive Control in Schizophrenia
Neural Circuit Models of Cognitive Deficits
Working Memory
Decision Making
Critical Role of E/I Balance
Deficits in Multi-Regional Brain Systems
Abnormal Default-Mode Network
Altered Macroscopic Gradients
Deficits in Top-Down Signaling
Big Data and Model-Aided Diagnosis
Summary
Biological and Artificial Intelligence
Introduction
Deep Feedforward Neural Networks
Basic Methods of Deep Neural Network Models
Deep Neural Network Modeling and the Brain
Cognitive-Type Recurrent Neural Networks
Abstraction
Categorization
Factorized Code for Abstract Knowledge
Task Set
Learning-to-Learn
Reasoning and Fluid Intelligence
Compositionality
Inference and Cognitive Maps
Mental Programming and Intelligence
Cross-Scale Brain Basis of Intelligence
Summary
Looking Back and Ahead
Building Blocks of Behavior and Cognition
Take-Home Messages
Shifting Perspectives
Less Charted Territories
References
Index