Theoretical Neuroscience: Understanding Cognition

Cover
Endorsements
Half Title
Title
Copyright
Contents
Preface
Part I
	Chapter 1 Understanding the Cognitive Brain
		1.1 Introduction
		1.2 On Epistemology
		1.3 The Mind-Brain Society
		1.4 Cross-Level Mechanistic Theory
		1.5 Layout of the Book
	Chapter 2 Neurons and Synapses
		2.1 Introduction
		2.2 Integrate-and-Fire Neuron
			2.2.1 Neuronal Membrane as an RC Circuit
			2.2.2 LIF as a Simple Spiking Neuron Model
			2.2.3 Spiking Variability
		2.3 Conductance-Based Models of Single Neurons
			2.3.1 Hodgkin-Huxley Formalism of Action Potential
			2.3.2 Type I and Type II Neurons
		2.4 Time-Dependent Neuronal Firing Patterns
			2.4.1 Resonance in Response to Time-Dependent Noisy Inputs
			2.4.2 Spike Rate Adaptation
			2.4.3 Input Decorrelation
		2.5 Burst Firing
			2.5.1 Ping-Pong Interplay between Soma and Dendrite
			2.5.2 Postinhibitory Rebound
			2.5.3 Clustered and Irregular Spiking
		2.6 Single Synapse Models
			2.6.1 Kick Synapses
			2.6.2 Filter and Kinetic Models of Synaptic Transmission
			2.6.3 NMDA Receptor-Mediated Synaptic Excitation
		2.7 Short-Term Synaptic Plasticity
			2.7.1 Short-Term Synaptic Depression
			2.7.2 Short-Term Synaptic Facilitation
		2.8 Summary
	Chapter 3 Neural Networks
		3.1 Introduction
		3.2 Network Dynamics of Spiking Neurons
			3.2.1 Signal Propagation in a Feedforward Network
			3.2.2 Excitation and Inhibition Balance and Asynchronous State in a Recurrent Network
			3.2.3 Neuronal Correlations
		3.3 Population Rate Models
			3.3.1 Formulations of Rate Models
			3.3.2 Neural Integrator
			3.3.3 Inhibition-Stabilization and Balanced Amplification
		3.4 Coherent Neural Circuit Oscillations
			3.4.1 Synchronization of Neural Oscillators
			3.4.2 Sparsely Synchronous Rhythm
			3.4.3 At the Edge of Criticality
		3.5 Network Models of Information Representation
			3.5.1 Feedforward Continuous Network Model
			3.5.2 Normalization
			3.5.3 Recurrent Continuous Network Model
		3.6 Computing with Spatiotemporal Dynamics
			3.6.1 Time Integration
			3.6.2 Spatial Navigation
			3.6.3 Propagating Waves
		3.7 Reservoir Computing
			3.7.1 State Space, Dimensionality and Manifolds
			3.7.2 Feedforward Random Networks
			3.7.3 Recurrent Random Networks
		3.8 Summary
	Chapter 4 Plasticity, Learning and Memory
		4.1 Introduction
		4.2 Supervised Learning
		4.3 Reinforcement Learning
			4.3.1 The Rescorla-Wagner Rule and Reward Prediction Error
			4.3.2 Reward Signaling by the Dopamine System
			4.3.3 Action Valuation and Selection
			4.3.4 Temporal-Difference Learning
		4.4 Unsupervised Learning
			4.4.1 Hebbian Plasticity Rules
			4.4.2 Pattern Formation during Brain Development
			4.4.3 Spike-Timing Dependent Plasticity
			4.4.4 A Calcium-Based Plasticity Model
			4.4.5 Molecular Basis of Memories
			4.4.6 Homeostasis, Non-Hebbian and Non-Synaptic Plasticity
		4.5 Storage Capacity and Memory Retrieval
			4.5.1 Ideal Observer Analysis of Memory Capacity
			4.5.2 Hopfield Model of Associative Memory
			4.5.3 Plasticity-Stability Dilemma
		4.6 Memory Consolidation
		4.7 Summary
Part II
	Chapter 5 Working Memory
		5.1 Introduction
		5.2 Neural Representation of Working Memory
			5.2.1 Delay-Dependent Task and Self-Sustained Mnemonic Activity
			5.2.2 Three Types of Neuronal Working Memory Coding
			5.2.3 Feedback Mechanisms of Persistent Activity
		5.3 Attractor Network Model of Working Memory
			5.3.1 A Simple Rate Model
			5.3.2 Network Model of Stimulus-Selective Persistent Activity
			5.3.3 How Many Parameters Does This Model Have?
			5.3.4 Emergence of Self-Sustained Activity from a Bifurcation
			5.3.5 Inverted U-Shape of Dopamine Dependence
		5.4 Continuous Attractor Model for Spatial Working Memory
			5.4.1 A Model of the Oculomotor Delayed Response Task
			5.4.2 Stochastic Gamma Oscillations during Delay Period Activity
			5.4.3 Drifts of Neural Representation across the Delay
			5.4.4 Resistance against Distractors
		5.5 Line Attractors: Parametric Working Memory
		5.6 Yin and Yang of Neuronal Reverberation
			5.6.1 The Excitation-Inhibition Balance
			5.6.2 The Role of NMDA Receptors
			5.6.3 The Importance of Being Slow But Not Too Slow
			5.6.4 Cannabinoid Modulation and Cross-Trial Serial Effect
			5.6.5 Disinhibition Motif by Three Subtypes of Inhibitory Cells
		5.7 Limited Working Memory Capacity
		5.8 Dynamical Nature of Mnemonic Representation
			5.8.1 Dynamical Coding and Heterogenous Delay Activity
			5.8.2 Self-Sustained or Decaying Transient?
			5.8.3 Persistent Activity Is Required for Manipulation of Information in Working Memory
		5.9 Summary
	Chapter 6 Decision Making
		6.1 Introduction
		6.2 Mathematical Models of Decision Making
			6.2.1 Signal Detection Theory
			6.2.2 Drift Diffusion Model
			6.2.3 Race Models
			6.2.4 Bayesian Modeling
		6.3 Neural Circuit Mechanism of Decision Making
			6.3.1 Neural Correlates
			6.3.2 A Recurrent Neural Circuit Model
			6.3.3 State-Space Trajectories of Population Dynamics
		6.4 Termination Rule for a Decision Process
			6.4.1 Ramping-to-Threshold in the Brain
			6.4.2 Chronometric Function and Scale Invariance of Reaction Times
			6.4.3 The Biological Substrate of a Decision Threshold
			6.4.4 Speed-Accuracy Tradeoff
		6.5 Multi-Alternative Decisions
		6.6 Diverse Types of Perceptual Decisions
			6.6.1 Detection
			6.6.2 Comparison and Discrimination
			6.6.3 Pattern Match Decisions
		6.7 Confidence and Changes of Mind
		6.8 Duality of Cognitive-Type Neural Circuits
		6.9 Summary
	Chapter 7 Value-Based Economic Choice
		7.1 Introduction
		7.2 Neuroeconomics and Foraging Theory
		7.3 Neural Circuit Mechanism for Value-Based Choice
			7.3.1 Dopamine and Synaptic Plasticity
			7.3.2 A Decision-Making Network Model Endowed with Reward-Dependent Learning
			7.3.3 Computation of Returns by Synapses: Matching Law through Melioration
		7.4 Valuation
			7.4.1 Computation of Common Currency
			7.4.2 Cost and Regret
			7.4.3 Predictive Valuation
			7.4.4 Multi-Attribute Choice
		7.5 Probabilistic Reasoning
		7.6 Social Decision Making
			7.6.1 Random Choice Behavior in Matching Pennies Game
			7.6.2 Volatility and Reinforcement Learning on Multiple Timescales
			7.6.3 Cooperation
		7.7 Summary
	Chapter 8 Executive Function
		8.1 Introduction
		8.2 Response Inhibition
			8.2.1 Race Model and Neurophysiology of a Stop-Signal Task
			8.2.2 A Neural Circuit Model of Countermanding
			8.2.3 Role of Basal Ganglia in “Holding the Horse”
			8.2.4 Pro- versus Anti-Response
		8.3 Timing
		8.4 Selective Attention
			8.4.1 Biased Competition and Multiplicative Gain Modulation
			8.4.2 An Integrative Circuit Model of Selective Attention
			8.4.3 Attention Modulation of Network Synchrony and Noise Correlation
		8.5 Task Switching
		8.6 Behavioral Flexibility and Mixed Selectivity
		8.7 Summary
Part III
	Chapter 9 Large-Scale Multi-Regional Brain
		9.1 Introduction
		9.2 Cortex-Wide Connectivity
			9.2.1 Connectome
			9.2.2 Directed and Weighted Inter-Areal Cortical Connections
			9.2.3 Exponential Distance Rule
			9.2.4 A Generative Model of Spatially Embedded Neocortex
			9.2.5 Cortical Hierarchy
		9.3 Macroscopic Gradients
			9.3.1 Heterogeneous Variations of a Canonical Circuit
			9.3.2 Macroscopic Gradients of Synaptic Excitation
			9.3.3 Macroscopic Gradient of Input- versus Output-Controlling Inhibition
		9.4 A Hierarchy of Timescales
			9.4.1 A Dynamical Model of Multi-Regional Monkey Cortex
			9.4.2 A Spatial Localization Measure
			9.4.3 Experimental Observations of Timescale Hierarchy
		9.5 Functional Connectivity and Inter-Areal Communication
			9.5.1 Functional Connectivity in a Resting State
			9.5.2 Layer-Dependent Feedforward and Feedback Processes
			9.5.3 Gating of Inter-Areal Communication
		9.6 Distributed Working Memory
			9.6.1 The Parieto-Frontal Loop
			9.6.2 Distributed Mnemonic Activity in the Cortex
			9.6.3 Bifurcation in Space: Emergence of Modularity
			9.6.4 A Diversity of Spatially Distributed Persistent States
			9.6.5 Macroscopic Gradient of Dopamine Modulation
		9.7 Distributed Decision Making
		9.8 Summary
	Chapter 10 Computational Psychiatry
		10.1 Introduction
		10.2 Mental Disorder Classification versus Dimensional Psychiatry
		10.3 Reinforcement Learning Models of Behavioral Disorders
			10.3.1 Task Design and Behavioral Quantification
			10.3.2 Mood and Depression
			10.3.3 Addiction
		10.4 Deficits of Executive Control
			10.4.1 Loss of Control in Addiction and Depression
			10.4.2 Negative Bias in Anxiety and Obsessive-Compulsive Disorder
			10.4.3 Reactive versus Proactive Control in Schizophrenia
		10.5 Neural Circuit Models of Cognitive Deficits
			10.5.1 Working Memory
			10.5.2 Decision Making
			10.5.3 Critical Role of E/I Balance
		10.6 Deficits in Multi-Regional Brain Systems
			10.6.1 Abnormal Default-Mode Network
			10.6.2 Altered Macroscopic Gradients
			10.6.3 Deficits in Top-Down Signaling
		10.7 Big Data and Model-Aided Diagnosis
		10.8 Summary
	Chapter 11 Biological and Artificial Intelligence
		11.1 Introduction
		11.2 Deep Feedforward Neural Networks
			11.2.1 Basic Methods of Deep Neural Network Models
			11.2.2 Deep Neural Network Modeling and the Brain
		11.3 Cognitive-Type Recurrent Neural Networks
		11.4 Abstraction
			11.4.1 Categorization
			11.4.2 Factorized Code for Abstract Knowledge
			11.4.3 Task Set
		11.5 Learning-to-Learn
		11.6 Reasoning and Fluid Intelligence
			11.6.1 Compositionality
			11.6.2 Inference and Cognitive Maps
			11.6.3 Mental Programming and Intelligence
			11.6.4 Cross-Scale Brain Basis of Intelligence
		11.7 Summary
	Chapter 12 Looking Back and Ahead
		12.1 Building Blocks of Behavior and Cognition
		12.2 Take-Home Messages
		12.3 Shifting Perspectives
		12.4 Less Charted Territories
References
Index