Contents

 
• Introduction and Overview
  • Computational Cognitive Neuroscience
  • Basic Motivations for Computational Cognitive Neuroscience
    • Physical Reductionism
    • Reconstructionism
    • Levels of Analysis
    • Scaling Issues
  • Historical Context
  • Overview of Our Approach
  • General Issues in Computational Modeling
    • Advantages:
    • Problems:
  • Motivating Cognitive Phenomena and Their Biological Bases
    • Parallelism
    • Gradedness
    • Interactivity
    • Competition
    • Learning
  • Organization of the Book
  • Further Reading
• Basic Neural Computational Mechanisms
  • Individual Neurons
    • Overview
    • Detectors: How to Think About a Neuron
      • Understanding the Parts of the Neuron Using the Detector Model
    • The Biology of the Neuron
      • The Axon
      • The Synapse
      • The Dendrite
    • The Electrophysiology of the Neuron
      • Basic Electricity
      • Diffusion
      • Electric Potential versus Diffusion: The Equilibrium Potential
      • The Neural Environs and Ions
      • Putting It All Together: Integration
      • The Equilibrium Membrane Potential
      • Summary
    • Computational Implementation of the Neural Activation Function
      • Computing Input Conductances
        • Details of Input Conductance Computation
        • Differential Projection-Level Scaling
        • How Much of Dendritic Integration in Real Neurons Does Our Model Capture?
      • Point Neuron Parameter Values
      • The Discrete Spiking Output Function
      • The Rate Code Output Function
      • Summary
    • Explorations of the Individual Neuron
      • The Membrane Potential
      • The Activation Output
      • The Neuron as Detector
    • Hypothesis Testing Analysis of a Neural Detector
      • Objective Probabilities and Example
      • Subjective Probabilities
      • Similarity of and P(h|d)
    • The Importance of Keeping It Simple
    • Self-Regulation: Accommodation and Hysteresis
      • Implementation of Accommodation and Hysteresis
        • Exploration of Accommodation and Hysteresis
    • Summary
      • Neuron as Detector
      • Biology of the Neuron
      • Computational Implementation of the Neural Activation Function
      • Self-Regulation
    • Further Reading
  • Networks of Neurons
    • Overview
    • General Structure of Cortical Networks
    • Unidirectional Excitatory Interactions: Transformations
      • Exploration of Transformations
        • Bias Weights
        • Cluster Plots
        • Selectivity and Leak
        • Letter Inputs
      • Localist versus Distributed Representations
      • Exploration of Distributed Representations
    • Bidirectional Excitatory Interactions
      • Bidirectional Transformations
      • Bidirectional Pattern Completion
      • Bidirectional Amplification
        • Exploration of Simple Top-Down Amplification
        • Exploration of Amplification with Distributed Representations
      • Attractor Dynamics
    • Inhibitory Interactions
      • General Functional Benefits of Inhibition
      • Exploration of Feedforward and Feedback Inhibition
        • Strength of Inhibitory Conductances
        • Roles of Feedforward and Feedback Inhibition
        • Time Constants and Feedforward Anticipation
        • Effects of Learning
        • Bidirectional Excitation
        • Set Point Behavior
      • The k-Winners-Take-All Inhibitory Functions
        • kWTA Function Implementation
      • Exploration of kWTA Inhibition
      • Digits Revisited with kWTA Inhibition
      • Other Simple Inhibition Functions
    • Constraint Satisfaction
      • Attractors Again
      • The Role of Noise
      • The Role of Inhibition
      • Explorations of Constraint Satisfaction: Cats and Dogs
      • Explorations of Constraint Satisfaction: Necker Cube
    • Summary
      • General Structure of Cortical Networks
      • Excitatory Interactions
      • Inhibitory Interactions
      • Constraint Satisfaction
    • Further Reading
  • Hebbian Model Learning
    • Overview
    • Biological Mechanisms of Learning
    • Computational Objectives of Learning
      • Simple Exploration of Correlational Model Learning
    • Principal Components Analysis
      • Simple Hebbian PCA in One Linear Unit
      • Oja's Normalized Hebbian PCA
    • Conditional Principal Components Analysis
      • The CPCA Learning Rule
      • Derivation of CPCA Learning Rule
      • Biological Implementation of CPCA Hebbian Learning
    • Exploration of Hebbian Model Learning
    • Renormalization and Contrast Enhancement
      • Renormalization
      • Contrast Enhancement
      • Exploration of Renormalization and Contrast Enhancement in CPCA
    • Self-Organizing Model Learning
      • Exploration of Self-Organizing Learning
        • Unique Pattern Statistic
        • Parameter Manipulations
      • Summary and Discussion
    • Other Approaches to Model Learning
      • Algorithms That Use CPCA-Style Hebbian Learning
      • Clustering
      • Topography
      • Information Maximization and MDL
      • Learning Based Primarily on Hidden Layer Constraints
      • Generative Models
    • Summary
      • Biological Mechanisms of Learning
      • Hebbian Model Learning
    • Further Reading
  • Error-Driven Task Learning
    • Overview
    • Exploration of Hebbian Task Learning
    • Using Error to Learn: The Delta Rule
      • Deriving the Delta Rule
      • Learning Bias Weights
    • Error Functions, Weight Bounding, and Activation Phases
      • Cross Entropy Error
      • Soft Weight Bounding
      • Activation Phases in Learning
    • Exploration of Delta Rule Task Learning
    • The Generalized Delta Rule: Backpropagation
      • Derivation of Backpropagation
      • Generic Recursive Formulation
      • The Biological Implausibility of Backpropagation
    • The Generalized Recirculation Algorithm
      • Derivation of GeneRec
      • Symmetry, Midpoint, and CHL
        • GeneRec, CHL and other Algorithms
    • Biological Considerations for GeneRec
      • Weight Symmetry in the Cortex
      • Phase-Based Activations in the Cortex
      • Synaptic Modification Mechanisms
    • Exploration of GeneRec-Based Task Learning
    • Summary
    • Further Reading
  • Combined Model and Task Learning, and Other Mechanisms
    • Overview
    • Combined Hebbian and Error-driven Learning
      • Pros and Cons of Hebbian and Error-Driven Learning
      • Advantages to Combining Hebbian and Error-Driven Learning
      • Inhibitory Competition as a Model-Learning Constraint
      • Implementation of Combined Model and Task Learning
      • Summary
    • Generalization in Bidirectional Networks
      • Exploration of Generalization
    • Learning to Re-represent in Deep Networks
      • Exploration of a Deep Network
    • Sequence and Temporally Delayed Learning
    • Context Representations and Sequential Learning
      • Computational Considerations for Context Representations
      • Possible Biological Bases for Context Representations
      • Exploration: Learning the Reber Grammar
      • Summary
    • Reinforcement Learning for Temporally Delayed Outcomes
      • Behavior and Biology of Reinforcement Learning
      • The Temporal Differences Algorithm
      • Phase-Based Temporal Differences
      • Exploration of TD: Classical Conditioning
    • Summary
      • Combined Model and Task Learning
      • Sequence and Temporally Delayed Learning
    • Further Reading
• Large-Scale Brain Area Organization and Cognitive Phenomena
  • Large-Scale Brain Area Functional Organization
    • Overview
    • General Computational and Functional Principles
      • Structural Principles
        • Hierarchical Structure
        • Specialized Pathways
        • Inter-Pathway Interactions
        • Higher-Level Association Areas
        • Large-Scale Distributed Representation
        • Dedicated, Content-Specific Processing and Representations
      • Dynamic Principles
        • Mutual Support and Active Memory
        • Inhibition and Attention
    • General Functions of the Cortical Lobes and Subcortical Areas
      • Cortex
      • Limbic System
      • The Thalamus
      • The Basal Ganglia, Cerebellum, and Motor Control
    • Tripartite Functional Organization
      • Slow Integrative versus Fast Separating Learning
      • Active Memory versus Overlapping Distributed Representations
    • Toward a Cognitive Architecture of the Brain
      • Controlled versus Automatic Processing
      • Declarative/Procedural and Explicit/Implicit Distinctions
    • General Problems
      • The Binding Problem for Distributed Representations of Multiple Items
      • Representing Multiple Instances of the Same Thing
      • Comparing Representations
      • Representing Hierarchical Relationships
      • Recursion and Subroutine-like Processing
      • Generalization, Generativity, and Abstraction
      • Summary of General Problems
    • Summary
  • Perception and Attention
    • Overview
    • Biology of the Visual System
      • The Retina
      • The LGN of the Thalamus
      • Primary Visual Cortex: V1
      • Two Visual Processing Streams
      • The Ventral Visual Form Pathway: V2, V4, and IT
      • The Dorsal Where/Action Pathway
    • Primary Visual Representations
      • Basic Properties of the Model
      • Exploring the Model
      • Summary and Discussion
    • Object Recognition and the Visual Form Pathway
      • Basic Properties of the Model
        • Additional Model Details
      • Exploring the Model
        • Activation-Based Receptive Field Analysis
        • Probe Stimulus-Based Receptive Field Analysis
        • Sweep Analysis
        • Summary and Discussion of Receptive Field Analyses
        • Generalization Test
      • Summary and Discussion
    • Spatial Attention: A Simple Model
      • Basic Properties of the Model
        • Effects of Spatial Pathway Lesions
        • Object-Based Attention
      • Exploring the Simple Attentional Model
        • Perceiving Multiple Objects
        • The Posner Spatial Cuing Task
        • Effects of Spatial Pathway Lesions
        • Temporal Dynamics and Inhibition of Return
        • Object-Based Attentional Effects
      • Summary and Discussion
    • Spatial Attention: A More Complex Model
      • Exploring the Complex Attentional Model
      • Summary and Discussion
    • Summary
    • Further Reading
  • Memory
    • Overview
    • Weight-Based Memory in a Generic Model of Cortex
      • Long-Term Priming
        • Exploring the Model
        • Summary and Discussion
      • AB-AC List Learning
        • Exploring the Model
        • Summary and Discussion
    • The Hippocampal Memory System
      • Anatomy and Physiology of the Hippocampus
      • Basic Properties of the Hippocampal Model
        • Encoding and Retrieval
        • Pattern Separation
        • Pattern Completion
        • Details of the Model
      • Explorations of the Hippocampus
      • Summary and Discussion
    • Activation-Based Memory in a Generic Model of Cortex
      • Short-Term Priming
        • Exploring the Model
      • Active Maintenance
        • Exploring the Model
      • Robust yet Rapidly Updatable Active Maintenance
        • Exploring the Model
    • The Prefrontal Cortex Active Memory System
      • Dynamic Regulation of Active Maintenance
      • Details of the Prefrontal Cortex Model
      • Exploring the Model
      • Summary and Discussion
    • The Development and Interaction of Memory Systems
      • Basic Properties of the Model
      • Exploring the Model
      • Summary and Discussion
    • Memory Phenomena and System Interactions
      • Recognition Memory
      • Cued Recall
      • Free Recall
      • Item Effects
      • Working Memory
    • Summary
    • Further Reading
  • Language
    • Overview
    • The Biology and Basic Representations of Language
      • Biology
      • Phonology
        • Vowels
        • Consonants
        • Words
    • The Distributed Representation of Words and Dyslexia
      • Comparison with Traditional Dual-Route Models
      • The Interactive Model and Division of Labor
      • Dyslexia
      • Basic Properties of the Model
      • Exploring the Model
        • Normal Reading Performance
        • Reading with Complete Pathway Lesions
        • Reading with Partial Pathway Lesions
      • Summary and Discussion
    • The Orthography to Phonology Mapping
      • Basic Properties of the Model
      • Exploring the Model
        • Reading Words
        • Network Connectivity and Learning
        • Nonword Pronunciation
        • Naming Latencies
      • Summary and Discussion
    • Overregularization in Past-Tense Inflectional Mappings
      • U-Shaped Curve of Overregularization
      • Existing Neural Network Models
      • A Competitive, Priming-Based Model
      • Basic Properties of the Model
      • Exploring the Model
      • Summary and Discussion
    • Semantic Representations from Word Co-occurrences and Hebbian Learning
      • Basic Properties of the Model
      • Exploring the Model
        • Individual Unit Representations
        • Distributed Representations via Sending Weights
        • Summarizing Similarity with Cosines
        • Distributed Representations via Activity Patterns
        • A Multiple-Choice Quiz
      • Summary and Discussion
    • Sentence-Level Processing
      • Basic Properties of the Model
        • Semantics
        • Syntax
        • Network Structure and Training
      • Exploring the Model
        • Training
        • Testing
        • Nature of Representations
      • Summary and Discussion
    • Summary
    • Further Reading
  • Higher-Level Cognition
    • Overview
      • Framing the Challenge of Higher-Level Cognition
      • The Importance of Activation-Based Processing
      • The Control of Activation-Based Processing
      • The Nature of Activation-Based Processing Representations
      • Chapter Organization
    • Biology of the Frontal Cortex
    • Controlled Processing and the Stroop Task
      • Basic Properties of the Model
      • Exploring the Model
        • Basic Stroop Task
        • SOA Timing Data
        • Effects of Frontal Damage
      • Summary and Discussion
    • Dynamic Categorization/Sorting Tasks
      • The Dynamic Categorization Task
      • Basic Properties of the Model
      • Exploring the Model
        • Training
        • Intradimensional Shift
        • Intradimensional Reversal
        • Extradimensional Shift
      • Summary and Discussion
    • General Role of Frontal Cortex in Higher-Level Cognition
      • Functions Commonly Attributed to Frontal Cortex
        • Inhibition
        • Flexibility
        • Fluency
        • Executive Control
        • Monitoring/Evaluation
        • Summary
      • Other Models and Theoretical Frameworks
    • Interacting Specialized Systems and Cognitive Control
    • Summary
    • Further Reading
  • Conclusions
    • Overview
    • Fundamentals
    • General Challenges for Computational Modeling
      • Models Are Too Simple
        • Details of Neurobiology
        • Missing Brain Areas
        • Scaling
      • Models Are Too Complex
      • Models Can Do Anything
      • Models Are Reductionistic
      • Modeling Lacks Cumulative Research
    • Specific Challenges
      • Analytical Treatments of Learning
      • Error Signals
      • Regularities and Generalization
      • Capturing Higher-Level Cognition
    • Contributions of Computation to Cognitive Neuroscience
      • Models Help Us to Understand Phenomena
      • Models Deal with Complexity
      • Models Are Explicit
      • Models Allow Control
      • Models Provide a Unified Framework
    • Exploring on Your Own
• Simulator Details
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