Introduction to Neural and Cognitive Modeling [3rd ed.]

Contents......Page 3
Structure of the Book......Page 5
Preface......Page 6
--- Foundations of NN Theory......Page 10
What Are Neural Networks?......Page 11
What Are Some Principles of Neural Network Theory?......Page 13
Methodological Considerations......Page 18
Digital Approaches......Page 21
Continuous and Random Net Approaches......Page 34
A few of Rosenblatt’s definitions......Page 40
Detailed Description: Perceptron to Discriminate Vertical versus Horizontal......Page 41
Exercises for Chapter 2......Page 44
Some Additional Sources......Page 46
Notes......Page 47
Physiological Bases for Learning......Page 48
Rules for Associative Learning......Page 52
Learning Rules Related to Changes in Node Activities......Page 62
Associative Learning of Patterns......Page 72
Spike Timing–Dependent Plasticity......Page 77
Equations for Networks in Chapter 3 Detailed Description: The Gated Dipole......Page 80
Exercises for Chapter 3......Page 91
Notes......Page 96
Contrast Enhancement, Competition, and Normalization......Page 97
Lateral Inhibition and Excitation between Sensory Representations......Page 103
Visual Pattern Recognition Models......Page 116
Uses of Lateral Inhibition in Higher Level Processing......Page 129
Equations for Networks in Chapter 4 Detailed Description: Shunting Lateral Inhibition with Faster-than-Linear Signal Function......Page 131
Exercises for Chapter 4......Page 139
Some Additional Sources......Page 143
Note......Page 144
--- Computational Cognitive Neuroscience......Page 145
The Emergence of Cognitive Neuroscience......Page 146
Cognitive Neuroscience of Conditioning and Reinforcement Learning......Page 147
Cognitive Neuroscience of Categorization......Page 154
Cognitive Neuroscience of Vision and Visual Attention......Page 155
Cognitive Neuroscience of Sequence Learning and  Performance......Page 161
Cognitive Neuroscience of Executive Function and Cognitive Control......Page 164
Cognitive Neuroscience of Decision-Making......Page 169
Some Additional Sources......Page 173
Early Network Models of Classical Conditioning......Page 174
Attention and Short-Term Memory in Conditioning  Models......Page 184
Computational Cognitive Neuroscience of  Conditioning......Page 193
Multiple Levels: Model-Free and Model-Based  Learning......Page 205
Equations for Networks in Chapter 6 Detailed Description: Sutton–Barto Equations......Page 211
Exercises for Chapter 6......Page 218
Some Additional Sources......Page 221
Notes......Page 222
7 Models of Coding, Categorization & Unsupervised Learning......Page 223
Interactions between Short-and Long-Term Memory in Code Development......Page 224
Self-Organization and Unsupervised Categorization  Models......Page 235
Translation and Scale Invariance......Page 245
Equations for Networks in Chapter 7 Detailed Description: Adaptive Resonance Theory......Page 247
Exercises for Chapter 7......Page 253
Notes......Page 256
8 Models of Supervised Pattern & Category Learning......Page 257
The Back Propagation Network and PDP Networks......Page 258
“Semantics without Categorization” in PDP Networks......Page 265
Deep Learning......Page 267
ARTMAP: A Family of Supervised Adaptive Resonance Networks......Page 269
Exemplars, Prototypes and Rules in Models of Category Learning......Page 272
Brain-State-in-a-Box Models......Page 274
Some Brain-Based Models: COVIS and SUSTAIN......Page 276
Equations for Networks in Chapter 8......Page 280
Exercises for Chapter 8......Page 288
Some Additional Sources......Page 291
Notes......Page 292
How Do We Model Complex Brain Functions?......Page 293
Models of Vision and Visual Attention......Page 294
Models of Sequence Learning and Performance......Page 299
Models of Executive Function and Cognitive Control......Page 310
Models of Decision-Making......Page 319
Models of Thinking and Problem Solving......Page 327
Equations for Networks in Chapter 9 Wisconsin Card Sorting Test Model......Page 334
Exercises for Chapter 9......Page 344
Some Additional Sources......Page 347
Notes......Page 349
Difference and Differential Equations......Page 350
Differential versus Difference Equations......Page 353
Outstar Equations: Network Interpretation and Numerical Implementation......Page 356
Vectors and Matrices......Page 362
The Chain Rule and Back Propagation......Page 367
Dynamical Systems: Steady States, Limit Cycles, and Chaos......Page 370
Integrate-and-Fire (AKA Leaky Integrator) Models......Page 372
Note......Page 373
Basic Facts of Neurobiology......Page 374
The Neuron......Page 375
Synapses, Transmitters, Messengers, and Modulators......Page 378
Invertebrate and Vertebrate Nervous Systems......Page 381
Functions of Vertebrate Subcortical Regions......Page 382
Functions of the Mammalian Cerebral Cortex......Page 385
Refs......Page 388
Index......Page 445