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9780262571524

Gateway to Memory : An Introduction to Neural Network Modeling of the Hippocampus and Learning

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  • ISBN13:

    9780262571524

  • ISBN10:

    0262571528

  • Edition: 1st
  • Format: Paperback
  • Copyright: 2001-08-01
  • Publisher: MIT PRESS

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Summary

This book is for students and researchers who have a specific interest in learning and memory and want to understand how computational models can be integrated into experimental research on the hippocampus and learning. It emphasizes the function of brain structures as they give rise to behavior, rather than the molecular or neuronal details. It also emphasizes the process of modeling, rather than the mathematical details of the models themselves. The book is divided into two parts. The first part provides a tutorial introduction to topics in neuroscience, the psychology of learning and memory, and the theory of neural network models. The second part, the core of the book, reviews computational models of how the hippocampus cooperates with other brain structures--including the entorhinal cortex, basal forebrain, cerebellum, and primary sensory and motor cortices--to support learning and memory in both animals and humans. The book assumes no prior knowledge of computational modeling or mathematics. For those who wish to delve more deeply into the formal details of the models, there are optional "mathboxes" and appendices. The book also includes extensive references and suggestions for further readings.

Table of Contents

Preface xi
Acknowledgments xv
I Fundamentals
Introduction
3(8)
Computational Models as Tools
3(2)
Goals and Structure of This Book
5(6)
The Hippocampus in Learning and Memory
11(32)
Introduction
11(3)
Human Memory and the Medical Temporal Lobes
14(10)
Medial Temporal Lobe Damage and Memory Loss
14(3)
Anterograde Versus Retrograde Amnesia
17(4)
Preserved Learning in Amnesia
21(3)
Animal Learning Studies of Hippocampal Function
24(7)
Episodic Memory in Animals
26(2)
Spatial Navigation and the Hippocampus
28(3)
Importance of Well-Characterized Learning Behaviors
31(1)
Classical Conditioning and the Hippocampus
31(9)
Hippocampal Lesions and Simple Conditioning
34(2)
The Hippocampus and Complex Conditioning
36(4)
Is a Unified Theory of Hippocampal Function in Learning Possible?
40(3)
Summary
41(2)
Association in Neural Networks
43(38)
What is a Neural Network?
44(7)
Neurons and Information Processing in the Brain
44(3)
Information Processing in Neural Network Models
47(2)
Application of Network Models to Motor-Reflex Conditioning
49(2)
Neural Network Models of Learning
51(10)
The Widrow-Hoff Learning Rule
52(9)
Relationship to Animal Learning
61(14)
The Blocking Effect
62(3)
The Rescorla-Wagner Model of Conditioning
65(3)
Broad Implications of the Rescorla-Wagner Model
68(1)
Error-Correction Learning and the Brain
69(6)
Limitations of Error-Correction Learning
75(6)
Sensory Preconditioning
75(1)
Latent Inhibition
76(2)
Implications of the Limitations of Error-Correction Learning
78(1)
Summary
79(2)
Representation and Generalization in Neural Networks
81(30)
Representation and Generalization
82(4)
Generalization in One-Layer Networks
84(2)
Two Challenges for Generalization in Networks
86(1)
When Similar Stimuli Map to Similar Outcomes
86(8)
Application of Distributed Representations to Learning
87(3)
Stimulus Generalization and Distributed Representations in Multilayer Networks
90(1)
Integrating Distributed Representations with Error-Correction Learning
91(3)
The Limits of Similarity-Based Generalization
94(1)
When Similar Stimuli Map to Different Outcomes
94(17)
Multilayered Networks and Configuration
97(2)
Configuration and Combinatorial Explosion
99(3)
Learning New Representations in Multilayer Networks
102(3)
Computational Limitations of Backpropagation
105(4)
Psychological and Biological Validity of Backpropagation
109(1)
Summary
110(1)
Unsupervised Learning: Autoassociative Networks and the Hippocampus
111(34)
Autoassociative Networks
114(3)
Hippocampal Anatomy and Autoassociation
117(7)
Storage Versus Retrieval
120(2)
Capacity, Consolidation, and Catastrophic Interference
122(2)
Autoencoders: Autoassociation with Representation
124(15)
A New Interpretation of Autoassociators
124(4)
Autoencoders: Multilayer Autoassociators
128(1)
Predictive Autoencoders
129(10)
Interim Summary: Where Are We Now?
139(6)
II Modeling Memory
Cortico-Hippocampal Interaction in Associative Learning
145(44)
The Hippocampal Region and Adaptive Representations
146(19)
The Cortico-Hippocampal Model
148(3)
Representational Differentiation
151(6)
Representational Compression
157(3)
Limitations of the Cortico-Hippocampal Model
160(3)
Neurophysiological Support for the Cortico-Hippocampal Model
163(2)
Schmajuk and DiCarlo (S-D) Model
165(10)
Comparison with Gluck and Myers's Cortico-Hippocampal Model
169(6)
Relationship of Models to Qualitative Theories
175(6)
Stimulus Configuration
175(2)
Contextual Learning
177(1)
Stimulus Selection
177(1)
Intermediate-Term and Working Memory
178(2)
Cognitive Mapping
180(1)
``Flexible'' Memory
180(1)
Implications for Human Memory and Memory Disorders
181(8)
Summary
184(2)
Appendix 6.1 Simulation Details
186(3)
Cortico-Hippocampal Interaction and Contextual Processing
189(26)
Overview of Contextual Processing
189(4)
Computational Models
193(14)
Context as ``Just Another CS''
193(3)
Combining the Associative and Occasion-Setting Properties of Context
196(9)
Occasion-Setting Properties of Phasic Cues
205(2)
Relationship of Computational Models to Qualitative Theories
207(4)
Implications for Human Memory and Memory Disorders
211(4)
Summary
214(1)
Stimulus Representation in Cortex
215(44)
Cortical Representation and Plasticity
216(7)
Computational Models
223(27)
Competitive Learning and Topographic Maps
223(9)
Piriform Cortex
232(5)
Piriform-Hippocampal Interactions
237(4)
Integrating Piriform Cortex with the Cortico-Hippocampal Model
241(9)
Relationship of Computational Models to Qualitative Theories
250(4)
Implications for Human Memory and Memory Disorders
254(5)
Summary
256(1)
Appendix 8.1 Simulation Details
257(2)
Entorhinal Cortex
259(46)
Anatomy and Physiology of the Hippocampal Region
260(5)
Computational Models
265(26)
Entorhinal Cortex and Redundancy Compression
266(17)
Stimulus Competition in Entorhinal Cortex
283(3)
Backprojections from Entorhinal Cortex
286(5)
Relationship to Qualitative Theories: Stimulus Buffering and Configuration
291(3)
Implications for Human Memory and Memory Disorders
294(11)
Behavioral Measures of Hippocampal Atrophy
297(5)
Entorhinal Versus Hippocampal Atrophy in AD
302(1)
Summary
303(1)
Appendix 9.1 Simulation Details
303(2)
Cholinergic Modulation of Hippocampal-Region Function
305(40)
Acetylcholine as a Neuromodulator
306(5)
Neuromodulation
307(1)
Acetylcholine (ACh) and Memory Function
308(3)
Computational Models
311(23)
Acetylcholine in the Hippocampal Autoassociator
311(7)
Cholinergic Modulation of Cortico-Hippocampal Interaction
318(13)
Who Modulates the Modulator?
331(3)
Other Theories and Issues
334(4)
Septohippocampal GABAergic Projections
334(2)
Cholinergic Modulation in Cortex
336(2)
Implications for Human Memory and Memory Disorders
338(7)
Basal Forebrain Damage Following Cerebral Aneurysm
338(3)
Cholinergic Depletion in Alzheimer's Disease
341(1)
Summary
342(1)
Appendix 10.1 Simulation Details
342(3)
Emergent Themes
345(5)
Hippocampal Function Can Best Be Understood in Terms of How the Hippocampus Interacts and Cooperates with the Functioning of Other Brain Systems
345(1)
Partial Versus Complete Lesions May Differ in More Than Just Degree
345(2)
Disrupting a Brain System Has Different Effects Than Removing It
347(1)
Studies of the Simplest Forms of Animal Learning May Bootstrap Us Toward Understanding More Complex Aspects of Learning and Memory in Humans
347(1)
Keep It Simple. Keep It Useful. Keep It Testable.
348(2)
Keep It Simple
348(1)
Keep It Useful
349(1)
Keep It Testable
349(1)
Conclusion 350(1)
Glossary 351(22)
Notes 373(18)
References 391(32)
Author Index 423(16)
Subject Index 439

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