
Pattern Recognition by Self-Organising Neural Networks
By: Gail A. Carpenter (Editor), Stephen Grossberg (Editor)
Hardcover | 14 June 1991
Sorry, we are not able to source the book you are looking for right now.
We did a search for other books with a similar title, however there were no matches. You can try selecting from a similar category, click on the author's name, or use the search box above to find your book.
Pattern Recognition by Self-Organizing Neural Networks presents the most recent advances in an area of research that is becoming vitally important in the fields of cognitive science, neuroscience, artificial intelligence, and neural networks in general. The 19 articles take up developments in competitive learning and computational maps, adaptive resonance theory, and specialized architectures and biological connections.Introductory survey articles provide a framework for understanding the many models involved in various approaches to studying neural networks. These are followed in Part 2 by articles that form the foundation for models of competitive learning and computational mapping, and recent articles by Kohonen, applying them to problems in speech recognition, and by Hecht-Nielsen, applying them to problems in designing adaptive lookup tables. Articles in Part 3 focus on adaptive resonance theory (ART) networks, selforganizing pattern recognition systems whose top-down template feedback signals guarantee their stable learning in response to arbitrary sequences of input patterns. In Part 4, articles describe embedding ART modules into larger architectures and provide experimental evidence from neurophysiology, event-related potentials, and psychology that support the prediction that ART mechanisms exist in the brain.Gail A. Carpenter is Professor of Mathematics and Cognitive and Neural Systems at Boston University, where Stephen Grossberg is Wang Professor of Cognitive and Neural Systems and Director of the Center for Adaptive Systems. Together they direct the university's Cognitive and Neural Systems Program.Contributors: J.-P. Banquet, G. A. Carpenter, S. Grossberg, R. Hecht-Nielsen, T. Kohonen, B. Kosko, T. W. Ryan, N. A. Schmajuk, W. Singer, D. Stork, C. von der Malsburg, C. L. Winter.
List Of Authors | |
Editorial Preface | |
Neural Network Models for Pattern Recognition and Associative Memory | |
Preface | |
Neural Network Models for Pattern Recognition and Associative Memory | |
Abstract | |
Introduction | |
The McCulloch-Pitts Neuron | |
Adaptive Filter Formalism | |
Logical Calculus and Invariant Patterns | |
Perceptrons and Back-Coupled Error Correction | |
Adaline and Madaline | |
Multi-Level Perceptrons: Early Back Propagation | |
Later Back Propagation | |
Hebbian Learning | |
The Learning Matrix | |
Linear Associative Memory (LAM) | |
Real-Time Models and Embedding Fields | |
Instars and Outstars | |
Additive and Shunting Activation Equations | |
Learning Equations | |
Learning Space-Time Patterns: The Avalanche | |
Adaptive Coding and Category Formation | |
Shunting Competitive Networks | |
Competitive Learning | |
Computational Maps | |
Instability of Computational Maps | |
Adaptive Resonance Theory (ART) | |
ART for Associative Memory | |
Cognitron and Neocognitron | |
Simulated Annealing | |
Conclusion | |
Bibliography | |
References | |
Nonlinear Neural Networks: Principles, Mechanisms, and Architectures | |
Preface | |
Reference | |
Nonlinear Neural Networks: Principles, Mechanisms, and Architectures | |
Abstract | |
Introduction | |
Interdisciplinary Studies during the Nineteenth Century: Helmholtz, Maxwell, and Mach | |
The Schism between Physics and Psychology | |
The Nonlinear, Nonlocal, and Nonstationary Phenomena of Mind and Brain | |
Color Theory | |
Top-Down Learning, Expectation, and Matching | |
The Nature of an Enduring Synthesis | |
Sources of Neural Network Research: Binary, Linear, Continuous-Nonlinear | |
Binary | |
Linear | |
Continuous-Nonlinear | |
Nonlinear Feedback between Fast Distributed STM Processing and Slow Associative LTM Processing | |
Principles, Mechanisms, and Architectures | |
Content-Addressable Memory Storage: A General STM Model and Liapunov Method | |
Other Liapunov Methods | |
Testing the Global Consistency of Decisions in Competitive Systems | |
Stable Production Strategies for a Competitive Market | |
Sensitive Variable-Load Parallel Processing by Shunting Cooperative-Competitive Networks: Automa... | |
Physiological Interpretation of Shunting Mechanisms as a Membrane Equation | |
Sigmoid Feedback, Contrast Enhancement, and Short Term Memory Storage by Shunting Feedback Netwo... | |
Competitive Learning Models | |
Stable Self-Organization of Pattern Recognition Codes | |
Internally Regulated Learning and Performance in Neural Models of Sensory-Motor Control: Adaptiv... | |
External Error Signals for Learning Adaptive Movement Gains: Push-Pull Opponent Processing | |
Match-Invariants: Internally Regulated Learning of an Invariant Self-Regulating Target Position ... | |
Presynaptic Competition for Long Term Memory: Self-Regulating Competitive Learning | |
References | |
Neural Pattern Discrimination | |
Preface | |
References | |
Neural Pattern Discrimination | |
Abstract | |
Introduction | |
Connection with Learning Theory | |
Local Temporal Discrimination | |
Choices between Incompatible Behaviors: "Majority Rule" in Non-Recurrent "Interference Patterns" | |
Local Temporal Generalization: Variable Velocities of Motor Performance | |
Why are Sensory Pathways in Different Modalities Anatomically Different if Universal Discriminato... | |
Unselective Filtering of Spatial Patterns by Excitatory Networks | |
Two Stages of Non-Recurrent Inhibition for Pattern Discrimination | |
Specific versus Nonspecific Inhibitory Interneurons, Inhibition at the Axon Hillock, Presynaptic ... | |
Pattern Normalization in Type I Networks | |
High-Band Filters | |
Discrimination of Space-Time Patterns | |
Velocity and Orientation Detectors | |
Alternative Mechanisms of Pattern Normalization: Saturating Potentials in an On-Off Field, or Lo... | |
Proof of Proposition 1 | |
Proof of Lemma 2 | |
Proof of Theorem 1 | |
Proof of Lemma 3 | |
Proof of Proposition 3 | |
Proof of Corollary 1 | |
Proof of Proposition 4 | |
References | |
Neural Expectation: Cerebellar and Retinal Analogs of Cells Fired by Learnable or Unlearned Pattern Classes | |
Preface | |
References | |
Neural Expectation: Cerebellar And Retinal Analogs of Cells Fired by Learnable or Unlearned Pattern ... | |
Abstract | |
Introduction | |
Theoretical Review | |
Retinal Analog of R Cells | |
A Learnable Preset Mechanism: Subtractive Case | |
Cerebellar Analogs of U Cells | |
A Learnable Present Mechanism: Multiplicative Case | |
References | |
Self-Organization of Orientation Sensitive Cells in the Striate Cortex | |
Preface | |
Self-Organization of Orientation Sensitive Cells In The Striate Cortex | |
Abstract | |
Introduction | |
The Model | |
The Elements | |
The Wiring of the Model | |
The Afferent Organization | |
The Learning Principle | |
The Function of the Model | |
Basic Equations of Evolution | |
Specification of Details | |
The Procedure of the Numerical Calculations | |
The Results without Learning | |
The Results after a Learning Phase | |
The Effect of Non-Standard Stimuli | |
The Sensitivity to Nonspecific Input | |
Redundancy of Information Storage | |
Discussion | |
Structure of the Model and Generalizations | |
Comparison with Experiments | |
References | |
Adaptive Pattern Classification and Universal Recoding, I: Parallel Development and Coding of Neural Feature Detectors | |
Preface | |
References | |
Adaptive Pattern Classification And Universal Recoding, I: Parallel Development And Coding Of Neural... | |
Abstract | |
Introduction | |
The Tuning Process | |
Ritualistic Pattern Classification | |
Shunts versus Additive Interactions as Mechanisms of Pattern Classification | |
What Do Retinal Amacrine Cells Do? | |
Arousal as a Tuning Mechanism | |
Arousal as a Search Mechanism | |
Development of an STM Code | |
Appendix | |
References | |
The "Neural" Phonetic Typewriter by | |
Preface | |
Reference | |
The ''Neural" Phonetic Typewriter | |
Why is speech recognition difficult? | |
The promise of neural computers | |
Acoustic preprocessing | |
Vector quantization | |
The neural network | |
Shortcut learning algorithm | |
Phonotopic maps | |
Postprocessing in symbolic form | |
Hardware implementations and performance | |
References | |
Counterpropagation Networks | |
Preface | |
References | |
Counterpropagation Networks | |
Abstract | |
Introduction | |
Counterpropagation Network | |
CPN Error Analysis | |
CPN Variants and Evolutes | |
Discussion | |
References | |
Bibliography | |
Adaptive Pattern Classification and Universal Recoding, Ii: Feedback, Expectation, Olfaction, and Illusions | |
Preface | |
References | |
Adaptive Pattern Classification And Universal Recoding, Ii: Feedback, Expectation, Olfaction, Illusi... | |
Abstract | |
Introduction | |
Adaptive Resonance: Stable Coding and Reset of STM | |
Adaptive Resonance in Reinforcement, Motivation, and Attention | |
Search and Lock Mechanism | |
Olfactory Coding and Learned Expectation | |
Modulation of Nonspecific Arousal by a Learned Expectation Mechanism | |
Universal Recoding | |
Search | |
Slow Noradrenergic Transmitter Accumulation-Depletion as a Search Mechanism | |
Spatial Frequency Adaptation | |
Afterimages | |
Conclusion | |
References | |
A Massively Parallel Architecture for A Self-Organizing Neural Pattern Recognition Machine | |
Preface | |
References | |
A Massively Parallel Architecture for A Self-Organizing Neural Pattern Recognition Machine | |
Abstract | |
Introduction: Self-Organization of Neural Recognition Codes | |
Self-Scaling Computational Units, Self-Adjusting Memory Search, Direct Access, and Attentional Vi... | |
Bottom-Up Adaptive Filtering and Contrast-Enhancement in Short Term Memory | |
Top-Down Template Matching and Stabilization of Code Learning | |
Interactions between Attentional and Orienting Subsystems: STM Reset and Search | |
Attentional Gain Control and Attentional Priming | |
Matching: The 2/3 Rule | |
Code Instability and Code Stability | |
Using Context to Distinguish Signal from Noise in Patterns of Variable Complexity | |
Vigilance Level Tunes Categorical Coarseness: Disconfirming Feedback | |
Rapid Classification of an Arbitrary Type Font | |
Network Equations: Interactions between Short Term Memory and Long Term Memory Patterns | |
Direct Access to Subset and Superset Patterns | |
Weber Law Rule and Associative Decay Rule for Bottom-Up LTM Traces | |
Template Learning Rule and Associative Decay Rule for Top-Down LTM Traces | |
Direct Access to Nodes Coding Perfectly Learned Patterns | |
Initial Strengths of LTM Traces | |
Summary of the Model | |
Order of Search and Stable Choices in Short-Term Memory | |
Stable Category Learning | |
Critical Feature Patterns and Prototypes | |
Direct Access after Learning Self-Stabilizes | |
Order of Search: Mathematical Analysis | |
Order of Search: Computer Simulations | |
Biasing the Network towards Uncommitted Nodes | |
Computer Simulation of Self-Scaling Computational Units: Weighing the Evidence | |
Concluding Remarks: Self-Stabilization and Unitization within Associative Networks | |
Appendix | |
References | |
Variations on Adaptive Resonance | |
Preface | |
Variations on Adaptive Resonance | |
Background | |
Adaptive Thresholding | |
Learning with Iterative Short-Time Pattern Presentations | |
Continuous ARC Operation | |
Conclusions | |
References | |
ART 2: Self-Organization of Stable Category Recognition Codes for Analog Input Patterns | |
Preface | |
Reference | |
Art 2: Self-Organization of Stable Category Recognition Codes for Analog Input Patterns | |
Abstract | |
Adaptive Resonance Architectures | |
ART 1: Binary Input Patterns | |
ART 2: Analog Input Patterns | |
ART 2 Design Principles | |
ART 2 STM Equations: F1 | |
ART 2 STM Equations: F2 | |
ART 2 LTM Equations | |
ART 2 Reset Equations: The Orienting Subsystem | |
The Match-Reset Tradeoff: Choice of Top-down Initial LTM Values | |
Learning Increases Mismatch Sensitivity and Confirms Category Choice | |
Choosing a New Category: Bottom-up LTM Initial Values | |
The Stability-Plasticity Tradeoff | |
Alternative ART 2 Architectures | |
References | |
Adaptive Bidirectional Associative Memories | |
Preface | |
Adaptive Bidirectional Associative Memories | |
Abstract | |
Introduction: Storing Data Pairs in Associative Memory Matrices | |
Discrete Bidirectional Associative Memory (BAM) Stability | |
BAM Correlation Encoding | |
Continuous BAMs | |
Adaptive BAMs | |
References | |
ART 3: Hierarchical Search Using Chemical Transmitters in Self-Organizing Pattern Recognition Architectures | |
Preface | |
References | |
Art 3: Hierarchical Search Using Chemical Transmitters in Self-Organizing Pattern Recognition Archit... | |
Abstract | |
Introduction: Distributed Search of ART Network Hierarchies | |
An ART Search Cycle | |
ART 2: Three-Layer Competitive Fields | |
ART Bidirectional Hierarchies and Homology of Fields | |
ART Cascade | |
Search in an ART Hierarchy | |
A New Role for Chemical Transmitters in ART Search | |
Equations for Transmitter Production, Release, and Inactivation | |
Alternative ART 3 Systems | |
Transmitter Release Rate | |
System Dynamics at Input Onset: An Approximately Linear Filter | |
System Dynamics after Intrafield Feedback: Amplification of Transmitter Release by Postsynaptic ... | |
System Dynamics during Reset: Inactivation of Bound Transmitter Channels | |
Parametric Robustness of the Search Process | |
Summary of System Dynamics during a Mismatch-Reset Cycle | |
Automatic STM Reset by Real-Time Input Sequences | |
Reinforcement Feedback | |
Notation for Hierarchies | |
Trade-Off between Weight Size and Pattern Match | |
ART 3 Simulations: Mismatch Reset and Input Reset of STM Choices | |
Search Time Invariance at Different Vigilance Values | |
Reinforcement Reset | |
Input Hysteresis Simulation | |
Distributed Code Simulation | |
Alternative ART 3 Model Simulation | |
Simulation Equations | |
Conclusion | |
References | |
Artmap: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-organizing Neural Network | |
Preface | |
References | |
Artmap: Supervised Real-Time Learning and Classification of Nonstatationary Data by a Self-Organizin... | |
Abstract | |
Introduction | |
The ARTMAP system | |
ARTMAP simulations: Distinguishing edible and poisonous mushrooms | |
ART modules ARTa and ARTb | |
The Map Field | |
References | |
Appendix | |
Simulation Algorithms | |
Neuronal Activity as A Shaping factor in The Self Organization of Neuron Assemblies | |
Preface | |
References | |
Neuronal Activity As A Shaping Factor in The Self-Organization of Neuron Assemblies | |
Abstract | |
Introduction | |
A Teleological Argument | |
Evidence for a Central Control of Local Hebbian Modifications | |
The Gating Mechanism | |
Functional Implications of Developmental Plasticity | |
References | |
Probing Cognitive Processes Through The Structure of Event-Related Potentials: An Experimental and Theoretical Analysis | |
Preface | |
Reference | |
Probing Cognitive Processes Through The Structure of Event-Related Potentials During Learning: An Ex... | |
Abstract | |
Introduction | |
Attentional Subsystem and Orienting Subsystem | |
Bottom-Up Adaptive Filtering and Contrast-Enhancement in Short Term Memory | |
Top-Down Template Matching and Stabilization of Code Learning | |
STM Reset and Search | |
Attentional Gain Control and Attentional Priming | |
Matching via the 2/3 Rule | |
ERP Components | |
Experimental Paradigm | |
Experimental Results: ERP Profiles | |
Comparison of ERP Profiles with Adaptive Resonance Theory Mechanisms | |
Conclusion: The Relationship of Learning to ERPs | |
References | |
Unitization, Automaticity, Temploral Order, and Word Recongnition | |
Preface | |
Unitization, Automaticity, Temporal Order, and Word Recognition | |
Abstract | |
The Word Length Effect | |
Unitization and Psychological Progress | |
The Temporal Chunking Problem | |
All Letters are Sublists | |
Expectancy Learning and Priming | |
The McClelland and Rumelhart Model | |
The Schneider and Shiffrin Model | |
Parallel Processing and Unlimited Capacity | |
The Functional Unit of Cognitive Processing: Not Spreading Activation | |
Capacity versus Matching | |
Adaptive Filter: The Processing Bridge between Sublist Masking and Temporal Order Information ov... | |
The LTM Invariance Principle: Temporal Order Information without a Serial Buffer | |
Spatial Frequency Analysis of Temporal Order Information | |
Conclusion | |
References | |
Speech Perception and Production by a Self-Organizing Neural Network | |
Preface | |
Speech Perception and Production By A Self-Organizing Neural Network | |
Abstract | |
The Learning of Language Units | |
Low Stages of Processing: Circular Reactions and the Emerging Auditory and Motor Codes | |
The Vector Integration to Endpoint Model | |
Self-Stabilization of Imitation via Motor-to-Auditory Priming | |
Higher Stages of Processing: Context-Sensitive Chunking and Unitization of the Emerging Auditory ... | |
Masking Fields | |
References | |
Neural Dynamics of Adaptive Timing and Temporal Discrimination During Associative Learning | |
Preface | |
References | |
Neural Dynamics of Adaptive Timing and Temporal Discrimination During Associative Learning | |
Abstract | |
Introduction: Timing the Expected Delay of a Goal Object in a Spatially Distributed and Nonstatio... | |
Timing the Balance between Exploration for Novel Rewards and Consummation of Expected Rewards | |
Distinguishing Expected Nonoccurrences from Unexpected Nonoccurrences: Inhibiting the Negative Co... | |
Spectral Timing Model | |
Spectral Timing Model: An Application of Gated Dipole Theory | |
Spectral Timing Equations | |
The Activation Spectrum | |
The Habituation Spectrum | |
The Gated Signal Spectrum | |
Temporally Selective Associative Learning | |
The Doubly Gated Signal Spectrum | |
The Output Signal | |
Effect of Increasing ISI and US Intensity | |
Comparison with Nictitating Membrane Conditioning Data | |
Inverted U in Learning as a Function of ISI | |
Multiple Timing Peaks | |
Effect of Increasing US Duration | |
Effect of Increasing CS Intensity | |
Timed Gating of Read-Out From the Orienting Subsystem | |
Locating the Timing Circuit within a Self-Organizing Sensory-Cogni-tive and Cognitive-Reinforcem... | |
Cognitive-Reinforcement Circuit | |
The Gated Dipole Opponent Process | |
Adaptive Timing as Spectral Conditioned Reinforcer Learning | |
Timed Inhibition of the Orienting Subsystem by Drive Representations | |
Timed Activation of the Hippocampus and the Contingent Negative Variation | |
Effect of CS Intensity on Timed Motor Behavior | |
Spatial Coding of Stimulus Intensity by a PTS Shift Map | |
Effect of Drugs on Timed Motor Behavior | |
Concluding Remarks: Timing Paradox and Multiple Types of Timing Circuits | |
References | |
Author Index | |
Subject Index | |
Table of Contents provided by Publisher. All Rights Reserved. |
ISBN: 9780262031769
ISBN-10: 0262031760
Series: Bradford Books
Published: 14th June 1991
Format: Hardcover
Language: English
Number of Pages: 710
Audience: Professional and Scholarly
For Ages: 22+ years old
Publisher: AMITY PUB CO
Country of Publication: US
Dimensions (cm): 26.31 x 18.8 x 4.6
Weight (kg): 1.66
Shipping
Standard Shipping | Express Shipping | |
---|---|---|
Metro postcodes: | $9.99 | $14.95 |
Regional postcodes: | $9.99 | $14.95 |
Rural postcodes: | $9.99 | $14.95 |
How to return your order
At Booktopia, we offer hassle-free returns in accordance with our returns policy. If you wish to return an item, please get in touch with Booktopia Customer Care.
Additional postage charges may be applicable.
Defective items
If there is a problem with any of the items received for your order then the Booktopia Customer Care team is ready to assist you.
For more info please visit our Help Centre.
You Can Find This Book In
This product is categorised by
- Non-FictionScienceBiology, Life SciencesLife Sciences in GeneralNeurosciences
- Non-FictionComputing & I.T.Computer ScienceImage Processing
- Non-FictionComputing & I.T.Computer ScienceArtificial IntelligenceNeural Networks & Fuzzy Systems
- Non-FictionComputing & I.T.Computer ScienceArtificial IntelligencePattern Recognition
- Non-FictionComputing & I.T.Computer Networking & Communications
- Non-FictionPsychologyCognition & Cognitive Psychology