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| (Chapter Headings) Preface | |
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| Contributors | |
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| Pulse-Coupled Neural Networks | |
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| A Neural Network Model for Optical Flow Computation | |
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| Temporal Pattern Matching Using an Artificial Neural Network | |
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| Patterns of Dynamic Activity and Timing in Neural Network Processing | |
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| A Macroscopic Model of Oscillation in Ensembles of Inhibitory and Excitatory Neurons | |
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| Finite State Machines and Recurrent Neural Networks | |
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| Automata and Dynamical Systems Approaches | |
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| Biased Random-Walk Learning: A Neurobiological Correlate to Trial-and-Error | |
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| Using SONNET 1 to Segment Continuous Sequences of Items | |
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| On the Use of High Level Petri Nets in the Modeling of Biological Neural Networks | |
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| Locally Recurrent Networks: The Gamma Operator, Properties, and Extensions | |
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| Preface | |
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| Contributors | |
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| Pulse-Coupled Neural Networks | |
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| Introduction | |
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| Basic Model | |
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| Multiple Pulses | |
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| Multiple Receptive Field Inputs | |
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| Time Evolution of Two Cells | |
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| Space to Time | |
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| LinkingWaves and Time Scales | |
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| Groups | |
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| Invariances | |
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| Segmentation | |
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| Adaptation | |
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| Time to Space | |
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| Implementations | |
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| Integration into Systems | |
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| Concluding Remarks | |
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| References | |
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| A Neural Network Model for Optical Flow Computation: Introduction | |
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| Theoretical Background | |
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| Discussion on the Reformulation | |
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| Choosing Regularization Parameters | |
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| A Recurrent Neural Network Model | |
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| Experiments | |
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| Comparison to Other Work | |
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| Summary and Discussion | |
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| References | |
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| TemporalPattern Matching Using an Artificial Neural Network: Introduction | |
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| Solving Optimization Problems Using the Hopfield Network | |
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| Dynamic Time Warping Using Hopfield Network | |
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| Computer Simulation Results | |
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| Conclusions | |
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| References | |
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| Patterns of Dynamic Activity and Timing in Neural Network Processing: Introduction | |
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| Dynamic Networks | |
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| Chaotic Attractors and Attractor Locking | |
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| Developing Multiple Attractors | |
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| Attractor Basins and Dynamic Binary Networks | |
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| Time Delay Mechanisms and Attractor Training | |
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| Timing of Action Potentials in Impulse Trains | |
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| Discussion | |
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| Acknowledgments | |
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| References | |
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| A Macroscopic Model of Oscillation in Ensembles of Inhibitory and Excitatory Neurons: Introduction | |
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| A Macroscopic Model for Cell Assemblies | |
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| Interactions Between Two Neural Groups | |
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| Stability of Equilibrium States | |
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| Oscillation Frequency Estimation | |
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| Experimental Validation | |
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| Conclusion | |
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| Appendix | |
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| References | |
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| Finite State Machines and Recurrent Neural Networks | |
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| Automata and Dynamical Systems Approaches: Introduction | |
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| State Machines | |
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| Dynamical Systems | |
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| Recurrent Neural Network | |
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| RNN as a State Machine | |
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| RNN as a Collection of Dynamical Systems | |
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| RNN with Two State Neurons | |
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| Experiments--Learning Loops of FSM. Discussion | |
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| References | |
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| Biased Random-Walk Learning: A Neurobiological Correlate to Trial-and-Error: Introduction | |
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| Hebb''s Rule | |
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| Theoretical Learning Rules | |
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| Biological Evidence | |
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| Conclusions | |
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| Acknowledgments | |
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| References and Bibliography | |
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| Using SONNET 1 to Segment Continuous Sequences of Items | |
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| Introduction | |
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| Learning Isolated and Embedded Spatial Patterns | |
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| Storing Items with Decreasing Activity | |
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| The LTM Invariance Principle | |
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| Using Rehearsal to Process Arbitrarily Long Lists | |
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| Implementing the LTM Invariance Principle with an On-Center Off-Surround Circuit | |
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| Resetting Items Once They can be Classified | |
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| Properties of a Classifying System | |
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| Simulations | |
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| Discussion | |
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| On the Use of High Level Petri Nets in the Modeling of Biological Neural Networks | |
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| Introduction | |
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| Fundamentals of PNs M | |