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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 | |