Description 
1 online resource (xvi, 351 pages) : illustrations 
Contents 
(Chapter Headings) Preface. Contributors. J.L. Johnson, H. Ranganath, G. Kuntimad, and H.J. Caulfield, PulseCoupled Neural Networks. H. Li and J. Wang, A Neural Network Model for Optical Flow Computation. F. Unal and N. Tepedelenlioglu, Temporal Pattern Matching Using an Artificial Neural Network. J. Dayhoff, P. Palmadesso, F. Richards, and D.T. Lin, Patterns of Dynamic Activity and Timing in Neural Network Processing. J. Ghosh, H.J. Chang, and K. Liano, A Macroscopic Model of Oscillation in Ensembles of Inhibitory and Excitatory Neurons. P. Tito, B. Horne, C.L. Giles, and P. Collingwood, Finite State Machines and Recurrent Neural NetworksAutomata and Dynamical Systems Approaches. R. Anderson, Biased RandomWalk Learning: A Neurobiological Correlate to TrialandError. A. Nigrin, Using SONNET 1 to Segment Continuous Sequences of Items. K. Venkatesh, A. Pandya, and S. Hsu, On the Use of High Level Petri Nets in the Modeling of Biological Neural Networks. J. Principe, S. Celebi, B. de Vries, and J. Harris, Locally Recurrent Networks: The Gamma Operator, Properties, and Extensions. Preface. Contributors. J.L. Johnson, H. Ranganath, G. Kuntimad, and H.J. Caulfield, PulseCoupled Neural Networks: Introduction. Basic Model. Multiple Pulses. Multiple Receptive Field Inputs. Time Evolution of Two Cells. Space to Time. LinkingWaves and Time Scales. Groups. Invariances. Segmentation. Adaptation. Time to Space. Implementations. Integration into Systems. Concluding Remarks. References. H. Li and J. Wang, A Neural Network Model for Optical Flow Computation: Introduction. Theoretical Background. Discussion on the Reformulation. Choosing Regularization Parameters. A Recurrent Neural Network Model. Experiments. Comparison to Other Work. Summary and Discussion. References. F. Unal and N. Tepedelenlioglu, TemporalPattern Matching Using an Artificial Neural Network: Introduction. Solving Optimization Problems Using the Hopfield Network. Dynamic Time Warping Using Hopfield Network. Computer Simulation Results. Conclusions. References. J. Dayhoff, P. Palmadesso, F. Richards, and D.T. Lin, Patterns of Dynamic Activity and Timing in Neural Network Processing: Introduction. Dynamic Networks. Chaotic Attractors and Attractor Locking. Developing Multiple Attractors. Attractor Basins and Dynamic Binary Networks. Time Delay Mechanisms and Attractor Training. Timing of Action Potentials in Impulse Trains. Discussion. Acknowledgments. References. J. Ghosh, H.J. Chang, and K. Liano, A Macroscopic Model of Oscillation in Ensembles of Inhibitory and Excitatory Neurons: Introduction. A Macroscopic Model for Cell Assemblies. Interactions Between Two Neural Groups. Stability of Equilibrium States. Oscillation Frequency Estimation. Experimental Validation. Conclusion. Appendix. References. P. Tito, B. Horne, C.L. Giles, and P. Collingwood, Finite State Machines and Recurrent Neural NetworksAutomata and Dynamical Systems Approaches: Introduction. State Machines. Dynamical Systems. Recurrent Neural Network. RNN as a State Machine. RNN as a Collection of Dynamical Systems. RNN with Two State Neurons. ExperimentsLearning Loops of FSM. Discussion. References. R. Anderson, Biased RandomWalk Learning: A Neurobiological Correlate to TrialandError: Introduction. Hebb's Rule. Theoretical Learning Rules. Biological Evidence. Conclusions. Acknowledgments. References and Bibliography. A. Nigrin, Using SONNET 1 to Segment Continuous Sequences of Items: Introduction. Learning Isolated and Embedded Spatial Patterns. Storing Items with Decreasing Activity. The LTM Invariance Principle. Using Rehearsal to Process Arbitrarily Long Lists. Implementing the LTM Invariance Principle with an OnCenter OffSurround Circuit. Resetting Items Once They can be Classified. Properties of a Classifying System. Simulations. Discussion. K. Venkatesh, A. Pandya, and S. Hsu, On the Use of High Level Petri Nets in the Modeling of Biological Neural Networks: Introduction. Fundamentals of PNs. Modeling of Biological Neural Systems with High Level PNs. New/Modified Elements Added to HPNs to Model BNNs. Example of a BNN: The Olfactory Bulb. Conclusions. References. J. Principe, S. Celebi, B. de Vries, and J. Harris, Locally Recurrent Networks: The Gamma Operator, Properties, and Extensions: Introduction. Linear Finite Dimensional Memory Structures. The Gamma Neural Network. Applications of the Gamma Memory. Interpretations of the Gamma Memory. Laguerre and Gamma II Memories. Analog VLSI Implementations of the Gamma Filter. Conclusions. References 

Pulsecoupled neural networks / J.L. Johnson [and others]  A neural network model for optical flow computation / Hua Li ; Jun Wang  Temporal pattern matching using an artificial neural network / Fatih A. Unal ; Nazif Tepedelenlioglu  Patterns of dynamic activity and timing in neural network processing / Judith E. Dayhoff [and others]  A macroscopic model of oscillation in ensembles of inhibitory and excitatory neurons / Joydeep Ghosh ; HungJen Chang ; Kadir Liano  Finite state machines and recurrent neural networksautomata and dynamical systems approaches / Peter Tiňo [and others]  Biased randomwalk learning: a neurobiological correlate to trialanderror / Russell W. Anderson  Using SONNET 1 to segment continuous sequences of items / Albert Nigrin  On the use of highlevel petri nets in the modeling of biological neural networks / Kurapati Venkatesh ; Abhijit Pandya ; Sam Hsu  Locally recurrent networks: the gamma operator, properties, and extensions / Jose C. Principe [and others] 
Summary 
This book is one of the most uptodate and cuttingedge texts available on the rapidly growing application area of neural networks. Neural Networks and Pattern Recognition focuses on the use of neural networksin pattern recognition, a very important application area for neural networks technology. The contributors are widely known and highly respected researchers and practitioners in the field. Key Features * Features neural network architectures on the cutting edge of neural network research * Brings together highly innovative ideas on dynamical neural networks * Includes articles written by authors prominent in the neural networks research community * Provides an authoritative, technically correct presentation of each specific technical area 
Bibliography 
Includes bibliographical references and index 
Notes 
Print version record 
Subject 
Neural networks (Computer science)


Pattern recognition systems.

Form 
Electronic book

Author 
Dayhoff, Judith E.


Omidvar, Omid.

ISBN 
0080512615 (electronic bk.) 

0125264208 

128103343X 

9780080512617 (electronic bk.) 

9780125264204 

9781281033437 
