The Resource Neural networks and pattern recognition, edited by Omid Omidvar, Judith Dayhoff, (electronic resource/)

Neural networks and pattern recognition, edited by Omid Omidvar, Judith Dayhoff, (electronic resource/)

Label
Neural networks and pattern recognition
Title
Neural networks and pattern recognition
Statement of responsibility
edited by Omid Omidvar, Judith Dayhoff
Contributor
Subject
Genre
Language
eng
Summary
This book is one of the most up-to-date and cutting-edge 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
Cataloging source
OPELS
Dewey number
006.3/2
Illustrations
illustrations
Index
index present
LC call number
QA76.87
LC item number
.O45 1998eb
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
Label
Neural networks and pattern recognition, edited by Omid Omidvar, Judith Dayhoff, (electronic resource/)
Link
http://library.quincycollege.edu:2048/login?url=http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=209290
Instantiates
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • (Chapter Headings) Preface. Contributors. J.L. Johnson, H. Ranganath, G. Kuntimad, and H.J. Caulfield, Pulse-Coupled 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 Networks--Automata and Dynamical Systems Approaches. R. Anderson, Biased Random-Walk Learning: A Neurobiological Correlate to Trial-and-Error. 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, Pulse-Coupled 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 Networks--Automata 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. Experiments--Learning Loops of FSM. Discussion. References. R. Anderson, Biased Random-Walk Learning: A Neurobiological Correlate to Trial-and-Error: Introduction. Hebbs 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 On-Center Off-Surround 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
  • Pulse-coupled 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 ; Hung-Jen Chang ; Kadir Liano -- Finite state machines and recurrent neural networks--automata and dynamical systems approaches / Peter Tino [and others] -- Biased random-walk learning: a neurobiological correlate to trial-and-error / Russell W. Anderson -- Using SONNET 1 to segment continuous sequences of items / Albert Nigrin -- On the use of high-level 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]
Control code
ocn162128691
Dimensions
unknown
Extent
1 online resource (access may be restricted)
Form of item
online
Governing access note
Access restricted to subscribing institution
Media category
computer
Media MARC source
rdamedia
Media type code
c
Note
eBooks on EBSCOhost
Specific material designation
remote
Stock number
77584:77584
Label
Neural networks and pattern recognition, edited by Omid Omidvar, Judith Dayhoff, (electronic resource/)
Link
http://library.quincycollege.edu:2048/login?url=http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=209290
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • (Chapter Headings) Preface. Contributors. J.L. Johnson, H. Ranganath, G. Kuntimad, and H.J. Caulfield, Pulse-Coupled 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 Networks--Automata and Dynamical Systems Approaches. R. Anderson, Biased Random-Walk Learning: A Neurobiological Correlate to Trial-and-Error. 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, Pulse-Coupled 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 Networks--Automata 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. Experiments--Learning Loops of FSM. Discussion. References. R. Anderson, Biased Random-Walk Learning: A Neurobiological Correlate to Trial-and-Error: Introduction. Hebbs 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 On-Center Off-Surround 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
  • Pulse-coupled 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 ; Hung-Jen Chang ; Kadir Liano -- Finite state machines and recurrent neural networks--automata and dynamical systems approaches / Peter Tino [and others] -- Biased random-walk learning: a neurobiological correlate to trial-and-error / Russell W. Anderson -- Using SONNET 1 to segment continuous sequences of items / Albert Nigrin -- On the use of high-level 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]
Control code
ocn162128691
Dimensions
unknown
Extent
1 online resource (access may be restricted)
Form of item
online
Governing access note
Access restricted to subscribing institution
Media category
computer
Media MARC source
rdamedia
Media type code
c
Note
eBooks on EBSCOhost
Specific material designation
remote
Stock number
77584:77584

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