The Resource Neural networks and pattern recognition, edited by Omid Omidvar, Judith Dayhoff
Neural networks and pattern recognition, edited by Omid Omidvar, Judith Dayhoff
 Language
 eng
 Extent
 1 online resource (xvi, 351 pages)
 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]
 Isbn
 9781281033437
 Label
 Neural networks and pattern recognition
 Title
 Neural networks and pattern recognition
 Statement of responsibility
 edited by Omid Omidvar, Judith Dayhoff
 Language
 eng
 Cataloging source
 DLC
 Illustrations
 illustrations
 Index
 index present
 LC call number
 QA76.87
 LC item number
 .O45 1998
 Literary form
 non fiction
 Nature of contents
 bibliography
 Label
 Neural networks and pattern recognition, edited by Omid Omidvar, Judith Dayhoff
 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, 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]
 http://library.link/vocab/cover_art
 https://contentcafe2.btol.com/ContentCafe/Jacket.aspx?Return=1&Type=S&Value=9781281033437&userID=ebscotest&password=ebscotest
 Dimensions
 unknown
 http://library.link/vocab/discovery_link
 {'f': 'http://opac.lib.rpi.edu/record=b4169692'}
 Extent
 1 online resource (xvi, 351 pages)
 Form of item
 online
 Isbn
 9781281033437
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Other physical details
 illustrations
 Specific material designation
 remote
Embed (Experimental)
Settings
Select options that apply then copy and paste the RDF/HTML data fragment to include in your application
Embed this data in a secure (HTTPS) page:
Layout options:
Include data citation:
<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.lib.rpi.edu/portal/Neuralnetworksandpatternrecognitionedited/3awDqYdQSmI/" typeof="WorkExample http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.lib.rpi.edu/portal/Neuralnetworksandpatternrecognitionedited/3awDqYdQSmI/">Neural networks and pattern recognition, edited by Omid Omidvar, Judith Dayhoff</a></span>  <span property="offers" typeOf="Offer"><span property="offeredBy" typeof="Library ll:Library" resource="http://link.lib.rpi.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.lib.rpi.edu/">Rensselaer Libraries</a></span></span></span></span></div>
Note: Adjust the width and height settings defined in the RDF/HTML code fragment to best match your requirements
Preview
Cite Data  Experimental
Data Citation of the Item Neural networks and pattern recognition, edited by Omid Omidvar, Judith Dayhoff
Copy and paste the following RDF/HTML data fragment to cite this resource
<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.lib.rpi.edu/portal/Neuralnetworksandpatternrecognitionedited/3awDqYdQSmI/" typeof="WorkExample http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.lib.rpi.edu/portal/Neuralnetworksandpatternrecognitionedited/3awDqYdQSmI/">Neural networks and pattern recognition, edited by Omid Omidvar, Judith Dayhoff</a></span>  <span property="offers" typeOf="Offer"><span property="offeredBy" typeof="Library ll:Library" resource="http://link.lib.rpi.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.lib.rpi.edu/">Rensselaer Libraries</a></span></span></span></span></div>