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Neural Networks for Perception
Computation, Learning, and Architectures
- 1st Edition - May 10, 2014
- Editor: Harry Wechsler
- Language: English
- Paperback ISBN:9 7 8 - 1 - 4 8 3 2 - 4 8 2 1 - 9
- eBook ISBN:9 7 8 - 1 - 4 8 3 2 - 6 2 7 9 - 6
Neural Networks for Perception, Volume 2: Computation, Learning, and Architectures explores the computational and adaptation problems related to the use of neuronal systems, and… Read more
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Request a sales quoteNeural Networks for Perception, Volume 2: Computation, Learning, and Architectures explores the computational and adaptation problems related to the use of neuronal systems, and the corresponding hardware architectures capable of implementing neural networks for perception and of coping with the complexity inherent in massively distributed computation. This book addresses both theoretical and practical issues related to the feasibility of both explaining human perception and implementing machine perception in terms of neural network models. The text is organized into two sections. The first section, computation and learning, discusses topics on learning visual behaviors, some of the elementary theory of the basic backpropagation neural network architecture, and computation and learning in the context of neural network capacity. The second section is on hardware architecture. The chapters included in this part of the book describe the architectures and possible applications of recent neurocomputing models. The Cohen-Grossberg model of associative memory, hybrid optical/digital architectures for neorocomputing, and electronic circuits for adaptive synapses are some of the subjects elucidated. Neuroscientists, computer scientists, engineers, and researchers in artificial intelligence will find the book useful.
Contents of Volume 1 : Human and Machine Perception
Contributors
Foreword
Part III Computation and Learning
III.Introduction
III.1 Learning Visual Behaviors
III.2 Nonparametric Regression Analysis Using Self-Organizing Topological Maps
III.3 Theory of the Backpropagation Neural Network
III.4 Hopfield Model and Optimization Problems
III.5 DAM, Regression Analysis, and Attentive Recognition
III.6 Intelligence Code Machine
III.7 Cycling Logarithmically Converging Networks That Flow Information to Behave (Perceive) and Learn
III.8 Computation and Learning in the Context of Neural Network Capacity
Part IV Architectures
IV.Introduction
IV.1 Competitive and Cooperative Multimode Dynamics in Photorefractive Ring Circuits
IV.2 Hybrid Neural Networks and Algorithms
IV.3 The Use of Fixed Holograms for Massively-Interconnected, Low-Power Neural Networks
IV.4 Electronic Circuits for Adaptive Synapses
IV.5 Neural Network Computations on a Fine Grain Array Processor
Index
- No. of pages: 384
- Language: English
- Edition: 1
- Published: May 10, 2014
- Imprint: Academic Press
- Paperback ISBN: 9781483248219
- eBook ISBN: 9781483262796
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