Ebook: Neuromorphic Systems Engineering: Neural Networks in Silicon
- Tags: Circuits and Systems, Electronic and Computer Engineering, Complexity, Computer Science general
- Series: The Springer International Series in Engineering and Computer Science 447
- Year: 1998
- Publisher: Springer US
- Edition: 1
- Language: English
- pdf
Neuromorphic Systems Engineering: Neural Networks in Silicon emphasizes three important aspects of this exciting new research field. The term neuromorphic expresses relations to computational models found in biological neural systems, which are used as inspiration for building large electronic systems in silicon. By adequate engineering, these silicon systems are made useful to mankind.
Neuromorphic Systems Engineering: Neural Networks in Silicon provides the reader with a snapshot of neuromorphic engineering today. It is organized into five parts viewing state-of-the-art developments within neuromorphic engineering from different perspectives.
Neuromorphic Systems Engineering: Neural Networks in Silicon provides the first collection of neuromorphic systems descriptions with firm foundations in silicon. Topics presented include:
Neuromorphic Systems Engineering: Neural Networks in Silicon serves as an excellent resource for scientists, researchers and engineers in this emerging field, and may also be used as a text for advanced courses on the subject.
Neuromorphic Systems Engineering: Neural Networks in Silicon emphasizes three important aspects of this exciting new research field. The term neuromorphic expresses relations to computational models found in biological neural systems, which are used as inspiration for building large electronic systems in silicon. By adequate engineering, these silicon systems are made useful to mankind.
Neuromorphic Systems Engineering: Neural Networks in Silicon provides the reader with a snapshot of neuromorphic engineering today. It is organized into five parts viewing state-of-the-art developments within neuromorphic engineering from different perspectives.
Neuromorphic Systems Engineering: Neural Networks in Silicon provides the first collection of neuromorphic systems descriptions with firm foundations in silicon. Topics presented include:
Neuromorphic Systems Engineering: Neural Networks in Silicon serves as an excellent resource for scientists, researchers and engineers in this emerging field, and may also be used as a text for advanced courses on the subject.
Neuromorphic Systems Engineering: Neural Networks in Silicon emphasizes three important aspects of this exciting new research field. The term neuromorphic expresses relations to computational models found in biological neural systems, which are used as inspiration for building large electronic systems in silicon. By adequate engineering, these silicon systems are made useful to mankind.
Neuromorphic Systems Engineering: Neural Networks in Silicon provides the reader with a snapshot of neuromorphic engineering today. It is organized into five parts viewing state-of-the-art developments within neuromorphic engineering from different perspectives.
Neuromorphic Systems Engineering: Neural Networks in Silicon provides the first collection of neuromorphic systems descriptions with firm foundations in silicon. Topics presented include:
Neuromorphic Systems Engineering: Neural Networks in Silicon serves as an excellent resource for scientists, researchers and engineers in this emerging field, and may also be used as a text for advanced courses on the subject.
Content:
Front Matter....Pages i-xvii
Front Matter....Pages 1-1
Filter Cascades as Analogs of the Cochlea....Pages 3-18
An Analogue VLSI Model of Active Cochlea....Pages 19-47
A Low-Power Wide-Dynamic-Range Analog VLSI Cochlea....Pages 49-103
Speech Recognition Experiments with Silicon Auditory Models....Pages 105-126
Front Matter....Pages 127-127
The Retinomorphic Approach: Pixel-Parallel Adaptive Amplification, Filtering, and Quantization....Pages 129-150
Analog VLSI Excitatory Feedback Circuits for Attentional Shifts and Tracking....Pages 151-174
Floating-Gate Circuits for Adaptation of Saccadic Eye Movement Accuracy....Pages 175-189
Front Matter....Pages 191-191
Introduction to Neuromorphic Communication....Pages 193-200
A Pulsed Communication/Computation Framework for Analog VLSI Perceptive Systems....Pages 201-215
Asynchronous Communication of 2D Motion Information Using Winner-Takes-All Arbitration....Pages 217-227
Communicating Neuronal Ensembles between Neuromorphic Chips....Pages 229-259
Front Matter....Pages 261-261
Introduction: From Neurobiology to Silicon....Pages 263-266
A Low-Power Wide-Linear-Range Transconductance Amplifier....Pages 267-313
Floating-Gate MOS Synapse Transistors....Pages 315-337
Neuromorphic Synapses for Artificial Dendrites....Pages 339-365
Winner-Take-All Networks with Lateral Excitation....Pages 367-377
Front Matter....Pages 379-379
Neuromorphic Learning VLSI Systems: A Survey....Pages 381-408
Analog VLSI Stochastic Perturbative Learning Architectures....Pages 409-435
Winner-Takes-All Associative Memory: A Hamming Distance Vector Quantizer....Pages 437-456
Back Matter....Pages 457-462
Neuromorphic Systems Engineering: Neural Networks in Silicon emphasizes three important aspects of this exciting new research field. The term neuromorphic expresses relations to computational models found in biological neural systems, which are used as inspiration for building large electronic systems in silicon. By adequate engineering, these silicon systems are made useful to mankind.
Neuromorphic Systems Engineering: Neural Networks in Silicon provides the reader with a snapshot of neuromorphic engineering today. It is organized into five parts viewing state-of-the-art developments within neuromorphic engineering from different perspectives.
Neuromorphic Systems Engineering: Neural Networks in Silicon provides the first collection of neuromorphic systems descriptions with firm foundations in silicon. Topics presented include:
Neuromorphic Systems Engineering: Neural Networks in Silicon serves as an excellent resource for scientists, researchers and engineers in this emerging field, and may also be used as a text for advanced courses on the subject.
Content:
Front Matter....Pages i-xvii
Front Matter....Pages 1-1
Filter Cascades as Analogs of the Cochlea....Pages 3-18
An Analogue VLSI Model of Active Cochlea....Pages 19-47
A Low-Power Wide-Dynamic-Range Analog VLSI Cochlea....Pages 49-103
Speech Recognition Experiments with Silicon Auditory Models....Pages 105-126
Front Matter....Pages 127-127
The Retinomorphic Approach: Pixel-Parallel Adaptive Amplification, Filtering, and Quantization....Pages 129-150
Analog VLSI Excitatory Feedback Circuits for Attentional Shifts and Tracking....Pages 151-174
Floating-Gate Circuits for Adaptation of Saccadic Eye Movement Accuracy....Pages 175-189
Front Matter....Pages 191-191
Introduction to Neuromorphic Communication....Pages 193-200
A Pulsed Communication/Computation Framework for Analog VLSI Perceptive Systems....Pages 201-215
Asynchronous Communication of 2D Motion Information Using Winner-Takes-All Arbitration....Pages 217-227
Communicating Neuronal Ensembles between Neuromorphic Chips....Pages 229-259
Front Matter....Pages 261-261
Introduction: From Neurobiology to Silicon....Pages 263-266
A Low-Power Wide-Linear-Range Transconductance Amplifier....Pages 267-313
Floating-Gate MOS Synapse Transistors....Pages 315-337
Neuromorphic Synapses for Artificial Dendrites....Pages 339-365
Winner-Take-All Networks with Lateral Excitation....Pages 367-377
Front Matter....Pages 379-379
Neuromorphic Learning VLSI Systems: A Survey....Pages 381-408
Analog VLSI Stochastic Perturbative Learning Architectures....Pages 409-435
Winner-Takes-All Associative Memory: A Hamming Distance Vector Quantizer....Pages 437-456
Back Matter....Pages 457-462
....
Download the book Neuromorphic Systems Engineering: Neural Networks in Silicon for free or read online
Continue reading on any device:
Last viewed books
Related books
{related-news}
Comments (0)