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Learning on Silicon : Adaptive VLSI Neural Systems - Gert Cauwenberghs

Learning on Silicon

Adaptive VLSI Neural Systems

By: Gert Cauwenberghs (Editor), Magdy A. Bayoumi (Editor)

Hardcover Published: 30th June 1999
ISBN: 9780792385554
Number Of Pages: 426

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Learning on Silicon combines models of adaptive information processing in the brain with advances in microelectronics technology and circuit design. The premise is to construct integrated systems not only loaded with sufficient computational power to handle demanding signal processing tasks in sensory perception and pattern recognition, but also capable of operating autonomously and robustly in unpredictable environments through mechanisms of adaptation and learning.
This edited volume covers the spectrum of Learning on Silicon in five parts: adaptive sensory systems, neuromorphic learning, learning architectures, learning dynamics, and learning systems. The 18 chapters are documented with examples of fabricated systems, experimental results from silicon, and integrated applications ranging from adaptive optics to biomedical instrumentation.
As the first comprehensive treatment on the subject, Learning on Silicon serves as a reference for beginners and experienced researchers alike. It provides excellent material for an advanced course, and a source of inspiration for continued research towards building intelligent adaptive machines.

` This is an excellent book which takes the reader from the physical basis of learning on silicon to algorithms and architectures. The contributed chapters are authoritatively written and the material is well organized, strongly recommended to anyone interested in neuromorphic engineering, adaptive hardware systems.' A.G. Andreou, Johns Hopkins University

Preface
Acknowledgements
Learning on Silicon: A Survey
Adaptive Sensory Processing
Adaptive Circuits and Synapses using pFET Floating-Gate Devices
Silicon Photoreceptors with Controllable Adaptive Filtering Properties
Analog VLSI System for Active Drag Reduction
Neuromorphic Learning
Biologically-inspired Learning in Pulsed Neural Networks
Spike Based Normalizing Hebbian Learning in an Analog VLSI Artificial Neuron
Antidromic Spikes Drive Hebbian Learning in an Artificial Dendritic Tree
Learning Architecture
ART1 and ARTMAP VLSI Circuit Implementation
Circuits for On-Chip Learning in Neuro-Fuzzy Controllers
Analog VLSI Implementation of Self-learning Neural Networks
A 1.2 GFLOPS Neural Network Processor for Large-Scale Neural Network Accelerator Systems
Learning Dynamics
Analog Hardware Implementation of Continuous-Time Adaptive Filter Structures
A Chip for Temporal Learning with Error Forward Propagation
Analog VLSI On-Chip Learning Neural Network with Learning Rate Adaptation
Learning Systems
Learning on CNN Universal Machine Chips
Analog VLSI Parallel Stochastic Optimization for Adaptive Optics
A Nonlinear Noise-Shaping Delta-Sigma Modulator with On-Chip Reinforcement Learning
A Micropower Adaptive Linear Transform Vector Quantiser
Index
Table of Contents provided by Publisher. All Rights Reserved.

ISBN: 9780792385554
ISBN-10: 0792385551
Series: The Springer International Series in Engineering and Computer Science
Audience: General
Format: Hardcover
Language: English
Number Of Pages: 426
Published: 30th June 1999
Publisher: Springer
Country of Publication: NL
Dimensions (cm): 23.5 x 15.5  x 3.18
Weight (kg): 1.77