G. Cauwenberghs / Magdy A. Bayoumi (Hgg.)

Learning on Silicon

Adaptive VLSI Neural Systems
Herausgegeben:Cauwenberghs, G.; Bayoumi, Magdy A.

• Gebundenes Buch

Produktbeschreibung

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.

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Produktdetails

• The Springer International Series in Engineering and Computer Science 512
• Verlag: Springer / Springer US / Springer, Berlin
• Artikelnr. des Verlages: 978-0-7923-8555-4
• 1999.
• Seitenzahl: 444
• Erscheinungstermin: 30. Juni 1999
• Englisch
• Abmessung: 241mm x 160mm x 29mm
• Gewicht: 803g
• ISBN-13: 9780792385554
• ISBN-10: 0792385551
• Artikelnr.: 23075036

Inhaltsangabe

Preface. Acknowledgements. 1. Learning on Silicon: A Survey; G.
Cauwenberghs. Part I: Adaptive Sensory Processing. 2. Adaptive Circuits and
Synapses using pFET Floating-Gate Devices; P. Hasler, et al. 3. Silicon
Photoreceptors with Controllable Adaptive Filtering Properties; S.-C. Liu.
4. Analog VLSI System for Active Drag Reduction; V. Koosh, et al. Part II:
Neuromorphic Learning. 5. Biologically-inspired Learning in Pulsed Neural
Networks; T. Lehmann, R. Woodburn. 6. Spike Based Normalizing Hebbian
Learning in an Analog VLSI Artificial Neuron; P. Häfliger, M. Mahowald. 7.
Antidromic Spikes Drive Hebbian Learning in an Artificial Dendritic Tree;
W.C. Westerman, et al. Part III: Learning Architecture. 8. ART1 and ARTMAP
VLSI Circuit Implementation; T. Serrano-Gotarredona, B. Linares-Barranco.
9. Circuits for On-Chip Learning in Neuro-Fuzzy Controllers; F.
Vidal-Verdú, et al. 10. Analog VLSI Implementation of Self-learning Neural
Networks; T. Morie. 11. A 1.2 GFLOPS Neural Network Processor for
Large-Scale Neural Network Accelerator Systems; Y. Kondo, et al. Part IV:
Learning Dynamics. 12. Analog Hardware Implementation of Continuous-Time
Adaptive Filter Structures; J.G. Harris, et al. 13. A Chip for Temporal
Learning with Error Forward Propagation; F.M. Salam, H.-J. Oh. 14. Analog
VLSI On-Chip Learning Neural Network with Learning Rate Adaptation; G.M.
Bo, et al. Part V: Learning Systems. 15. Learning on CNN Universal Machine
Chips; R. Carmona, et al. 16. Analog VLSI Parallel Stochastic Optimization
for Adaptive Optics; R.T. Edwards, et al. 17. A Nonlinear Noise-Shaping
Delta-Sigma Modulator with On-Chip Reinforcement Learning; G. Cauwenberghs.
18. A Micropower Adaptive Linear Transform Vector Quantiser; R.J. Coggins,
et al. Index.

Rezensionen

` 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

` 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