Learning on silicon : adaptive VLSI neural systems
著者
書誌事項
Learning on silicon : adaptive VLSI neural systems
(The Kluwer international series in engineering and computer science, SECS 512)
Kluwer Academic, c1999
大学図書館所蔵 全8件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
- 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. Hafliger, 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-Verdu, 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.
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