Neuromorphic Systems Engineering
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Neuromorphic Systems Engineering : Neural Networks in Silicon

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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: large scale analog systems in silicon
neuromorphic silicon
auditory (ear) and vision (eye) systems in silicon
learning and adaptation in silicon
merging biology and technology
micropower analog circuit design
analog memory
analog interchipcommunication on digital buses GBP/LISTGBP
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.
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Product details

  • Hardback | 462 pages
  • 155 x 235 x 26.92mm | 1,880g
  • Dordrecht, Netherlands
  • English
  • 1998 ed.
  • XVII, 462 p.
  • 0792381580
  • 9780792381587

Table of contents

Foreword. Preface. I: Cochlear Systems. 1. Filter Cascades as Analogs of the Cochlea; R.F. Lyon. 2. An Analogue VLSI Model of Active Cochlea; E. Fragniere, et al. 3. A Low-Power Wide-Dynamic-Range Analog VLSI Cochlea; R. Sarpeshkar, et al. 4. Speech Recognition Experiments with Silicon Auditory Models; J. Lazzaro, J. Wawrzynek. II: Retinomorphic Systems. 5. The Retinomorphic Approach: Pixel-Parallel Adaptive Amplification, Filtering, and Quantization; K.A. Boahlen. 6. Analog VLSI Excitatory Feedback Circuits for Attentional Shifts and Tracking; T.G. Morris, S.P. DeWeerth. 7. Floating-Gate Circuits for Adaptation of Saccadic Eye Movement Accuracy; T.K. Horiuchi, C. Koch. III: Neuromorphic Communication. 8. Introduction to Neuromorphic Communication; T.S. Lande. 9. A Pulsed Communication/Computation Framework for Analog VLSI Perceptive Systems; A. Mortara. 10. Asynchronous Communication of 2D Motion Information Using Winner-Takes-All Arbitration; Z. Kalayjian, A.G. Andreou. 11. Communicating Neuronal Ensembles between Neuromorphic Chips; K.A. Boahen. IV: Neuromorphic Technology. 12. Introduction: From Neurobiology to Silicon; C. Diorio. 13. A Low-Power Wide-Linear-Range Transconductance Amplifier; R. Sarpeshkar, et al. 14. Floating-Gate MOS Synapse Transistors; C. Diorio, et al. 15. Neuromorphic Synapses for Artificial Dendrites; W.C. Westerman, et al. 16. Winner-Take-All Networks with Lateral Excitation; G. Indiveri. V: Neuromorphic Learning. 17. Neuromorphic Learning VLSI Systems: ASurvey; G. Cauwenberghs. 18. Analog VLSI Stochastic Perturbative Learning Architectures; G. Cauwenberghs. 19. Winner-Takes-All Associative Memory; P.O. Pouliquen, et al. Index.
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