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100301s1998 xxu| s |||| 0|eng d |
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|a 9780585280011
|9 978-0-585-28001-1
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|a 10.1007/b102308
|2 doi
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|a TK7888.4
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|a 621.3815
|2 23
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|a Neuromorphic Systems Engineering
|b Neural Networks in Silicon /
|c edited by Tor Sverre Lande.
|h [electronic resource] :
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|a Boston, MA :
|b Springer US,
|c 1998.
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| 300 |
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|a XVII, 462 p.
|b online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
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|a text file
|b PDF
|2 rda
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|a The Springer International Series in Engineering and Computer Science, Analog Circuits and Signal Processing,
|v 447
|x 0893-3405 ;
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| 505 |
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|a Cochlear Systems -- Filter Cascades as Analogs of the Cochlea -- An Analogue VLSI Model of Active Cochlea -- A Low-Power Wide-Dynamic-Range Analog VLSI Cochlea -- Speech Recognition Experiments with Silicon Auditory Models -- Retinomorphic Systems -- The Retinomorphic Approach: Pixel-Parallel Adaptive Amplification, Filtering, and Quantization -- Analog VLSI Excitatory Feedback Circuits for Attentional Shifts and Tracking -- Floating-Gate Circuits for Adaptation of Saccadic Eye Movement Accuracy -- Neuromorphic Communication -- to Neuromorphic Communication -- A Pulsed Communication/Computation Framework for Analog VLSI Perceptive Systems -- Asynchronous Communication of 2D Motion Information Using Winner-Takes-All Arbitration -- Communicating Neuronal Ensembles between Neuromorphic Chips -- Neuromorphic Technology -- Introduction: From Neurobiology to Silicon -- A Low-Power Wide-Linear-Range Transconductance Amplifier -- Floating-Gate MOS Synapse Transistors -- Neuromorphic Synapses for Artificial Dendrites -- Winner-Take-All Networks with Lateral Excitation -- Neuromorphic Learning -- Neuromorphic Learning VLSI Systems: A Survey -- Analog VLSI Stochastic Perturbative Learning Architectures -- Winner-Takes-All Associative Memory: A Hamming Distance Vector Quantizer.
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|a 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 £/LIST£ 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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|a Engineering.
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|a Computer science.
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|a Statistical physics.
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|a Dynamical systems.
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|a Electrical engineering.
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| 650 |
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|a Electronic circuits.
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|a Engineering.
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| 650 |
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|a Circuits and Systems.
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|a Electrical Engineering.
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|a Statistical Physics, Dynamical Systems and Complexity.
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| 650 |
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|a Computer Science, general.
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| 700 |
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|a Lande, Tor Sverre.
|e editor.
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|a SpringerLink (Online service)
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|t Springer eBooks
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| 776 |
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|i Printed edition:
|z 9780792381587
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| 830 |
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|a The Springer International Series in Engineering and Computer Science, Analog Circuits and Signal Processing,
|v 447
|x 0893-3405 ;
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| 856 |
4 |
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|u https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/b102308
|z View fulltext via EzAccess
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| 912 |
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|a ZDB-2-ENG
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| 912 |
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|a ZDB-2-BAE
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| 950 |
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|a Engineering (Springer-11647)
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