Neuromorphic Systems Engineering Neural Networks in Silicon

Neuromorphic Systems Engineering Neural Networks in Silicon / edited by Tor Sverre Lande.

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 syste...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Lande, Tor Sverre (Editor)
Format: eBook
Language:English
Published: New York, NY : Springer US : Imprint: Springer, 1998.
Edition:1st ed. 1998.
Series:The Springer International Series in Engineering and Computer Science, 447
Springer eBook Collection.
Subjects:
Electronic circuits.
Electrical engineering.
Statistical physics.
Dynamical systems.
Computer science.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 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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