Analog VLSI Neural Networks

This book brings together in one place important contributions and state-of-the-art research in the rapidly advancing area of analog VLSI neural networks. The book serves as an excellent reference, providing insights into some of the most important issues in analog VLSI neural networks research efforts.

List of contents

Guest Editorial.- Analog Computational Models of Concept Formation.- An Analog BiCMOS Hopfield Neuron.- Full Analog CMOS Integration of Very Large Time Constants for Synaptic Transfer in Neural Networks.- A Hierarchical Clustering Network Based on a Model of Olfactory Processing.- CMOS Analog/Digital Circuits of the Hysteresis McCulloch-Pitts Neuron for Ramsey Numbers.- Competitive Learning in Asynchronous-Pulse-Density Integrated Circuits.- A Programmable Analog CMOS Synapse for Neural Networks.- Two-Stage Neural Network Architecture for Feedback Control of Dynamic Systems.- Temporal Signal Processing with High-Speed Hybrid Analog-Digital Neural Networks.- A Super Parallel Sorter Using a Binary Neural Network with AND-OR Synaptic Connections.

Product details

• Assisted by: Yoshiyasu Takefuji (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Released: 01.01.1992
• EAN: 9780792392736
• ISBN: 978-0-7923-9273-6
• No. of pages: 131
• Weight: 463 g
• Illustrations: 3 SW-Abb., IV, 131 p.
• Series: The Springer International Series in Engineering and Computer Science; The Springer International Series in Engineering and Computer Science
• Subject: Natural sciences, medicine, IT, technology > Technology > Electronics, electrical engineering, communications engineering