Analog VLSI implementation of adaptive synapses in pulsed neural networks

Authors:
Tim Kaulmann;Markus Ferber;Ulf Witkowski;Ulrich Rückert
Affiliations:
System and Circuit Technology, Heinz Nixdorf Institute, University of Paderborn, Paderborn, Germany;System and Circuit Technology, Heinz Nixdorf Institute, University of Paderborn, Paderborn, Germany;System and Circuit Technology, Heinz Nixdorf Institute, University of Paderborn, Paderborn, Germany;System and Circuit Technology, Heinz Nixdorf Institute, University of Paderborn, Paderborn, Germany
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

Citing 2
Cited 0

Mixed Mode VLSI Implementation of a Neural Associative Memory

Analog Integrated Circuits and Signal Processing
A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory

IEEE Transactions on Neural Networks

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Abstract

An analog VLSI implementation of adaptive synapses being part of an associative memory realised with pulsed neurons is presented. VLSI implementations of dynamic synapses and pulsed neurons are expected to provide robustness and low energy consumption like observed in the human brain. We have developed a VLSI implementation of synaptic connections for an associative memory which is used in a biological inspired image processing system using pulse coded neural networks. The system consists of different layers for feature extraction to decompose the image in several features. The pulsed associative memory is used for completing or binding features. In this paper, we focus on the dynamics and the analog implementation of adaptive synapses. The discussed circuits were designed in a 130 nm CMOS process.