A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Analog implementation of shunting neural networks
Advances in neural information processing systems 1
Sensory Neural Networks: Lateral Inhibition
Sensory Neural Networks: Lateral Inhibition
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Electronic implementation of a class of neural networks whose short-term memory equation is governed by multiplicative, rather than additive, inhibition is proposed. The network models can be derived from ionic flow in nerve membranes and multiplicative terms result from control of conductive paths by voltages of other cells in the network. Since Field Effect Transistors (FETs) are voltage controlled conductances when operated below pinch-off, these networks can be readily implemented in FET technology using this physical property. This class of neural networks appears in many areas of the brain as well as the sensory system and has been used as a basic building block for the multilayer self-organizing architecture of Adaptive Resonance Theory (ART). The model has been especially useful for explaining a wide range of peripheral visual phenomena. The implementation is intended to specifically demonstrate desirable front-end image processing properties of contrast enhancement, edge detection, dynamic range compression, and adaptation of dynamics to mean intensity levels. Since the network can be mathematically described, its dynamics and stability may be examined. Compatibility of the network with higher level processing allows for its inclusion in multilayer self-organizing neural network architectures.