Complex-valued multistate neural associative memory

1996/01/01 by S. Jankowski, A. Lozowski, Jacek M. Żurada +1 · 4 citations
Computer Science · Neuroscience · #Neural Networks and Applications #Neural dynamics and brain function #Neural Networks Stability and Synchronization

paper · doi:10.1109/72.548176

Abstract

A model of a multivalued associative memory is presented. This memory has the form of a fully connected attractor neural network composed of multistate complex-valued neurons. Such a network is able to perform the task of storing and recalling gray-scale images. It is also shown that the complex-valued fully connected neural network may be considered as a generalization of a Hopfield network containing real-valued neurons. A computational energy function is introduced and evaluated in order to prove network stability for asynchronous dynamics. Storage capacity as related to the number of accessible neuron states is also estimated.

Cited by

• Multi-Level Error-Resilient Neural Networks with Learning
• Novel Complex-Valued Hopfield Neural Networks with Phase and Magnitude Quantization
• A Survey of Complex-Valued Neural Networks
• Global Exponential Stability for Complex-Valued Recurrent Neural Networks With Asynchronous Time Delays

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