Limiting capability of cellular-neural associative memory

The problem of achieving Cellular-Neural Associative Memory (CNAM) limiting capability is considered. At first a CNAM learning method based on the idea of Perceptron Learning Rule which provides maximal ability to restore distorted patterns is suggested. Next, expressions for determining self-connection weight values which increase attractivity and decrease the number of oscillation states are obtained. Finally, influence of neuron threshold on basic characteristics of CNAM is investigated. It is shown that CNAM is capable to store more than 2q patterns where q is the cardinality of neuron neighborhood.