Estimating the storage capacity of space-varying cellular neural networks

The capacity of associative memories based on space-varying, nonreciprocal, discrete-time cellular neural networks (CNNs) is investigated. The capacity is defined as the maximum number of random bipolar prototypes which can be stored with probability larger than a suitable threshold. It is shown that the capacity of the CNN is actually determined by the cells with the smallest number of connections, i.e. those located at the corners of the rectangular array. A simple empirical formula is presented which enables a prediction to be made as to the capacity of the CNN associative memory as a function of the neighbourhood radius and of the stability margin