CHAPTER III Neural Networks as Associative Memory

Associative memories can be implemented either by using feedforward or recurrent neural networks. Such associative neural networks are used to associate one set of vectors with another set of vectors, say input and output patterns. The aim of an associative memory is, to produce the associated output pattern whenever one of the input pattern is applied to the neural network. The input pattern may be applied to the network either as input or as initial state, and the output pattern is observed at the outputs of some neurons constituting the network. According to the way that the network handles errors at the input pattern, they are classified as interpolative and accretive memory. In the interpolative memory it is allowed to have some deviation from the desired output pattern when added some noise to the related input pattern. However, in accretive memory, it is desired the output to be exactly the same as the associated output pattern, even if the input pattern is noisy. Another classification of associative memory is such that while the memory in which the associated input and output patterns differ are called heteroassociative memory, it is called autoassociative memory if they are the same.