On the capacity of a Markov-chain encoded associative memory

The author proposes a Markov-chain encoded associative memory as a general autocorrelation associative memory which can embrace hitherto-proposed encodings such as the conventional (biased or unbiased Bernoulli trial) and sparse encodings if the chain´s parameters are chosen appropriately. The proposed associative memory is a network of n fully interconnected two-state formal neurons. One chooses m realizations (patterns) of a Markov chain at random (independently), and then stores them in the network by adjusting the network synaptic matrix according to the Hebbian rule. A general condition which must be satisfied by the number of the stored patterns m is derived. Then, the capacity of the network is given as the solution of an optimization problem