A neural network-based associative memory for storing complex-valued patterns

A neural network-based associative memory for storing complex patterns is proposed. Two variations of the model are proposed: 1) a discrete model, and 2) a continuous model. The latter approaches the former as a limit. A crude capacity estimate for the discrete model is made. Network weights can be calculated in one step using a complex outer-product rule or can be adjusted adaptively using a Hebbian learning rule. Possible biological significance of the complex neuron state is briefly discussed.<
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