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Delays in Neural Networks

In this letter we consider the effect of random transmission delays on the dynamics of a fully connected neural network. We show that, if these delays are also present during a learning stage in which patterns are presented in succession, the network will be capable of regenerating this sequence of patterns. This capability does not depend on the actual distribution of the delays. For the condition where the pattern sequence has a nonzero correlation time we present an equation with which to compute the time-dependent overlap of the system state with the sequence.