Abstract
We present a continuous-time neural network model which consists of neurons with a continuous input-output relation. We use a computationally efficient discrete-time equivalent of this model to study its time-dependent properties. Detailed numerical results are presented for the behavior of the relaxation time to a target pattern as a function of the storage capacity of the network.
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