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A neural network for temporal sequential information

We report a neural network model that is capable of learning arbitrary input sequences quickly and online. It is also capable of completing a sequence upon cueing from any point in the sequence and in the presence of background noise. The architecture of the neural network utilizes sigmoid-pulse generating spiking neurons together with a Hebbian learning rule with synaptic noise.