Backpropagation-decorrelation: online recurrent learning with O(N) complexity

@article{Steil2004BackpropagationdecorrelationOR,
  title={Backpropagation-decorrelation: online recurrent learning with O(N) complexity},
  author={J. J. Steil},
  journal={2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541)},
  year={2004},
  volume={2},
  pages={843-848 vol.2}
}
We introduce a new learning rule for fully recurrent neural networks which we call backpropagation-decorrelation rule (BPDC). It combines important principles: one-step backpropagation of errors and the usage of temporal memory in the network dynamics by means of decorrelation of activations. The BPDC rule is derived and theoretically justified from regarding learning as a constraint optimization problem and applies uniformly in discrete and continuous time. It is very easy to implement, and… CONTINUE READING
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