Learning State Space Trajectories in Recurrent Neural Networks

@article{Pearlmutter1989LearningSS,
  title={Learning State Space Trajectories in Recurrent Neural Networks},
  author={Barak A. Pearlmutter},
  journal={Neural Computation},
  year={1989},
  volume={1},
  pages={263-269}
}
Many neural network learning procedures compute gradients of the errors on the output layer of units after they have settled to their final values. We describe a procedure for finding E/wij, where E is an error functional of the temporal trajectory of the states of a continuous recurrent network and wij are the weights of that network. Computing these quantities allows one to perform gradient descent in the weights to minimize E. Simulations in which networks are taught to move through limit… 
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