next up previous
Next: About this document ... Up: ratio Previous: ACKNOWLEDGEMENTS


A.M.H.J. Aertsen, G.L. Gerstein, M.K. Habib, and G. Palm.
Dynamics of neuronal firing correlation: Modulation of ``effective connectivity''.
Journal of Neurophysiology, 61:900-917, 1989.

A. J. Robinson and F. Fallside.
The utility driven dynamic error propagation network.
Technical Report CUED/F-INFENG/TR.1, Cambridge University Engineering Department, 1987.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams.
Learning internal representations by error propagation.
In D. E. Rumelhart and J. L. McClelland, editors, Parallel Distributed Processing, volume 1, pages 318-362. MIT Press, 1986.

J. Schmidhuber.
A fixed size storage $O(n^3)$ time complexity learning algorithm for fully recurrent continually running networks.
Neural Computation, 4(2):243-248, 1992.

J. Schmidhuber.
Learning to control fast-weight memories: An alternative to recurrent nets.
Neural Computation, 4(1):131-139, 1992.

C. v.d. Malsburg.
Technical Report 81-2, Abteilung für Neurobiologie, Max-Planck Institut für Biophysik und Chemie, Göttingen, 1981.

R. J. Williams.
Complexity of exact gradient computation algorithms for recurrent neural networks.
Technical Report Technical Report NU-CCS-89-27, Boston: Northeastern University, College of Computer Science, 1989.

R. J. Williams and D. Zipser.
A learning algorithm for continually running fully recurrent networks.
Neural Computation, 1(2):270-280, 1989.

Juergen Schmidhuber 2003-02-21

Back to Recurrent Neural Networks page