Thèse Sciences Cognitives - Olivier Nerot
Thèse Sciences Cognitives - Olivier Nerot
Thèse Sciences Cognitives - Olivier Nerot
Create successful ePaper yourself
Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.
Mémorisation par forçage des dynamiques chaotiques dans les modèles connexionnistes récurrents<br />
[[88]] D.O. Hebb. Essay on Mind. Lawrance-Erlbaum Assc., Hillsdale NJ. (1980)<br />
[[91]] Herz, B. Sulzer, R. Kühn, J.L. van Hemmen. Hebbian learning reconsidered : representation of<br />
static and dynamic objects in associative neural nets. Biol. Cyber. 60. p457-467.(1989)<br />
[[96]] J.J. Hopfield. Neural networks and physical systems with emergent collective computational<br />
abilities. Proceedings of the National Academy of <strong>Sciences</strong> 79:2554-2558 (1982)<br />
[[97]] J.J. Hopfield. Neurons with graded response have collective computationnal properties like those<br />
of two-state neurons. Proceedings of the National Academy of <strong>Sciences</strong> 81:3088-3092.(1984)<br />
[[98]] J.J. Hopfield. Pattern recognition computation using action potential timing for stimulus<br />
representation. Nature. Vol. 376. p33-36. (1995)<br />
[[102]] Lester Ingber (ingber@alumni.caltech.edu), P.L. Nunez. Statistical mechanics of neocortical<br />
interactions : high resolution path-integral calculation in short term memory. Physical Review E.<br />
Vol. 51, No.5. (1995)<br />
[[128]] M. W. Mak, Y.L. Lu, K.W. Ku. Improved real time recurrent learning algorithms : a review and<br />
some new approaches. ISANN95.<br />
[[141]] J.P Nadal (nadal@physique.ens.fr), N. Parga. Duality between learning machines : a bridge<br />
between supervised and unsupervised learning. Neural Computation. 6. p491-508. (1994)[[153]]<br />
Barak A. Pearlmutter. Gradient Calculations for dynamic recurrent neural networks : a<br />
survey. IEEE transactions on Neural Networks. Vol.6. No.5. (1995)<br />
[[172]] Jürgen Schmidhuber (yirgan@cs.colorado.edu) . Learning Factorial codes by predictability<br />
minimization. Technical Report. TR CU-CS-565-91. (1991)<br />
[[173]] Jürgen Schmidhuber (yirgan@cs.colorado.edu). A Fixed size storage O(n3) time complexity<br />
learning algorithm for fully recurrent continually running networks. Neural computation. 4. p243-<br />
248. (1992)<br />
[[174]] Jürgen Schmidhuber (yirgan@cs.colorado.edu). Learning complex, extended sequences using the<br />
principle of history compression. Neural computation. 4. p234-242. (1992)<br />
[[182]] Sompolinsky, I. Kanter. Temporal association in asymmetric neural networks.Physical Review E.<br />
Vol.57. No.22. p2861-2864. (1986)<br />
[[184]] Srinivasan, U.R. Prasad, N.J. Rao. Back Propagation through adjoints for the identification of<br />
nonlinear dynamic systems using recurrent neural models. IEEE TNN. Vol.5. No.2. (1994)<br />
[[187]] J.G Taylor. Neural network capacity for temporal sequence storage. International journal of<br />
Neural Systems. Vol. 2, Nos 1&2. pp 47-54 (1991)<br />
[[193]] Nikzad Benny Toomarian, Jacob Barhen. Learning a trajectory using adjoint functions and<br />
teacher forcing. Neural Networks. 5. p 473-383. (1992)<br />
[[195]] Ah Chung Tsoi, Andrew D. Back. Locally recurrent globally feedforward networks : a critical<br />
review of architectures. IEEE TNN. Vol. 5. No.2. p 229-239. (1994)<br />
[[199]] Fu-Sheng Tsung (tsung@cs.ucsd.edu), Garrison W. Cottrell (gary@cs.ucsd.edu). Learning in<br />
recurrent finite difference networks. International Journal of Neural Systems. Vol. 6, No 3. p249-<br />
256. (1995)<br />
[[202]] P. Unnikrishnan(unni@neuro.cs.gmr.com), K. P. Venugopal. Alopex : a correlation-based<br />
learning algorithm for feedforward and recurrent neural networks. Neural Computation.Vol. 6,<br />
No. 3. may (1994)<br />
[[207]] Eric. A. Wan (wan@isl.stanford.edu). Time series prediction by using a connectionist network with<br />
internal delay lines. Dans Time Series prediction, Forecasting the future and understanding the<br />
past. A. Weigend, N. Gershenfeld, editors. SFI studies in the sciences of complexity. Vol. XVII.<br />
Addison-Wesley (1994)<br />
92<br />
PREMIERE PARTIE : ANALYSE