Thèse Sciences Cognitives - Olivier Nerot

Recommendations

Info

Mémorisation par forçage des dynamiques chaotiques dans les modèles connexionnistes récurrents [145] O. Nerrand, P. Roussel-Ragot, L. Personnaz, G. Dreyfus. S. Marcos. Neural networks and nonlinear adaptative filtering. Unifying concepts and new algorithms. Neural Computation. 5. p165-199. (1993) [146] A. Newell, H.A. Simon. Human problem solving. Englewood Cliffs, NJ. Prentice-Hall. (1972) [147] Hidetoshi Nishimori, Tota Nakamura, Masatoshi Shiino. Retrieval of spatio-temporal sequence in asynchronous neural network. Physical Review A. Vol. 41. No. 6. p3346-3354.(1990) [148] S. Olafsson. On the stability of neural networks with arbitrary weights. Neural computing and applications. 4:2-9. (1996) [149] Christian W. Omlin, C.Lee Giles. Extraction of rules from discrete time recurrent neural networks. [150] A. Pelah, H.B Barlow. Visual illusion from running. Nature. Vol 381. may 1996, p.283. (1996) [151] Alexander Parlos. Kil T. Chong. Amir F. Atyia. Application of the recurrent multilayer perceptron in modeling complex process dynamics. IEEE TNN. Vol. 5. No.2.. p255-266. (1994) [152] Barak A. Pearlmutter. Dynamic Recurrent Neural Networks. Technical Report. CMU-CS-90-196. Carnegie Mellon University. Pittsburgh, PA 15213 (1990) [153] Barak A. Pearlmutter. Gradient Calculations for dynamic recurrent neural networks : a survey. IEEE transactions on Neural Networks. Vol.6. No.5. (1995) [154] C. Perky. An experimental study of imagination. Amer. J. Psychol. 21. p.422-452. (1910) [155] Rolf Pfeifer (pfeifer@ifi.unizh.ch),, christian Scheier. From perception to action : the right direction ? PerAc '94 conference. IEEE. (1994) [156] Stephen W. Piché. Steepest descent algorithms for neural network controllers and filters.IEEE TNN. Vol.5. No.2. p198-212 (1994) [157] Pinaki Poddar, K. P. Unnikrishnan. Memory neuron networks : A prolegomenon. General motors research laboratories. CMR-7493. 21 octobre. (1991) [158] Jordan B. Polllack. Implications of recursive distributed representations. [159] Eric O. Postma, Ernst H. Wolf, H. Jaap van den Herik. The nature of memory representation. Proceedings of the workshop on supercomputers in brain research. (1995) [160] Gintaras V. Puskorius, Lee A. Feldkamp. Neurocontrol of nonlinear dynamical systems with kalman filter trained recurrent networks. IEEE TNN. Vol.5. No.2. p279-297. (1994) [161] Mathias Quoy. Apprentissage dans les réseaux neuromimétiques à dynamiquechaotique.<strong>Thèse</strong>. Groupement d'Intelligence Artificielle. Centre d'études et de recherches de Toulouse (1994) [162] M.I. Rabinovich, R. Huerta, H.D.I Arbanel, A.I. Selverson. A minimal model for chaotic bursting of the LP neuron in lobster. Submitted to Proc. Natl. Acad. Sci. (1996) [163] Rander, K.P. Unnikrishnan. Learning the time-delay characteristics in a neural network. IEEE international conference on acoustics, speech and signal processing. Vol. II. pp285-288. (1992) [164] Steve Renals. Chaos in neural networks. Eurasip Workshop, 90-99. (1990) [165] L. Rodet, G. Tiberghien. Towards a dynamic model of associative semantic memory.Journal of biological systems. Vol. 2. No. 3. p401-441. (1994) [166] Eric Ronco, Peter Gawthrop. Modular neural networks : a state of the art. Technicalre-port CSC- 95026. (1995) [167] Bertrand Russel. Histoire de mes idées philosophiques. Coll. tel Gallimard. [168] Norihiro Sadato, Alvaro Pascual Leone, Jordan Grafman, Vicente Ibanez, Marie-Pierre Delber, Geaorge Dold, Mark Hellett. Activation of the primary visual cortex by Braille reading in blind subjects. Nature. Vol. 380. (1996). 226 ANNEXE
Mémorisation par forçage des dynamiques chaotiques dans les modèles connexionnistes récurrents [169] Toshimichi Saito. Chaos and fractals from forced artificial neural cell. IEEE TNN. Vol.4.No.1. (1993) [170] Sastry, G. Santharam, K. P. Unnikrishnan. Memory neuron networks for identification and control of dynamical systems. NAO Research and DEvelopment Center. GMR-7916. 9 mars (1993). [171] Steven J. Schiff, Kristin Jerger, Duc H. Duong, Taeun Chang, Mark L. Spano, William L. Ditto. controlling chaos in the brain. Nature. Vol 370. pp615-620. (1994) [172] Jürgen Schmidhuber (yirgan@cs.colorado.edu) . Learning Factorial codes by predictability minimization. Technical Report. TR CU-CS-565-91. (1991) [173] Jürgen Schmidhuber (yirgan@cs.colorado.edu). A Fixed size storage O(n3) time complexity learning algorithm for fully recurrent continually running networks. Neural computation. 4. p243-248. (1992) [174] Jürgen Schmidhuber (yirgan@cs.colorado.edu). Learning complex, extended sequences using the principle of history compression. Neural computation. 4. p234-242. (1992) [175] Erwin Schrödinger. L’esprit et la matière. Coll. Seuil. [176] S. Segal, V. Fusella. Influence of imaged pictures and sounds of visual and auditory signals. J. Esp. Psychol. 83. p458-464. (1970) [177] Ralph M. Siegel, Heather L. Read. Models of the temporal dynamics of visual processing.Journal of statistical physics. Vol. 70. No 1&2. (1993) [178] Hava T. Siegelmann (iehava@ie.technion.ac.il), Bill G. Horne, C. Lee Giles. Computational capabilities of recurrent NARX neural networks. Technical Report. UMIACS-TR-95-12. University of Maryland. (1995) [179] Hava T. Siegelmann (iehava@ie.technion.ac.il). Computation beyond the Turing Limit. Science. Vol. 268. 28 april 1995. pp 545-548. (1995) [180] Christine A. Skarda, Walter J. Freeman (wfreeman@garnet.berkeley.edu). How brains make chaos in order to make sense of the world. Behavioral and Brain <strong>Sciences</strong>. 10. pp 161-195. (1987) [181] Paul So, Edward Ott, W.P. Dayawansa. Observing chaos : deducing and tracking the sate of a chaotic system from limited observation. Physical review E. Vol.49. No.4. p2650-2864.(1994) [182] Sompolinsky, I. Kanter. Temporal association in asymmetric neural networks.Physical Review E. Vol.57. No.22. p2861-2864. (1986) [183] Piet Spiessens (piet@arti.vub.ac.be), Jan Torreele(jant@arti.vub.ac.be). Massively parallel evolution of recurrent networks : an approach to temporal processing. [184] Srinivasan, U.R. Prasad, N.J. Rao. Back Propagation through adjoints for the identification of nonlinear dynamic systems using recurrent neural models. IEEE TNN. Vol.5. No.2. (1994) [185] Michael Stiber. José P. Segundo. Learning in neural models with complex dynamics. [186] Guo-Zheng Sun, Hsing-Hen Chen, Yee-Chun Lee. A Fast on-line learning algorithm for recurrent neural networks.0-7803-0164-1/91. IEEE TNN. Vol.2. p13-18. (1991) [187] J.G Taylor. Neural network capacity for temporal sequence storage. International journal of Neural Systems. Vol. 2, Nos 1&2. pp 47-54 (1991) [188] James Theiler. On the evidence for low-dimensional chaos in an epileptic electroencephalogram. Physica Letters A. 196. p335-341. (1995) [189] James Theiler, P.E. Rapp. Re-examination of the evidence for low-dimensional, nonlinear strcuture in the human electroencephalogram.preprint. 1995. Bibliographie Générale 227
Page 1:
THESE présentée en vue d’obteni
Page 5 and 6:
Mémorisation par forçage neuronal
Page 7 and 8:
Mémorisation par forçage des dyna
Page 9 and 10:
Page 11 and 12:
Page 13 and 14:
Page 15 and 16:
Page 17 and 18:
Page 19 and 20:
Page 21:
Page 24 and 25:
Page 26 and 27:
26 Mémorisation par forçage des d
Page 28 and 29:
Page 30 and 31:
Page 32 and 33:
Page 34 and 35:
Page 36 and 37:
Page 38 and 39:
Page 40 and 41:
Page 42 and 43:
Page 44 and 45:
Page 46 and 47:
Page 48 and 49:
Page 50 and 51:
Page 52 and 53:
Page 54 and 55:
Page 56 and 57:
Page 58 and 59:
Page 60 and 61:
Page 62 and 63:
Page 65 and 66:
Page 67 and 68:
Page 69 and 70:
Page 71 and 72:
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
Page 81 and 82:
Page 83 and 84:
Page 85 and 86:
Page 87 and 88:
Page 89 and 90:
Page 91 and 92:
Page 93 and 94:
Page 95 and 96:
Page 97 and 98:
Page 99 and 100:
Page 101 and 102:
Page 103 and 104:
Page 105 and 106:
Page 107 and 108:
Page 109 and 110:
Page 111 and 112:
Page 113 and 114:
Page 115 and 116:
Page 117 and 118:
Page 119 and 120:
Page 121 and 122:
Page 123 and 124:
Page 125 and 126:
Page 128 and 129:
128 Mémorisation par forçage des
Page 130 and 131:
Page 132 and 133:
Page 134 and 135:
Page 136 and 137:
Page 138 and 139:
Page 140 and 141:
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
Page 149 and 150:
Page 151 and 152:
Page 153 and 154:
Page 155 and 156:
Page 157 and 158:
Page 159 and 160:
Page 161 and 162:
Page 163 and 164:
Page 165 and 166:
Page 167 and 168:
Page 169 and 170:
Page 171 and 172:
Page 173 and 174:
Page 175 and 176: Mémorisation par forçage des dyna
Page 205: Mémorisation par forçage des dyna
Page 208 and 209: 208 Mémorisation par forçage des
show all

Thèse Sciences Cognitives - Olivier Nerot

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?