Thèse Sciences Cognitives - Olivier Nerot

Recommendations

Info

Mémorisation par forçage des dynamiques chaotiques dans les modèles connexionnistes récurrents [103] Masayoshi Inoue, Seiichirou Fukushima. A neural network of chaotic oscillators. Prog. Theor. Phys. Vol.87. No.3. (1992) [104] Shin Ishi. Kenji Fukumizu. Sumio Watanabe. A network of chaotic elements for information processing. Neural Networks. Vol. 9. No 1.p25-40. (1996) [105] Kunihiko Ishiyama, Susumu Itoh, Toshio Utsunomiya, Kazuyuki Aihara. Analysis of chaotic neuron models with information Theory. Electronics and communications in Japan. Part 3. Vol. 76, No. 4, (1993) [106] Izhikevich, G. G. Malinetskii. A possible role of chaos in neurosystems. Sov. Phys.Dokl.37(10), octobre (1992) [107] Marc Jeannerod. Le cerveau-machie. Eds. Fayard. [108] Christel Jézéquel. Simulation d'un réseau de neurones stochastiques défini selon le modèle de Ising. Mise en évidence de transition de phase. Rapport de DEA. Laboratoire TIMC.(1995) [109] C. Jézéquel, O. Nérot, J. Demongeot. Dynamical Confinement in neural networks. A paraitre dans J. Biol. Phys. [110] W. Kintsch. Semantic memory : a tutorial. Dans R.S. Nickerson, Attention and performance VIII. p595-620. Hillsdale, NJ. Lawrence Erlbaum Associates Inc. [111] Kittel, J. parisi, R. Richter. Delayed feedback control of chaos in an electronic double-scroll oscillator. 4th annual meeting of ENGADYN. Grenoble (1993) [112] George K. Knopf, Madan M. Gupta. Dynamics of antagonistic neural processing elements.International journal of neural systems. Vol.4. No.3. p291-303. (1993) [113] Pascal Koiran (koiran@lip.ens-lyon.fr). On the relation between dynamical systems and boolean circuits. Technical Report 93-01. LIP. Ecole Normale Supérieure de Lyon. (1993) [114] Christof Koch (koch@iago.caltech.edu), Heinz Schuster. A simple network showing burst synchronization without frequency locking. [115] John F. Kolen (kolen-j@cis.ohio-state.edu). Fool's gold : extracting finite state machines from recurrent network dynamics. [116] John F. Kolen (kolen-j@cis.ohio-state.edu). Recurrent networks : state machines or iterated function systems [117] John F. Kolen (kolen-j@cis.ohio-state.edu), Jordan B. Pollack (pollack@cis.ohio-state.edu). The observers' paradox : apparent computational complexity in physical systems. [118] Peter König, Bernd Janosch, Thomas B. Schillen. Stimulus-dependent assembly formation of ascillatory responses: III. learning. Neural computation. 4, 666-681. (1992) [119] Stephen M.Kosslin, William L. Thompson, Irene J. Kim, Nathaniel M. Alpert. Topographical representations of mental images in primary visual cortex. Nature. Vol. 378. November. (1995) [120] Abderrahim Labbi. Sur l'approximation et les systèmes dynamiques dans les réseaux neuronaux. <strong>Thèse</strong> de mathématiques appliquées. INPG. Grenoble. (1993) [121] E. Labos. Chaos and neural networks. [122] David A. Leopold, Nikos K. Logothetis. Activity changes in early visual cortex reflect monkeys’percepts during binocular rivalry. Nature. Vol. 379. February. p. 549-553. (1996) [123] John. E. Lewis. Leon Glass. Nonlinear dynamics and symbolic dynamics of neural networks.Neural Computation. 4. 621-642. (1992) [124] Tsungnan Lin, Bill G. Horne, Peter Tiño, C. Lee Giles. Learning long-term dependencies is not as difficult with NARX recurrent networks. Technical report. UMIACS-TR-95-78. University of Maryland. (1995) 224 ANNEXE
Mémorisation par forçage des dynamiques chaotiques dans les modèles connexionnistes récurrents [125] Daw-Tung Lin, Judith E. Dayhoff, Panos A.Ligomenides. Trajectory production with the adaptative time-delay neural network. Neural Networks. Vol.8. No.3. p447-461. (1995) [126] Ralph Linsker. Self-organization in a perceptual network. computer magazine 21:105-117.(1988) [127] Lourenço, A. Babloyantz. Control of chaos in networks with delay: a model for synchronization of cortical tissue. Neural Computation. 6. p1141-1154. (1994) [128] M. W. Mak, Y.L. Lu, K.W. Ku. Improved real time recurrent learning algorithms : a review and some new approaches. ISANN95. [129] Mario Markus, Benno Hess. Isotropic cellular automaton for modelling excitable media.Nature. Vol. 347. No 6288. pp 56-58. (1990) [130] Lina E.L. Massone(massone@eecs.nwu.edu) , tony Khoshaba. Local dynamic interactions in the collicular motor map: a neural network model. [131] Nobuyuki Matsui, Elichi Bamba. Neural activities and cluster-formation in a random neural network. [132] W.C Mead, R.D Jones, Y.C Lee, C.W. Barnes, G.W. Flake, L.A Lee, M.K. O’Rourke. Prediction of chaotic timeseries using CNLS-Net-Example : The Mackey-Glass equation. Nonlinear modeling and forecasting. Casdagli & Eubank eds. Santa Fe. (1992) [133] Lisa Meeden, Gary McGraw, Douglas Blank. Emergent control and planning in an autonomous vehicle. Proceedings of the 15th annual conference of the cognitive science society. [134] Menon, D.S. Tang. Population oscillations in neuronal groups. International Journal of Neural Systems. Vol. 2. No. 3. pp 237-262. (1991) [135] Claude Meunier. The electrical coupling of two simple oscillators : load ans acceleration effects. Biol. Cyber. 67. p155-164.(1992) [136] Marvin L. Minsky(minsky@media.mit.edu), Seymour A. Papert. Perceptrons, Cambridge,MA:MIT Press. Introduction, pp 1-20 (1969) [137] Marvin L.Minsky(minsky@media.mit.edu), Seymour A.Papert. Epilog : The new connectionism. Perceptrons, third edition. Cambridge, MA: MIT Press, pp 247-280. (1988) [138] Melanie Mitchell (mm@santafe.edu), James P. Crutchfield, Peter T. Hraber. Dynamics, Computation and the Edge of Chaos. A Re-examination. [139] Michael C. Mozer. Neural net architectures for temporal sequence processing. Dans Time Series Prediction : Forecasting the future and understanding the past. Proc. Vol. XV. Addison-Wesley. (1993) [140] Johannes Müller-Gerking. Jacques Martinerie. Segio Neuenschwander. Laurent Pezard. Bernard Renault. Francisco J. Varela. Dectecting non-linearities in neuro-electrical signals : a study of synchronous local field potentials. à paraitre dans Physica D.(1996) [141] J.P Nadal (nadal@physique.ens.fr), N. Parga. Duality between learning machines : a bridge between supervised and unsupervised learning. Neural Computation. 6. p491-508. (1994) [142] Shigetoshi Nara, Peter Davis, Hiroo Totsuji. Memory search using complex dynamics in a recurrent neural network model. Neural Networks. Vol. 6. p963-973. (1993) [143] Kumpati S. Narendra, Snehasis Mukhopadhyay. Neural networks in control systems.Proceedings of the 31st conference on decision and control. pp1-6 (1992) [144] Kumpati S. Narendra, Snehasis Mukhopadhyay. Adaptative control of nonlinear multivariable systems using neural networks. Proceedings of the 31st conference on decision and control. pp3066-3071 (1992) Bibliographie Générale 225
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: 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?