Thèse Sciences Cognitives - Olivier Nerot

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Mémorisation par forçage des dynamiques chaotiques dans les modèles connexionnistes récurrents [190] Christopher M. Thomas. William G. Gibson. John Robinson. Stability and bifurcations in an associative memory model. Neural Networks. Vol. 9.No1. p53-66. (1996) [191] Peter Tiño (tino@decef.elf.stuba.sk), Bill G. Horne, C. Lee Giles. Fixed points in two-neuron discrete time recurrent networks : stability and bifurcation considerations. Technical report.UMIACS-TR-95-51. University of Maryland. (1995) [192] Peter Tiño (tino@decef.elf.stuba.sk), Bill G. Horne, C. Lee Giles. Finite State Machines and recurrent neural networks. Automata and dynamical systems approaches. Technical Report. UMIACS-TR-95-1. University of Maryland. (1995) [193] Nikzad Benny Toomarian, Jacob Barhen. Learning a trajectory using adjoint functions and teacher forcing. Neural Networks. 5. p 473-383. (1992) [194] Towards an artificial eye. IEEE Spectrum. p21-69. 0018-9235/96. (1996) [195] Ah Chung Tsoi, Andrew D. Back. Locally recurrent globally feedforward networks : a critical review of architectures. IEEE TNN. Vol. 5. No.2. p 229-239. (1994) [196] Ichiro Tsuda. Dynamic Link of memory- chaotic memory map in nonequilibrium neural networks. Neural networks, vol. 5. pp 313-326. (1992) [197] Fu-Sheng Tsung (tsung@cs.ucsd.edu). Learning in finite difference networks. [198] Fu-Sheng Tsung(tsung@cs.ucsd.edu), Garrison W. Cottrell (gary@cs.ucsd.edu). Hopf bifurcation and hopfhopping in recurrent nets. [199] Fu-Sheng Tsung (tsung@cs.ucsd.edu), Garrison W. Cottrell (gary@cs.ucsd.edu). Learning in recurrent finite difference networks. International Journal of Neural Systems. Vol. 6, No 3. p249- 256. (1995) [200] E. Tzirkel-Hancock (et@uk.ac.cam.eng), F. Fallside.. A direct control method for a class of nonlinear systems using neural networks. [201] E. Tzirkel-Hancock (et@uk.ac.cam.eng), F. Fallside. Stable control of nonlinear systems using neural networks. [202] P. Unnikrishnan(unni@neuro.cs.gmr.com), K. P. Venugopal. Alopex : a correlation-based learning algorithm for feedforward and recurrent neural networks. Neural Computation.Vol. 6, No. 3. may (1994) [203] K.P Unnikrishnan, J.J. Hopfield. Connected-digit speaker-dependent speech recognition using a neural network with time-delayed connections. IEEE transactions on signal processing. Vol. 39. No.3. p698-712. (1991) [204] Unnikrishnan, J.J. Hopfield, D.W Tank. Speaker-independent digit recognition using a neural network with time-delayed connections. Neural computations. 4. p108-119. (1992) [205] F.J. Varela. Autonomie et connaissance. La couleur des idées.Collection Seuil.Edition 1989. [206] Vibert, Khachayar Pakdaman, Noureddine Azmy. Interneural delay modification synchronizes biologically plausible neural networks. Neural Networks. Vol. 7. No.4 . pp 589-607. (1994) [207] Eric. A. Wan (wan@isl.stanford.edu). Time series prediction by using a connectionist network with internal delay lines. Dans Time Series prediction, Forecasting the future and understanding the past. A. Weigend, N. Gershenfeld, editors. SFI studies in the sciences of complexity. Vol. XVII. Addison-Wesley (1994) [208] Eric A. Wan. Modeling nonlinear dynamics with neural networks : examples in time series prediction. [209] Xin Wang. Discrete-time dynamics of coupled quasi-periodic and chaotic neural network oscillators. 0-7803-0559-0/92. IEEE TNN. Vol. 3. p517-522. (1992) 228 ANNEXE
Mémorisation par forçage des dynamiques chaotiques dans les modèles connexionnistes récurrents [210] Xiao-Jing Wang, John Rinzel. Alternating and synchronous rythms in reciprocally inhibitory model neurons. Neural Computations. 4. p84-97. (1992) [211] Deliang Wang, Joachim Buhmann. Pattern segmentation in associative memory. Neural Computation. 2. p94-106. (1990) [212] Michael Wellky. William H. Bosking. David Fitzpatrick. A systematic map of direction preference in primary visual cortex. Nature. Vol. 379. p725-728. (1996) [213] Ronald J. Williams. Training Recurrent networks using the extended kalman filter. 0-7803-0559- 0/92. IEEE. Vol. 4. p.241-246. (1992) [214] Ronald.J. Williams, Jing Peng. An efficient gradient based algorithm for on-line training of recurrent network trajectories. Neural Computation. 2. p490-501. (1990) [215] Ronald.J. Williams, D. Zipser (zipser@cogsci.ucsd.edu). A learning algorithm for continually running fully recurrent neural networks. Neural Computation. 1. p270-280. (1989) [216] Ronald.J. Williams, D. Zipser (zipser@cogsci.ucsd.edu). Experimental analysis of the realtime recurrent learning algorithm. Connection Science, 1, pp87-111. (1991) [217] Chwan-Hwa Wu, Jyun-Hwei Tsai. An asynchronous relaxation method for the parallel simulation of the learning of recurrent neural networks. [218] Yong Yao, Walter J. Freeman (wfreeman@garnet.berkeley.edu). Model of biological pattern recognition with spatially chaotic dynamics. Neural Networks. Vol. 3. pp 153-170. (1990) [219] Michael Zak. Terminal attractors in neural networks. Neural Networks. Vol.2. p259-274.(1989) [220] Rafal Waclaw Zbikowski. Recurrent neural networks, some control aspects. Glasgow University. (1994) [221] David Zipser. A subgrouping strategy that reduces complexity and speeds up learning in recurrent networks. Neural Computation. 1. p552-558. (1989) [222] David Zipser. Recurrent network model of the neural mechanism of short-term active memory. Neural computation. 3. 179-193. (1991) Bibliographie Générale 229
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