Thèse Sciences Cognitives - Olivier Nerot

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218 Mémorisation par forçage des dynamiques chaotiques dans les modèles connexionnistes récurrents z t i () Egal à + E () t x h Gain d’apprentissage m p s Fonction neurone i ANNEXE
Mémorisation par forçage des dynamiques chaotiques dans les modèles connexionnistes récurrents BIBLIOGRAPHIE GENERALE Certaines des références indiqueées sont incomplètes, et correspondent en général à des fichiers récupérés sur les sites ftp des auteurs. [1] Sergey K. Aityan.. Recurrent refractory neural field IEEE. O-7803-0559-0/92 .p 140-145 (1992) [2] Almeida. Backpropagation in non-feedforward networks. Dans Neural Computing architectures. North Oxford Academic. Aleksander eds. (1989) [3] Amit. Storage and retrieval of temporal sequences. p 215-264. Modeling brain functions. [4] Daniel J. Amit. (ilios.fiz.huji.ac.il). The hebbian paradigm reintegrated : local reverberations as internal representations. Behavioral and Brain <strong>Sciences</strong>. No18. p617-657. (1995) [5] Amir Atiya. Unifying recurrent network trining algorithms. World congress on neural networks. Portland. Vol.3. p 585-588 (1993) [6] Amir Atiya, Pierre Baldi. Oscillations and Synchronizations in neural networks : an exploration of the labelling hypothesis. International Journal of Neural Systems. Vol. 1. No. 2. p 103-124. (1989) [7] Alex Aussem (aaussem@eso.org). Training dynamical recurrent neural networks with the temporal recurrent back-propagation algorithm : application to the time series prediction and characterization. [8] Alex Aussem (aaussem@eso.org), Fion Murtagh, Marc Sarazin. Dynamical recurrent neural networks- towards environmental time series prediction. International Journal of Neural Systems. Vol. 6. no.2 .p 145-170. (1995) [9] A. Babloyantz, A. Destexhe. Nonlinear analysis and modelling of cortical activity. Mathematics applied to biology and medecine. J. Demongeot, V. Capasso (edts). ISBN 0-920063-63-2. p 35- 48 (1993) [10] A. Babloyantz, C. Lourenço. Computation with chaos. A paradigm for cortical activity. Proc. Natl. Acad. Sci. USA. Vol.91, p.9027. (1994) [11] Back, A.C Tsoi. FIR and IIR synapses, a new neural network architecture for time series modeling. Neural computation. 3. p 375-385. (1991) [12] A. Baddeley. La mémoire humaine : theorie et pratique. Editions PUG. (1993) [13] Pierre Baldi, Amir Atiya. How delays affect neural dynamics and learning. [14] Françoise Beaufays, Eric. A. Wan. Relating real-time backpropagation and backpropagation through time : an application of flow graph interreciprocity. [15] Yoshua Bengio (bengioy@iro.umontreal.ca), Paolo Frasconi(paolo@mcculloch.ing.unifi.it) .An EM approach to learning sequential behavior. Technical report. DSI 11/94. Università di Firenze. (1994) [16] Yoshua Bengio, Paolo Frasconi, Marco Gori, Giovanni Soda. Recurrent neural networks for adaptative temporal processing. Proc. of the 6th italian workshop on parallel architecture and neural networks. WIRN93. 1993, p85-117. (1993) [17] Henri Berson. Matière et mémoire. Essai sur la relation du corps à l’esprit. (1896) Bibliographie Générale 219
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Thèse Sciences Cognitives - Olivier Nerot

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