Bibliographie

1

Prigogine, I.:
la fin des certitudes.
Odile Jacob (1996)

2

Schrödinger, E.:
Qu'est-ce que la vie ?
Christian Bourgeois (1986) (1944)

3

Varela, F., Thomson, E., Rosch, E.:
L'inscription corporelle de l'esprit.
Seuil (1993)

4

Dupuy, J.P.:
Aux origines des sciences cognitives.
la Découverte (1999)

5

Lorenz, E.N.:
Deterministic nonperiodic flow.
J. Atmos. Sci. 20 (1963) 130-141

6

Mandelbröt, B.:
les objets fractals.
Flammarion (1975)

7

Ruelle, D., Takens, F.:
On the nature of turbulence.
Comm. Math. Phys. 20 (1971) 167-192

8

Bergé, P., Pomeau, Y., Vidal, C.:
L'ordre dans le chaos.
Hermann (1988,1992)

9

Cessac, B.:
Propriétés statistiques des dynamiques de réseaux neuromimétiques.
PhD thesis, Université Paul Sabatier (1994)

10

Arnold, V.:
Chapitre supplémentaire sur la théorie des équations différentielles ordinaires.
Mir, Moscou (1978)

11

Maas, W., Bishop, C.M.:
Pulsed Neural networks.
MIT Press (1999)

12

Hebb, D.:
The Organization of behavior.
Wiley, New York (1949)

13

Pelissier, A., Tête, A.:
Sciences cognitives textes fondateurs.
PUF (1995)

14

Bliss, T.V.P., Lomo, T.:
Long-lasting potentiation of synaptic transmission in the dendate area of the anaesthetized rabbit following stimulation of the perforant path.
J. Physiol. 232 (1973) 331-356

15

Schütz, A.:
Neuroanatomy in a computational perspective.
In Arbib, M., ed.: The handbook of Brain Theory and Neural Networks, MIT Press (1995) 230-234

16

Abeles, M.:
Local Cortical Circuits: An Electrophysiological Study.
Springer-Verlag (1982)

17

Berthoz, J.:
Le sens du mouvement.
Odile Jacob (1996)

18

Gray, C., Konig, P., Engel, A., Singer, W.:
Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflect global stimulus properties.
Nature 7 (1989) 334-338

19

Gevins, A.:
Dynamic cognitive networks in the human brain.
In Buzsáki, G., ed.: Temporal Coding in the Brain, Berlin Heidelberg, Springer-Verlag (1994) 273-290

20

Jirsa, V.K., Fuchs, A., Kelso, J.A.S.:
Connecting cortical and behaviorall dynamics: Bimanual coordination.
Neural Computation 10 (1998) 2019-2045

21

Gray, C., Singer, W.:
Simulus-dependent neuronal oscillations in the cat visual cortex area.
In: Neuroscience Suppl. Number 1301P, 2nd IRBO Congress (1987)

22

Abeles, M., Bergman, H., Margalit, E., Vaadia, E.:
Spatiotemporal firing patterns in the frontal cortex of behaving monkeys.
J. neurophys. 70 (1993) 1629-1638

23

Mac Leod, K., Laurent, G.:
Distinct mechanisms for synchronization and temporal patterning of odor-encoding cell assemblies.
Science 274 (1996) 976-979

24

Hansel, D., Sompolinsky, H.:
Chaos and synchrony in a model of a hypercolumn in visual cortex.
J. Comp. Neurosc. 3 (1996) 7-34

25

Finkel, L., Yen, S.C., Menschick, E.:
Synchronization: The computational currency of cognition.
In Niklasson, L., Boden, M., Ziemke, T., eds.: ICANN 98. Volume 1., Springer (1998) 23-40

26

Von der Malsburg, C.:
A neural cocktail-party processor.
Biol. Cybern. 54 (1986) 29-40

27

Thorpe, S.J., Fize, D., Marlot, C.:
Speed of processing in the human visual system.
Nature 381 (1996) 520-522

28

Rapp, P.:
Chaos in the neurosciences: cautionary tales from the frontier.
Biologist 40 (1993) 89-94

29

Babloyantz, A., Nicolis, C., Salazar, J.:
Evidence of chaotic dynamics of brain activity during the sleep cycle.
Phys. Lett (1985) 152-156

30

Martinerie, J., Adam, C., Le van Quyen, M., Baulac, M., Renault, B., Varela, F.J.:
Can epileptic crisis be anticipated?
Nature Medecine (1998) In press.

31

Thomasson, N., Pezard, L., Allillaire, J., Renault, B., Martinerie, J.:
Nonlinear eeg changes associated with clinical improvement in depressed patients.
Nonlinear Dynamics in psychology and life science 4 (1999) à paraître

32

Skarda, C., Freeman, W.:
How brains make chaos in order to make sense of the world.
Behav. Brain Sci. 10 (1987) 161-195

33

Mac Cullogh, W.S., Pitts, W.:
A logical calculus of the ideas immanent in nervous activity.
Bull. Math. Biophys. 5 (1943) 115-133

34

Rosenblatt:
Perceptron simulation experiments.
Proceedings of the I. R. E. (1960) 167-192

35

Hopfield, J.:
Neural networks and physical systems with emergent collective computational abilities.
Proc. Nat. Acad. Sci. 79 (1982) 2554-2558

36

Kohonen, T.:
Self-organized formation of topologically correct feature maps.
Biological Cybernetics 43 (1982) 59-69

37

Rumelhart, D., Hinton, G., Williams, R.:
Learning representations by back-propagating errors.
Nature (1986) 533-536

38

Mac Kay, D.:
Bayesian methods for supervised neural networks.
In Arbib, M., ed.: The handbook of Brain Theory and Neural Networks, MIT Press (1995) 144-149

39

Vapnik, V.N.:
The nature of statistical learning theory.
Springer Verlag (1995)

40

Lang, K.J., Waibel, A.H., Hinton, G.E.:
A time-delay neural network architecture for isolated word recognition.
Neural Networks 3 (1990) 23-43

41

Dorffner, G.:
Neural networks for time-series processing.
Neural Network World 6 (1996) 447-468

42

Haykin, S.:
Neural Networks : a comprehensive fundation.
Prentice-Hall (1999)

43

Singer, W.:
Time as coding space in neocortical processing : a hypothesis.
In Buzsáki, G., ed.: Temporal Coding in the Brain, Berlin Heidelberg, Springer-Verlag (1994) 51-79

44

Rolls, E.T., Treves, A.:
Neural Networks and brain functions.
Oxford University Press, Great Clarendon Street, Oxford OX2 6DP (1998)

45

Lanthorn, T., Storn, J., Andersen, P.:
Current-to-frequency transduction in ca1 hippocampal pyramidal cells : slow prepotentials dominate the primary range firing.
Experimental brain research 53 (1984) 431-443

46

Thorpe, S., Imbert, M.:
Parallel processing in neural systems.
R.Eckmiller G.Hartman and G.Hauske, North Holland (1990)

47

Hertz, J., Prugel-Bennett, A.:
Learning synfire chains: turning noise into signal.
Int. J. Neural Systems 7 (1996) 445-450

48

Eurich, C.W., Conradi, T., Schwegler, H.:
Critical and non-critical avalanche behaviour in networks of integrate and fire neurons.
In Verleysen, M., ed.: ESANN 99, D-Facto (1999) 411-416

49

Brunel, N., Hakim, V.:
Fast global oscillations in networks of integrate-and-fire neurons with low firing rates.
Neural Computation 11 (1999) 1621-1676

50

Sick, B.:
Structure evolution for time-delay neural networks.
In: ICANN 99 (Proceedings of the 9th International Conference on Artificial Neural Networks). Volume 2. (1999) 667-672

51

Changeux, J., Dehaene, S.:
Neuronal models of cognitive function.
Cognition 33 (1989) 63-109

52

Hirsch, M.W.:
Convergent activation dynamics in continuous time networks.
Neural Networks 2 (1989) 331-349

53

Yao, Y., Freeman, W.:
Model of biological pattern recognition with spatially chaotic dynamics.
Neural networks 3 (1990) 153-170

54

Nérot, O.:
Mémorisation par forçage neuronal des dynamiques chaotiques dans les modèles connexionnistes récurrents.
PhD thesis, INPG (1996)

55

Sejnowski, T.J.:
Strong covariance with nonlinearly interacting neurons.
Journal of Mathematical biology 4 (1977) 303-321

56

Watkins, C.J.C.H.:
Learning from delayed rewards.
PhD thesis, Cambridge University (1989)

57

Francois, O., Demongeot, J., Hervé, T.:
Convergence of a self-organizing stochastic neural network.
Neural networks 5 (1992) 277-282

58

Williams, R.J., Zipser, D.:
A learning algorithm for continually running recurrent neural networks.
Neural Computation 1 (1989) 270-280

59

Herz, A., Sulzer, B., Kuhn, R., van Hemmen, J.L.:
Hebbian learning reconsidered: Representation of static and dynamic objects in associatiive neural nets.
Biological Cybernetics 60 (1989) 457-467

60

Meunier, C., Nadal, J.P.:
Sparsely coded neural networks.
In Arbib, M., ed.: The handbook of Brain Theory and Neural Networks, MIT Press (1995) 899-901

61

Grossberg, S.:
Adaptive pattern classification and universal recoding (i and ii).
Biological Cybernetics 23 (1976) 121-134,187-202

62

Dehaene, S., Changeux, J.P., Nadal, J.P.:
Neural networks that learn temporal sequences by selection.
Proc. Nat. Acad. Sci. USA 84 (1987) 2727-2731

63

Reiss, M., Taylor, J.G.:
Storing temporal sequences.
Neural networks 4 (1991) 773-787

64

Ans, B., Coiton, Y., Gilhodes, J.C., Velay, J.L.:
A neural network model for temporal sequence learning and motor programming.
Neural Networks 7 (1994) 1461-1476

65

Pearlmutter, B.:
Learning state space trajectories in recurrent neural networks.
Neural coputation 2 (1989) 263-269

66

Tani, J., Fukumura, N.:
Embedding a grammatical description in deterministic chaos: an experiment in recurrent neural learning.
Biological Cybernetics 72 (1995) 365-370

67

Amari, S.:
Dynamics of pattern formation in lateral-inhibition type neural fields.
Biological Cybernetics 27 (1977) 77-87

68

Schöner, G., Dose, M., Engels, C.:
Dynamics of behavior: theory and applications for autonomous robot architectures.
Robotics and Autonomous System 16 (1995) 213-245

69

Bicho, E., Schöner, G.:
The dynamics approach to autonomous robotics demonstrated on a low-level vehicle platform.
Robotics and Autonomous System 21 (1997) 23-35

70

Kuniyoshi, Y., Berthouze, L.:
Neural learning of embodied interactionn dynamics.
Neural Networks 11 (1998) 1259-1276

71

Tani, J.:
An interpretation of the self from the dynamical systems perspective: a constructivist approach.
Journal of conciousness studies 5 (1998)

72

Tani, J., Nolfi, S.:
Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems.
In Pfeifer, R., Blumberg, B., Meyer, J., Wilson, S., eds.: From Animals to Animats: Simulation of Adaptive Behavior SAB'98, MIT Press (1998) 270-279

73

Gaussier, P., Zrehen, S.:
A topological map for on-line learning : Emergence of obstacle avoidance in a mobile robot.
In: From Animals to Animats: SAB'94, Brighton, MIT Press (1994) 282-290

74

Banquet, J., Gaussier, P., Dreher, J., Joulain, C., Revel, A., Gunther, W.:
Space-time, order and hierarchy in frontal-hippocampal system : a neural basis of personality.
In: Cognitive Science Perspectives on Personality and Emotion, Amsterdam, Elsevier Science (1998)

75

Gaussier, P., Moga, S., Banquet, J., Quoy, M.:
From perception-action loops to imitation processes.
Applied Artificial Intelligence 1 (1999)

76

Amari, S., Yoshida, K., Kanatani, K.I.:
A mathematical foundation for statistical neurodynamics.
SIAM J. Appl. Math. 33 (1977) 95-126

77

Sompolinsky, H., Crisanti, A., Sommers, H.:
Chaos in random neural networks.
Phys. Rev. Lett. 61 (1988) 259-262

78

Doyon, B., Cessac, B., Quoy, M., Samuelides, M.:
Control of the transition to chaos in neural networks with random connectivity.
Int. J. of Bif. and Chaos 3 (1993) 279-291

79

Cessac, B., Doyon, B., Quoy, M., Samuelides, M.:
Mean-field equations, bifurcation map and route to chaos in discrete time neural networks.
Physica D 74 (1994) 24-44

80

Cessac, B.:
Increase in complexity in random neural networks.
Journal de Physique I 5 (1995) 409-432

81

Quoy, M., Doyon, B., Samuelides, M.:
Hebbian learning in discrete time chaotic neural networks.
In: WCNN 95. (1995)

82

Daucé, E., Quoy, M., Cessac, B., Doyon, B., Samuelides, M.:
Self-organization and dynamics reduction in recurrent networks: stimulus presentation and learning.
Neural Networks 11 (1998) 521-533

83

Girko, V.:
Circular law.
Theory Prob. Appl. (USSR) 29 (1985) 694-706

84

Samuelides, M., Moynot, O., Pinaud, O., Daucé, E.:
étude de la dynamique de réseaux neuronaux aléatoires récurrents.
Technical report, ONERA (1999)

85

Amari, S.:
Characteristics of random nets of analog neuron-like elements.
IEEE Trans. Syst. Man. Cyb. 2 (1972)

86

Derrida, B., Pommeau, Y.:
Random networks of automata: A simple annealed approximation.
Europhys. lett. 1 (1986) 45-59

87

van Vreewijk, C., Sompolinsky, H.:
Chaotic balanced state in a model of cortical circuits.
Neural Computation 10 (1998) 1321-1371

88

Daucé, E., Moynot, O., Pinaud, O., Samuelides, M., Doyon, B.:
Mean field equations reveal synchronization in a 2-populations neural network model.
In Verleysen, M., ed.: ESANN 99, D-Facto (1999) 7-12

89

Moynot, O., Daucé, E., Pinaud, O.:
équations de champ moyen pour les réseaux de neurones à deux populations.
In Cognito (1999) À paraître.

90

Ginzburg, I., Sompolinsky, H.:
Theory of correlations in stochastic neural networks.
Phys. Rev. E. 50 (1994) 3171-3191

91

Pinaud, O.:
Chaos dans les équations de champ moyen d'un réseau neuronal à deux populations quasi-symétrique.
Technical report, ONERA (1999)

92

Weisbuch, G.:
Dynamique des systèmes complexes.
InterEditions/Editions du CNRS (1989)

93

Hertz, J.:
Computing with attractors.
In Arbib, M., ed.: The handbook of Brain Theory and Neural Networks, MIT Press (1995) 230-234

94

Daucé, E.:
Dynamique chaotique et apprentisage au sein de réseaux neuromimétiques.
ONERA-CERT (1995) rapport de DEA.

95

Quoy, M.:
Apprentissage dans les réseaux neuromimétiques à dynamique chaotique.
PhD thesis, ENSAE (1994)

96

Daucé, E., Doyon, B.:
Novelty learning in a discrete-time chaotic network.
In Niklasson, L., Boden, M., Ziemke, T., eds.: ICANN 98. Volume 2., Springer (1998) 1051-1056

97

Buonviso, N., Gervais, R., Chalansonnet, M., Chaput, M.:
Short-lasting exposure to one odor decreases general reactivity in the olfactory bulb of adult rat.
European J. Neuroscience 10 (1998) 2472 - 2475

98

Daucé, E., Doyon, B.:
Apprentissage dynamique dans les réseaux de neurones.
In Vivicorsi, B., ed.: Actes du troisième colloque jeunes chercheurs en sciences cognitives, ASCO, Université V. Segalen, Bordeaux (1999) 76-83

99

Aitken, A.:
An architecture for learning to behave.
In: From Animals to Animats 3. Proceedings of the Third International Conference on Simulation of Adaptive Behavior. (1994) 315-324

100

Quoy, M., Daucé, E.:
Visual and motor learning using a chaotic recurrent neural network: application to the control of a mobile robot.
In: NC 2000. (2000) accepted for oral presentation.

101

O'Keefe, J., Nadel, L.:
The hippocampus as a cognitive map.
Oxford University Press (1978)

102

Rolls, E., O'Mara, S.:
View-responsive neurons in the primate hippocampal complex.
Hippocampus (1995) 409-424