Bibliography

1

M. Abeles, H. Bergman, E. Margalit, and E. Vaadia.
Spatiotemporal firing patterns in the frontal cortex of behaving monkeys.
J. neurophys., 70(4):1629-1638, 1993.

2

S. Amari.
Characteristics of random nets of analog neuron-like elements.
IEEE Trans. Syst. Man. Cyb., SMC-2(5):643-657, 1972.

3

C.A. Barnes, M.S. Suster, J. Shen, and B.L. McNaughton.
Multistability of cognitive maps in the hippocampus of old rats.
Nature, (388):272-275, 1997.

4

E. Bienenstock and S. Geman.
Compositionality in neural networks.
In M.A. Arbib, editor, The handbook of Brain Theory and Neural Networks, pages 223-226. MIT Press, 1995.

5

G. A. Carpenter and S. Grossberg.
A massively parallel architecture for a self-organizing neural pattern recognition machine.
Computer vision, graphics and image processing, 37:54-115, 1987.

6

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

7

F. Crick.
Function of the thalamic reticular complex: the searchlight hypothesis.
Proc. Nat. Acad. Sci USA, 81(14):4586-4590, 1984.

8

E. Daucé.
Adaptation dynamique et apprentissage dans les réseaux de neurones récurrents aléatoires.
PhD thesis, ENSAE, 2000.

9

E. Daucé, O. Moynot, O. Pinaud, and M. Samuelides.
Mean-field theory and synchronization in random recurrent neural networks.
Neural Processing Letters, In press.

10

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

11

B. Doyon, B. Cessac, M. Quoy, and M. Samuelides.
Control of the transition to chaos in neural networks with random connectivity.
Int. J. of Bif. and Chaos, 3(2):279-291, 1993.

12

J. L. Elman.
Finding structure in time.
Cognitive Science, (14):179-211, 1990.

13

P. Gaussier, C. Joulain, J.P. Banquet, S. Leprêtre, and A. Revel.
The visual homing problem: an example of robotics/biology cross fertilization.
Robotics and Autonomous Systems, 30:155-180, 2000.

14

P. Gaussier and S. Zrehen.
Perac: A neural architecture to control artificial animals.
Robotics and Autonomous Systems, 16(2-4):291-320, 1995.

15

W. Gerstner, R. Ritz, and J.L. Van Hemmen.
A biologically motivated and analytically soluble model of collective oscillations in the cortex.
Biol. Cybern., 68:363-374, 1993.

16

C.M. Gray and W. Singer.
Simulus-dependent neuronal oscillations in the cat visual cortex area.
In Neuroscience Suppl., volume 1301P. 2nd IRBO Congress, 1987.

17

M. Hermann, J. Hertz, and A. Prugel-Bennett.
Analysis of synfire chains.
Network, 63(3):403-414, 1995.

18

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

19

A. Herz, B. Sulzer, R. Kuhn, and J. L. van Hemmen.
Hebbian learning reconsidered: Representation of static and dynamic objects in associative neural nets.
Biol. Cybern., 60:457-467, 1989.

20

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

21

J.-M. Hupé, A.C. James, B.R. Payne, S.G. Lomber, P. Girard, and J. Bullier.
Cortical feedback improves discrimination between figure and background by v1, v2 and v3 neurons.
Nature, 394:784-787, 1998.

22

Y. Kuniyoshi and L. Berthouze.
Neural learning of embodied interaction dynamics.
Neural Networks, 11:1259-1276, 1998.

23

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

24

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

25

O. Moynot and M. Samuelides.
Large deviations and mean-field theory for asymmetric random recurrent neural networks.
PTRF, In press.

26

A. Revel, P. Gaussier, and J.P. Banquet.
Taking inspiration from the hippocampus can help solving robotics problems.
In European Symposium on Artificial Neural Networks, Bruges, Belgium, Avril 1999. IEEE.

27

E. Rodriguez, N. George, J.-P. Lachaux, J. Martinerie, B. Renault, and F. J. Varela.
Perception's shadow: long-distance synchronization of human brain activity.
Nature, 397:430-433, 1999.

28

W. D. Ross, S. Grossberg, and E Mingolla.
Visual cortical mechanisms of perceptual grouping: Interacting layers, networks, columns, and maps.
Neural Networks, (13):571-588, 2000.

29

T.B. Schillen and P. König.
Stimulus dependent assembly formation of oscillatory responses: Ii. desynchronization.
Neural Computation, 3:167-178, 1991.

30

G. Schöner, M. Dose, and C. Engels.
Dynamics of behavior: theory and applications for autonomous robot architectures.
Robotics and Autonomous System, 16(2-4):213-245, December 1995.

31

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

32

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

33

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

34

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

35

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

36

F. Varela, E. Thompson, and E. Rosch.
The Embodied Mind.
MIT Press, 1991.

37

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

38

Ronald J. Williams and David Zipser.
A learning algorithm for continually running recurrent neural networks.
Neural Computation, 1(2):270-280, 1989.