.. La-représentation-de-l-'espace, 29 I. Les représentations spatiales en robotique, 31 II. Les représentations spatiales chez les animaux : l'hippocampe . . . . . . . 32 A

.. La-sélection-de-l-'action-par-renforcement, I. Origines, and .. , 36 II. Principes généraux, 41 C. Méthode des différences temporelles . . . . . . . . . . . . . . . . 41 IV, p.41

R. Ito, Synaptic plasticity in the cerebellar cortex and its role in motor learning, The Canadian journal of neurological sciences, vol.20, issue.3, pp.70-74, 1993.

&. T. Koch and . Poggio, Multiplying with Synapses and Neurons, Single neuron computation, pp.315-345
DOI : 10.1016/B978-0-12-484815-3.50019-0

T. Kohonen, Associative memory : A system-theoretical approach, 1977.
DOI : 10.1007/978-3-642-96384-1

W. S. Mcculloch and &. W. Pitts, A logical calculus of the ideas immanent in nervous activity, The Bulletin of Mathematical Biophysics, vol.5, issue.4, pp.115-133, 1943.
DOI : 10.1007/BF02478259

M. Minsky and &. S. Papert, Perceptrons : An introduction to computational geometry, 1969.

M. Riesenhuber and &. T. Poggio, Hierarchical models of object recognition in cortex, Nature Neuroscience, vol.28, pp.1019-1025, 1999.

R. Ritz and &. J. Sejnowski, Synchronous oscillatory activity in sensory systems: new vistas on mechanisms, Current Opinion in Neurobiology, vol.7, issue.4, pp.536-546, 1997.
DOI : 10.1016/S0959-4388(97)80034-7

E. T. Rolls and &. A. Treves, Neural networks and brain function, 1998.
DOI : 10.1093/acprof:oso/9780198524328.001.0001

F. Rosenblatt, Principles of neurodynamics ; perceptrons and the theory of brain mechanisms, 1962.

D. E. Rumelhart, G. E. Hinton, and &. J. Williams, Learning Internal Representations by Error Propagation, Parallel Distributed Processing, pp.318-362, 1986.
DOI : 10.1016/B978-1-4832-1446-7.50035-2

]. J. Shawe-taylor-91, P. Shawe-taylor, &. M. Jeavons, R. E. Daalen, and . Soodak, Reverse-Hebb plasticity leads to optimization and association in a simulated visual cortex, Probabilistic Bit Stream Neural Chip : Theory. Connection Science, pp.317-328, 1991.

&. G. Srinivasan and . Bernard, A proposed mechanism for multiplication of neural signals, Biological Cybernetics, vol.5, issue.b, pp.227-263, 1976.
DOI : 10.1007/BF00344168

S. Richard, &. Sutton, G. Andrew, and . Barto, Reinforcement learning : An introduction (adaptive computation and machine learning), 1998.

]. G. Wallis-96a and . Wallis, How neurons learn to associate 2D-views in invariant object recognition. Rapport technique 37, Max-Planck-Institute für, Biologishe Kybernetik, 1996.

]. G. Wallis-96b and . Wallis, Using Spatio-temporal Correlations to Learn Invariant Object Recognition, Neural Networks, vol.9, issue.9, pp.1513-1519, 1996.
DOI : 10.1016/S0893-6080(96)00041-X

]. A. Yuille, D. M. Kammer, and &. D. Cohen, Quadrature and the development of orientation selective cortical cells by Hebb rules, Biological Cybernetics, vol.46, issue.3, pp.183-194, 1989.
DOI : 10.1007/BF00198765

.. Vue-générale, 88 II. Cartes référentes et groupes associatifs

. Neurones, .. Connexions-pour-la-prévision, B. Réflexes, and .. , 100 I. Cartes temporisées étendues 100 II. Stabilité perceptive et diffusion de la prévision, 101 III. Connexions de maintien d'activation 102 IV. Cohérence de l'activation . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 A

]. A. Arleo-00a, &. W. Arleo, and . Gerstner, Modeling Rodent Head-direction Cells and Place Cells for Spatial Learning in Bio-mimetic Robotics, editeurs, From Animals to Animats VI, pp.236-245, 2000.

]. A. Arleo-00b, &. W. Arleo, and . Gerstner, Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity, Biological Cybernetics, vol.83, issue.3, 2000.
DOI : 10.1007/s004220000171

&. W. Arleo and . Gerstner, Spatial orientation in navigating agents: Modeling head-direction cells, Neurocomputing, vol.38, issue.40, pp.38-40, 2001.
DOI : 10.1016/S0925-2312(01)00572-0

A. Arleo, F. Smeraldi, and &. W. Gerstner, Cognitive Navigation Based on Nonuniform Gabor Space Sampling, Unsupervised Growing Networks, and Reinforcement Learning, IEEE Transactions on Neural Networks, vol.15, issue.3, pp.639-52, 2004.
DOI : 10.1109/TNN.2004.826221

R. Bellman, Dynamic programming, 1957.

I. Biederman and &. P. Gerhardstein, Recognizing depth-rotated objects: Evidence and conditions for three-dimensional viewpoint invariance., Journal of Experimental Psychology: Human Perception and Performance, vol.19, issue.6, pp.1162-1182, 1993.
DOI : 10.1037/0096-1523.19.6.1162

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.332.3438

T. V. Bliss and &. L. Collingridge, A synaptic model of memory: long-term potentiation in the hippocampus, Nature, vol.361, issue.6407, pp.31-39, 1993.
DOI : 10.1038/361031a0

H. Bülthoff, S. Edelman, and &. M. Tarr, How Are Three-Dimensional Objects Represented in the Brain ? Rapport technique, 1994.

R. Chatila and &. J. Laumond, Position referencing and consistent world modeling for mobile robots, Proceedings. 1985 IEEE International Conference on Robotics and Automation, 1985.
DOI : 10.1109/ROBOT.1985.1087373

W. Huu, &. R. Paquier, and . Chatila, Transition Cells for navigation and planning in an unknown environment. Dans The Society For Adaptive Behavior SAB Combining structural descriptions and image-based representations for image object and scene recognition, CQGGL06. [Do Huu 05 International Joint Conference on Artificial Intelligence (?CAI), pp.286-297, 2005.

D. Fregnac, S. Shulz, &. E. Thorpe, and . Bienenstock, A cellular analogue of visual cortical plasticity, Nature, vol.333, issue.6171, pp.367-370, 1988.
DOI : 10.1038/333367a0

P. Gaussier, A. Revel, J. P. Banquet, and &. V. Badeau, From view cells and place cells to cognitive map learning: processing stages of the hippocampal system, Biological Cybernetics, vol.86, issue.1, pp.15-28, 2002.
DOI : 10.1007/s004220100269

URL : https://hal.archives-ouvertes.fr/hal-00426240

P. Georges-françois, E. T. Rolls, and &. G. Robertson, Spatial View Cells in the Primate Hippocampus: Allocentric View not Head Direction or Eye Position or Place, Gerstner 95] W. Gerstner. Time structure of the activity in neural network models, pp.197-212, 1995.
DOI : 10.1093/cercor/9.3.197

J. J. Gibson, The ecological approach to visual perception, 1979.

O. Donald, . Hebb-c, &. T. Koch, T. Poggio, &. T. Kohonen-kortenkamp et al., Synaptic plasticity in the cerebellar cortex and its role in motor learning The Canadian journal of neurological sciences Multiplying with synapses and neurons Single neuron computation Associative memory : A system-theoretical approach Self-organization and associative memory Topological mapping for mobile robots using a combination of sonar and vision sensing, Twelfth National Conference on Artificial IntelligenceKuipers 78] B. Kuipers. Modeling Spatial KnowledgeKuipers 91] B. Kuipers & Y.T. Byun. A Robot Exploration and Mapping Strategy Based on a Semantic Hierarchy of Spatial Representations . Dans Robot. Autonomous SystLapicque 07] L. Lapicque. Recherches quantitatives sur l'excitation électrique des nerfs traitée comme une polarisation, pp.70-74, 1949.

G. S. Leprêtre, P. Gaussier, &. J. Cocquerez, W. S. Mcculloch, and &. W. Pitts, From Navigation to Active Object Recognition A Logical Calculus of the Ideas Immanent in Nervous Activity Perceptrons : An introduction to computational geometry High resolution maps from wide angle sonar, Maass 96] W. Maass. Lower Bounds for the Computational Power of Networks of Spiking Neurons. Neural ComputationMoravec 85] H. P. Moravec & A. Elfes IEEE Int. Conf. Robotics AutomationMoravec 88] H. P. Moravec. Sensor fusion in certainty grids for mobile robots. A.I. Magazine, pp.620-635, 1907.

[. Keefe-71, ]. J. O-'keefe, &. J. Dostrovsky, J. O-'keefe, and &. L. Nadel, The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat, Brain Research, vol.34, issue.1, pp.171-175, 1971.
DOI : 10.1016/0006-8993(71)90358-1

T. Poggio, &. S. Edelman, A. D. Redish, &. S. Touretzkyredish-97b-]-a, &. S. Redish et al., A Network That Learns to Recognize Three Dimensional Objects Cognitive maps beyond the hippocampus Navigating with landmarks : Computing goal locations from place codes Hierarchical models of object recognition in cortex Synchronous oscillatory activity in sensory systems : new vistas on mechanisms View-responsive neurons in the primate hippocampal complex, Thèse de doctorat Symbolic Visual LearningRiesenhuber 00] M. Riesenhuber & T. Poggio. Models of Object Recognition Neural networks and brain functionRolls 99] E. T. Rolls. Spatial View Cells and the Representation of Place in the Primate Hippocampus. Hippocampus, pp.263-266, 1990.

E. T. Rolls, J. Xiang, &. L. Franco, E. T. Rolls, &. J. Xiang et al., Reward-Spatial View Representations and Learning in the Primate Hippocampus Spatial view cells in the primate hippocampus and memory recall Principles of neurodynamics ; perceptrons and the theory of brain mechanisms Learning Internal Representations by Error Propagation Computer simulation of hippocampal place cells Reverse-Hebb plasticity leads to optimization and association in a simulated visual cortex A proposed mechanism for multiplication of neural signals, Object, Space, and Object- Space Representations in the Primate Hippocampus Parallel Distributed Processing Probabilistic Bit Stream Neural Chip : Theory. Connection Science, pp.833-844, 1962.

S. Richard, &. Sutton, G. Andrew, and . Barto, Reinforcement learning : An introduction (adaptive computation and machine learning

M. Tarr, &. H. Bülthoff, D. S. Touretzky, and &. A. Redish, Is human object recognition better described by geon structural descriptions or by multiple views? Comment on Biederman and Gerhardstein (1993)., Journal of Experimental Psychology: Human Perception and Performance, vol.21, issue.6, pp.1494-1505, 1988.
DOI : 10.1037/0096-1523.21.6.1494

]. G. Wallis-96a and . Wallis, How neurons learn to associate 2D-views in invariant object recognition. Rapport technique 37, Max-Planck-Institute für, Biologishe Kybernetik, 1996.

]. G. Wallis-96b and . Wallis, Using Spatio-temporal Correlations to Learn Invariant Object Recognition, Proceedings of the 1993 Connectionist Models Summer School, pp.1513-1519, 1994.
DOI : 10.1016/S0893-6080(96)00041-X

B. Yamauchi and &. R. Beer, Spatial learning for navigation in dynamic environments, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.26, issue.3, 1996.
DOI : 10.1109/3477.499799

]. A. Yuille, D. M. Kammer, and &. D. Cohen, Quadrature and the development of orientation selective cortical cells by Hebb rules, Biological Cybernetics, vol.46, issue.3, pp.183-194, 1989.
DOI : 10.1007/BF00198765