E. D. Adrian, Afferent discharges to the cerebral cortex from peripheral sense organs, Journal of Physiology London, p.100, 1941.

G. Alexander, M. Crutcher, and M. Delong, Basal ganglia-thalamocortical circuits: parallel substrates for motor, oculomotor, "prefrontal" and "limbic" functions, Prog Brain Res, vol.85, pp.119-146, 1990.

A. Arleo and W. Gerstner, Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity, Biological Cybernetics, vol.83, issue.3, pp.287-299, 2000.

I. A. Bachelder and A. M. Waxman, Mobile robot visual mapping and localization: A view-based neurocomputational architecture that emulates hippocampal place learning, Neural networks, vol.7, issue.6, pp.1083-1099, 1994.

B. W. Balleine and J. P. Doherty, Human and rodent homologies in action control: corticostriatal determinants of goal-directed and habitual action, Neuropsychopharmacology, vol.35, issue.1, pp.48-69, 2010.

J. Banquet, P. Gaussier, J. C. Dreher, C. Joulain, A. Revel et al., Space-time, order, and hierarchy in fronto-hippocampal system: A neural basis of personality, Cognitive Science Perspectives on Personality and Emotion, vol.124, pp.123-189, 1997.
URL : https://hal.archives-ouvertes.fr/hal-00448950

J. Banquet, P. Gaussier, M. Quoy, A. Revel, and Y. Burnod, A hierarchy of associations in hippocampo-cortical systems: Cognitive maps and navigation strategies, Neural Computation, vol.17, issue.6, pp.1339-1384, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00426212

P. Barone and J. P. Joseph, Prefrontal cortex and spatial sequencing in macaque monkey, Experimental Brain Research, vol.78, issue.3, pp.447-464, 1989.

A. Barto, Adaptive critics and the basal ganglia, Models of Information Processing in the Basal Ganglia, pp.215-232, 1995.

I. Bar-gad, G. Havazelet-heimer, J. A. Goldberg, E. Ruppin, and H. Bergman, Reinforcement-driven dimensionality reduction -a model for information processing in the basal ganglia, J Basic Clin Physiol Pharmacol, vol.11, p.30520, 2000.

A. M. Bastos, W. M. Usrey, R. A. Adams, G. R. Mangun, P. Fries et al., Canonical microcircuits for predictive coding, Neuron, vol.76, issue.4, pp.695-711, 2012.

J. Bellot, O. Sigaud, M. ;. Khamassi, C. Balkenius, H. et al., Which temporal difference learning algorithm best reproduces dopamine activity in a multi-choice task?, Ziemke, vol.12, pp.289-298, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00731475

T. W. Berger and R. F. Thompson, Neuronal plasticity in the limbic system during classical conditioning of the rabbit nictitating membrane response. i. the hippocampus, Brain research, vol.145, issue.2, pp.323-346, 1978.

A. Berthoz, Le Mouvement. Odile Jacob, 2002.

A. M. Bornstein and N. D. Daw, Multiplicity of control in the basal ganglia: computational roles of striatal subregions, Current opinion in neurobiology, vol.21, issue.3, pp.374-380, 2011.

R. A. Brooks, A robust layered control system for a mobile robot, IEEE Journal of Robotics and Automation, R.A, vol.2, pp.14-23, 1986.

R. A. Brooks, Elephants don't play chess. Robotics and Autonomous Systems, vol.6, pp.3-15, 1990.

N. Brunel and M. C. Van-rossum, Lapicque's 1907 paper: from frogs to integrate-and-fire, Biological Cybernetics, vol.97, issue.5, pp.337-339, 2007.

M. Bunsey and H. Eichenbaum, Conservation of hippocampal memory function in rats and humans, Nature, vol.379, pp.255-257, 1996.

Y. Burak and I. Fiete, Do we understand the emergent dynamics of grid cell activity?, J Neurosci, vol.26, issue.37, pp.9352-9356, 2006.

Y. Burak and I. R. Fiete, Accurate path integration in continuous attractor network models of grid cells, PLoS Comput Biol, vol.5, issue.2, p.1000291, 2009.

N. Burgess, J. G. Donnett, K. J. Jeffery, and O. John, Robotic and neuronal simulation of the hippocampus and rat navigation, Philosophical Transactions of the Royal Society of London B: Biological Sciences, vol.352, pp.1535-1543, 1360.

G. Buzsáki, Cognitive neuroscience: time, space and memory, Nature, vol.497, issue.7451, pp.568-569, 2013.

G. Buzsáki and E. I. Moser, Memory, navigation and theta rhythm in the hippocampal-entorhinal system, Nature neuroscience, vol.16, issue.2, pp.130-138, 2013.

K. Caluwaerts, M. Staffa, N. Grand, C. Dollé, L. Favre-félix et al., A biologically inspired meta-control navigation system for the psikharpax rat robot, Bioinspiration and Biomimetics, vol.7, issue.2, p.25009, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01000945

D. Caplan, C. Baker, and F. Dehaut, Syntactic determinants of sentence comprehension in aphasia, Cognition, vol.21, issue.2, pp.117-175, 1985.

R. Caze, M. Khamassi, L. Aubin, and B. Girard, Hippocampal replays under the scrutiny of reinforcement learning models, Journal of Neurophysiology, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02323528

G. Chevalier and J. Deniau, Disinhibition as a basic process in the expression of striatal functions, Trends Neurosci, vol.13, issue.7, pp.277-280, 1990.

K. R. Chi, P. Churchland, and T. J. Sejnowski, Neural modelling: abstractions of the mind, Nature, vol.531, 1992.

B. J. Clark and J. S. Taube, Vestibular and attractor network basis of the head direction cell signal in subcortical circuits, Front. Neural Circuits, vol.6, issue.7, pp.10-3389, 2012.

A. Cleeremans and J. L. Mcclelland, Learning the structure of event sequences, Journal of experimental psychology. General, vol.120, issue.3, pp.235-253, 1991.

N. Cohen and H. Eichenbaum, Memory, amnesia, and the hippocampal system, 1993.

S. Coombes, Waves, bumps and patterns in neural field theories, Biol. Cybern, vol.93, pp.91-108, 2005.

N. Daw, Y. Niv, and P. Dayan, Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control, Nat Neurosci, vol.8, issue.12, pp.1704-1711, 2005.

P. Dayan and L. F. Abbott, Theoretical neuroscience: computational and mathematical modeling of neural systems, 2001.

G. Detorakis, N. Rougier, and P. , A Neural Field Model of the Somatosensory Cortex: Formation, Maintenance and Reorganization of Ordered Topographic Maps, PLoS ONE, vol.7, issue.7, p.40257, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00716355

G. I. Detorakis and N. P. Rougier, Structure of receptive fields in a computational model of area 3b of primary sensory cortex, Frontiers in Computational Neuroscience, vol.8, p.26, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01052817

I. T. Diamond and W. D. Neff, Ablation of temporal cortex and discrimination of auditory patterns, Journal fo Neurophysiology, p.20, 1957.

L. Dollé, R. Chavarriaga, A. Guillot, and M. Khamassi, Interactions of spatial strategies producing generalization gradient and blocking: A computational approach, PLoS computational biology, vol.14, issue.4, p.1006092, 2018.

P. F. Dominey, Complex sensory-motor sequence learning based on recurrent state representation and reinforcement learning, Biological Cybernetics, vol.73, issue.3, pp.265-74, 1995.

P. F. Dominey, M. A. Arbib, J. , and J. , A Model of Cortico-Striatal Plasticity for Learning Oculomotor Associations and Sequences, Journal of Cognitive Neuroscience, vol.7, issue.3, pp.311-336, 1995.

P. F. Dominey, M. Hoen, J. Blanc, and T. Lelekov-boissard, Neurological basis of language and sequential cognition: Evidence from simulation, aphasia, and {ERP} studies, Brain and Language, vol.86, issue.2, pp.207-225, 2003.

P. F. Dominey, T. Inui, and M. Hoen, Neural network processing of natural language: Ii. towards a unified model of corticostriatal function in learning sentence comprehension and non-linguistic sequencing, Brain and Language, vol.109, issue.2-3, pp.80-92, 2009.

P. F. Dominey, T. Lelekov, J. Ventre-dominey, J. , and M. , Dissociable processes for learning the surface structure and abstract structure of sensorimotor sequences, Journal of Cognitive Neuroscience, vol.10, issue.6, pp.734-51, 1998.
URL : https://hal.archives-ouvertes.fr/hal-00655260

R. Douglas, C. Koch, M. Mahowald, K. Martin, and H. Suarez, Recurrent excitation in neocortical circuits, Science, vol.269, issue.5226, pp.981-985, 1995.

K. Doya, Complementary roles of basal ganglia and cerebellum in learning and motor control, Curr Opin Neurobiol, vol.10, issue.6, pp.732-739, 2000.

K. Doya, Metalearning and neuromodulation, Neural Netw, vol.15, issue.4-6, pp.495-506, 2002.

J. Duncan, An adaptive coding model of neural function in prefrontal cortex, Nature Reviews Neuroscience, vol.2, issue.11, pp.820-829, 2001.

H. Eichenbaum, Time cells in the hippocampus: a new dimension for mapping memories, Nature Reviews Neuroscience, vol.15, issue.11, pp.732-744, 2014.

H. Eichenbaum, T. Otto, and N. Cohen, Two functional components of the hippocampal memory system, Behav. Brain Sci, p.17, 1994.

J. L. Elman, Finding structure in time, Cognitive Science, vol.14, issue.2, pp.179-211, 1990.

J. L. Elman, Distributed representations, simple recurrent networks, and grammatical structure, Machine Learning, vol.7, pp.195-225, 1991.

P. Enel, E. Procyk, R. Quilodran, and P. F. Dominey, Reservoir computing properties of neural dynamics in prefrontal cortex, PLoS Comput Biol, vol.12, issue.6, pp.1-35, 2016.
URL : https://hal.archives-ouvertes.fr/inserm-02141782

D. Foster, R. Morris, and P. Dayan, A model of hippocampally dependent navigation, using the temporal difference learning rule, Hippocampus, vol.10, issue.1, pp.1-16, 2000.

M. J. Frank, Dynamic dopamine modulation in the basal ganglia: a neurocomputational account of cognitive deficits in medicated and nonmedicated parkinsonism, Journal of Cognitive Neuroscience, vol.17, issue.1, pp.51-72, 2005.

M. J. Frank, B. B. Doll, J. Oas-terpstra, and F. Moreno, Prefrontal and striatal dopaminergic genes predict individual differences in exploration and exploitation, Nature neuroscience, vol.12, issue.8, pp.1062-1068, 2009.

Y. Frégnac and G. Laurent, Where is the brain in the human brain project?, Nature, p.513, 2014.

S. Frisch, M. Schlesewsky, D. Saddy, A. , and A. , The {P600} as an indicator of syntactic ambiguity, Cognition, vol.85, issue.3, pp.83-92, 2002.

K. Friston, A Free Energy Principle for Biological Systems, Entropy, vol.14, issue.11, pp.2100-2121, 2012.

B. Fritzke, A growing neural gas network learns topologies, Advances in Neural Information Processing Systems, vol.7, pp.625-632, 1995.

M. C. Fuhs and D. S. Touretzky, A spin glass model of path integration in rat medial entorhinal cortex, Journal of Neuroscience, 2006.

S. Fusi, E. K. Miller, and M. Rigotti, Why neurons mix: high dimensionality for higher cognition, Neurobiology of cognitive behavior, vol.37, pp.66-74, 2016.

J. M. Fuster, Chapter 10 the prefrontal cortex and its relation to behavior, Progress in Brain Research, vol.87, pp.201-211, 1991.

M. Fyhn, S. Molden, M. P. Witter, E. I. Moser, and M. Moser, Spatial representation in the entorhinal cortex, Science, vol.305, issue.5688, pp.1258-1264, 2004.

P. Gaussier, J. Banquet, F. Sargolini, C. Giovannangeli, E. Save et al., A model of grid cells involving extra hippocampal path integration, and the hippocampal loop, Journal of integrative neuroscience, vol.6, issue.03, pp.447-476, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00321060

P. Gaussier, C. Joulain, J. Banquet, S. Leprêtre, and A. Revel, The visual homing problem: an example of robotics/biology cross fertilization. Robotics and autonomous systems, vol.30, pp.155-180, 2000.
URL : https://hal.archives-ouvertes.fr/hal-00426235

P. Gaussier, S. Moga, M. Quoy, and J. Banquet, From perception-action loops to imitation processes: A bottom-up approach of learning by imitation, Applied Artificial Intelligence, vol.12, issue.7-8, pp.701-727, 1998.
URL : https://hal.archives-ouvertes.fr/hal-01416796

P. Gaussier, A. Revel, J. Banquet, and V. Babeau, From view cells and place cells to cognitive map learning: processing stages of the hippocampal system, Biological Cybernetics, vol.86, pp.15-28, 2002.
URL : https://hal.archives-ouvertes.fr/hal-00426240

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

C. Gerfen, C. Wilson, L. Swanson, A. Björklund, and T. Hökfelt, The basal ganglia, Integrated Systems of the CNS, Part III, chapter Chapter II, vol.12, pp.371-468, 1996.

C. Giovannangeli, P. Gaussier, and J. Banquet, Robustness of visual place cells in dynamic indoor and outdoor environment, International Journal of Advanced Robotic Systems, vol.3, issue.2, pp.115-124, 2006.

B. Girard, V. Cuzin, A. Guillot, K. Gurney, P. et al., A basal ganglia inspired model of action selection evaluated in a robotic survival task, Journal of Integrative Neuroscience, vol.2, issue.2, pp.179-200, 2003.
URL : https://hal.archives-ouvertes.fr/hal-00016392

B. Girard, D. Filliat, J. Meyer, A. Berthoz, and A. Guillot, Integration of navigation and action selection functionalities in a computational model of cortico-basal ganglia-thalamo-cortical loops, Adaptive Behavior, vol.13, issue.2, pp.115-130, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00016389

B. Girard, N. Tabareau, Q. Pham, A. Berthoz, and J. Slotine, Where neuroscience and dynamic system theory meet autonomous robotics: a contracting basal ganglia model for action selection, Neural Networks, vol.21, issue.4, pp.628-641, 2008.
URL : https://hal.archives-ouvertes.fr/hal-01524676

M. A. Gluck and C. E. Myers, Hippocampal mediation of stimulus representation: A computational theory, Hippocampus, vol.3, issue.4, pp.491-516, 1993.

P. S. Goldman-rakic, Circuitry of primate prefrontal cortex and regulation of behavior by representational memory, Comprehensive Physiology, 1987.

J. H. Gould, C. G. Cusick, T. P. Pons, and J. H. Kaas, The relationship of corpus callosum connections to electrical stimulation maps of motor, supplementary motor, and frontal eye fields in owl monkeys, Journal of Comparative Neurology, p.247, 1986.

A. Graybiel, The basal ganglia and chunking of action repertoires, Neurobiol Learn Mem, vol.70, issue.1-2, pp.119-136, 1998.

S. Grossberg and J. Merrill, The hippocampus and cerebellum in adaptively timed learning, recognition, and movement, Journal of Cognitive Neuroscience, vol.8, pp.257-277, 1996.

A. Guazzelli, M. Bota, F. J. Corbacho, and M. A. Arbib, Affordances. motivations, and the world graph theory, Adaptive Behavior, vol.6, issue.3-4, pp.435-471, 1998.

K. Gurney, T. Prescott, R. , and P. , A computational model of action selection in the basal ganglia. I. A new functional anatomy, Biological Cybernetic, vol.84, issue.6, pp.401-410, 2001.

K. Gurney, T. Prescott, R. , and P. , A computational model of action selection in the basal ganglia. II. Analysis and simulation of behaviour, Biol Cybern, vol.84, issue.6, pp.411-423, 2001.

M. Guthrie, A. Leblois, A. Garenne, and T. Boraud, Interaction between cognitive and motor cortico-basal ganglia loops during decision making: a computational study, Journal of neurophysiology, vol.109, issue.12, pp.3025-3040, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00828004

S. Haber, J. Fudge, and N. Mcfarland, Striatonigrostriatal pathways in primates form an ascending spiral from the shell to the dorsolateral striatum, J Neurosci, vol.20, issue.6, pp.2369-2382, 2000.

T. Hafting, M. Fyhn, S. Molden, M. Moser, and E. Moser, Microstructure of a spatial map in the entorhinal cortex, Nature, vol.436, pp.801-806, 2005.

P. Hagoort and C. M. Brown, {ERP} effects of listening to speech compared to reading: the p600/sps to syntactic violations in spoken sentences and rapid serial visual presentation, Neuropsychologia, vol.38, issue.11, pp.1531-1549, 2000.

S. Harnard, The symbol grounding problem, Physica D: Nonlinear Phenomena, vol.42, pp.335-346, 1990.

D. Hassabis, D. Kumaran, C. Summerfield, and M. Botvinick, Neuroscience-inspired artificial intelligence, Neuron, vol.95, issue.2, pp.245-258, 2017.

M. E. Hasselmo, E. Schnell, and E. Barkai, Dynamics of learning and recall at excitatory recurrent synapses and cholinergic modulation in rat hippocampal region ca3, The Journal of neuroscience, vol.15, issue.7, pp.5249-5262, 1995.

M. E. Hasselmo, B. P. Wyble, and G. V. Wallenstein, Encoding and retrieval of episodic memories: role of cholinergic and gabaergic modulation in the hippocampus, Hippocampus, vol.6, issue.6, pp.693-708, 1996.

D. Hebb, The Organization of Behavior : A Neuropsychological Theory, 1949.

M. Hermans and B. Schrauwen, Recurrent kernel machines: Computing with infinite echo state networks, Neural Comput, vol.24, issue.1, pp.104-133, 2012.

X. Hinaut and P. F. Dominey, Real-time parallel processing of grammatical structure in the fronto-striatal system: a recurrent network simulation study using reservoir computing, PLoS ONE, vol.8, issue.2, p.52946, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01968923

J. Hirel, P. Gaussier, M. Quoy, J. Banquet, E. Save et al., The hippocampo-cortical loop: spatio-temporal learning and goal-oriented planning in navigation, Neural Networks, vol.43, pp.8-21, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00781988

M. Hoen, M. Pachot-clouard, C. Segebarth, and P. F. Dominey, When Broca experiences the Janus syndrome: an ER-fMRI study comparing sentence comprehension and cognitive sequence processing, Cortex, vol.42, issue.4, pp.605-623, 2006.
URL : https://hal.archives-ouvertes.fr/inserm-00388970

J. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities, Proceedings of the national academy of sciences, vol.79, pp.2554-2558, 1982.

J. Houk, J. Adams, and A. Barto, A model of how the basal ganglia generate and use neural signals that predict reinforcement, Models of Information Processing in the Basal Ganglia, chapter 13, pp.249-270, 1995.

D. Hubel and T. Wiesel, Receptive fields of single neurones in the cat's striate cortex, The Journal of physiology, vol.148, issue.3, pp.574-591, 1959.

D. Hubel and T. Wiesel, Anatomical demonstration of columns in the monkey striate cortex, Nature, p.221, 1969.

D. H. Hubel, T. N. Wiesel, and M. P. Stryker, Anatomical demonstration of orientation columns in macaque monkey, J. Comp. Neur, vol.177, pp.361-380, 1978.

M. Humphries, M. Khamassi, and K. Gurney, Dopaminergic control of the exploration-exploitation trade-off via the basal ganglia, Frontiers in Neuroscience, vol.6, p.9, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00688928

M. Ito and K. Doya, Multiple representations and algorithms for reinforcement learning in the cortico-basal ganglia circuit. Current opinion in neurobiology, vol.21, pp.368-373, 2011.

H. Jaeger, The "echo state" approach to analysing and training recurrent neural networks-with an erratum note, Information Technology GMD Technical Report, vol.148, p.34, 2001.

H. Jaeger and H. Haas, Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication, science, vol.304, issue.5667, pp.78-80, 2004.

A. Jauffret, N. Cuperlier, and P. Gaussier, From grid cells and visual place cells to multimodal place cell: a new robotic architecture, Frontiers in Neurorobotics, vol.9, issue.1, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01148847

D. Joel, Y. Niv, and E. Ruppin, Actor-critic models of the basal ganglia: new anatomical and computational perspectives, Neural Netw, vol.15, issue.4-6, pp.535-547, 2002.

M. I. Jordan, Serial order: A parallel, distributed processing approach, 1986.

J. Kaas, Plasticity of sensory and motor maps in adult mammals, Annual review of neuroscience, issue.1, p.14, 1991.

J. Kaas, M. Merzenich, and H. Killackey, The reorganization of somatosensory cortex following peripheral nerve damage in adult and developing mammals, Annual Review of Neuroscience, vol.6, pp.325-356, 1983.

J. H. Kaas, J. F. Baer, R. E. Weller, and I. Kakoma, Aotus: The owl monkey, chapter The organization of sensory and motor cortex in owl monkeys, 1994.

S. Kaski, J. Jangas, and T. Kohonen, Bibliography of self-organizing map papers: 1981-1997, 1998.

L. C. Katz and C. J. Shatz, Synaptic activity and the construction of cortical circuits, Science, p.274, 1996.

M. Khamassi, B. Girard, A. Clodic, S. Devin, E. Renaudo et al., Integraton of action, joint action and learning in robot cognitive architectures. Intellectica, vol.1, pp.169-203, 2016.

M. Khamassi and M. Humphries, Integrating cortico-limbic-basal ganglia architectures for learning model-based and model-free navigation strategies, Frontiers in Behavioral Neuroscience, vol.6, p.79, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01219958

M. Khamassi, L. Lacheze, B. Girard, A. Berthoz, and A. Guillot, Actorcritic models of reinforcement learning in the basal ganglia: from natural to arificial rats, Adaptive Behavior, vol.13, pp.131-148, 2005.

M. Khamassi, L. Martinet, and A. Guillot, Combining self-organizing maps with mixtures of experts: application to an actor-critic model of reinforcement learning in the basal ganglia, From Animals to Animats 9, vol.4095, pp.394-405, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00688933

M. Khamassi, A. Mulder, E. Tabuchi, V. Douchamps, and S. Wiener, , 2008.

, Anticipatory reward signals in ventral striatal neurons of behaving rats, European Journal of Neuroscience, vol.28, pp.1849-1866

M. Khamassi, G. Velentzas, T. Tsitsimis, and C. Tzafestas, Robot fast adaptation to changes in human engagement during simulated dynamic social interaction with active exploration in parameterized reinforcement learning, IEEE Transactions on Cognitive and Developmental Systems, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02324064

J. J. Kim, R. E. Clark, and R. F. Thompson, Hippocampectomy impairs the memory of recently, but not remotely, acquired trace eyeblink conditioned responses, Behavioral neuroscience, vol.109, issue.2, p.195, 1995.

J. Kober, J. A. Bagnell, and J. Peters, Reinforcement learning in robotics: A survey, The International Journal of Robotics Research, pp.1238-1274, 2013.

E. Koechlin, C. Ody, and F. Kouneiher, The Architecture of Cognitive Control in the Human Prefrontal Cortex, Science, vol.302, issue.5648, pp.1181-1185, 2003.

T. Kohonen, Self-organized formation of topologically correct feature maps, Biological Cybernetics, p.43, 1982.

B. J. Kraus, R. J. Robinson, J. A. White, H. Eichenbaum, and M. E. Hasselmo, Hippocampal "time cells": time versus path integration, Neuron, vol.78, issue.6, pp.1090-1101, 2013.

J. L. Krichmar, A. K. Seth, D. A. Nitz, J. G. Fleischer, and G. M. Edelman, Spatial navigation and causal analysis in a brain-based device modeling corticalhippocampal interactions, Neuroinformatics, vol.3, issue.3, pp.197-221, 2005.

R. Lemon, An enduring map of the motor cortex, Experimental Physiology, vol.93, pp.798-802, 2008.

S. Leyton and C. Sherrington, Observations on the excitable cortex of the chimpanzee, orang-utan and gorilla, Quaterly Journal of Experimntal Physiology, vol.11, pp.135-222, 1917.

Y. Linde, A. Buzo, and R. Gray, An algorithm for vector quantization design, IEEE Trans. on Communications, 1980.

M. Lukosevicius and H. Jaeger, Survey: Reservoir computing approaches to recurrent neural network training, Comput. Sci. Rev, vol.3, issue.3, pp.127-149, 2009.

W. Maass, P. Joshi, and E. D. Sontag, Computational aspects of feedback in neural circuits, PLoS Comput Biol, vol.3, issue.1, pp.1-20, 2007.

W. Maass, T. Natschläger, and H. Markram, Real-time computing without stable states: A new framework for neural computation based on perturbations, Neural Comput, vol.14, issue.11, pp.2531-2560, 2002.

J. B. Macqueen, Some methods of classification and analysis of multivariate observations, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp.281-297, 1967.

H. Markram, E. Muller, S. Ramaswamy, M. W. Reimann, M. Abdellah et al., Reconstruction and Simulation of Neocortical Microcircuitry, vol.163, pp.456-492, 2015.

D. Marr, Vision: A Computational Investigation into the Human Representation and Processing of Visual Information, 1982.

D. Marr, D. Willshaw, and B. Mcnaughton, Simple memory: a theory for archicortex, Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, vol.262, pp.23-81, 1971.

W. H. Marshall, C. N. Woolsey, and R. Bard, Cortical representation of tactile sensibility as indicated by cortical potentials, Science, p.85, 1937.

T. M. Martinetz, S. G. Berkovich, and K. J. Schulten, Neural-gas network for vector quantization and its application to time-series prediction, IEEE Trans. on Neural Networks, vol.4, issue.4, pp.558-569, 1993.

M. J. Mataric, Navigating with a rat brain: a neurobiologically inspired model, From Animals to Animats; Proceedings of the First International Conference on Simulation of Adaptive Behavior, 1991.

J. L. Mcclelland, B. L. Mcnaughton, and R. C. Reilly, Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory, Psychological review, vol.102, issue.3, p.419, 1995.

W. Mcculloch and W. Pitts, A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics, vol.5, pp.115-133, 1943.

B. Mcnaughton, C. Barnes, J. Gerrard, K. Gothard, M. Jung et al., Deciphering the hippocampal polyglot: the hippocampus as a path integration system, The Journal of Experimental Biology, vol.199, issue.1, pp.173-185, 1996.

B. Mcnaughton, F. P. Battaglia, O. Jensen, E. I. Moser, and M. Moser, Path integration and the neural basis of the 'cognitive map, Nat Rev Neurosci, vol.7, pp.663-678, 2006.

B. L. Mcnaughton and R. G. Morris, Hippocampal synaptic enhancement and information storage within a distributed memory system, Trends in neurosciences, vol.10, pp.408-415, 1987.

M. Merzenich and J. Kaas, Reorganization of mammalian somatosensory cortex following peripheral nerve injury, Trends in Neurosciences, vol.5, pp.434-436, 1982.

M. M. Merzenich, J. H. Kaas, J. M. Sprague, and A. Epstein, Progress in psychobiology and physiological psychology, chapter Principles of organization of sensory-perceptual systems in mammals, 1980.

M. J. Milford and G. F. Wyeth, Mapping a suburb with a single camera using a biologically inspired slam system, IEEE Transactions on Robotics, vol.24, issue.5, pp.1038-1053, 2008.

M. J. Milford, G. F. Wyeth, and D. Rasser, Ratslam: a hippocampal model for simultaneous localization and mapping, Proceedings. ICRA'04. 2004 IEEE International Conference on, vol.1, pp.403-408, 2004.

E. K. Miller and J. D. Cohen, An integrative theory of prefrontal cortex function, Annu. Rev. Neurosci, vol.24, pp.167-202, 2001.

J. W. Mink, The basal ganglia: focused selection and inhibition of competing motor programs, Progress in neurobiology, vol.50, issue.4, pp.381-425, 1996.

G. Morris, A. Nevet, D. Arkadir, E. Vaadia, and H. Bergman, Midbrain dopamine neurons encode decisions for future action, Nat Neurosci, vol.9, issue.8, pp.1057-1063, 2006.

R. Morris, P. Garrud, J. Rawlins, and J. Keefe, Place navigation impaired in rats with hippocampal lesions, Nature, vol.297, p.24, 1982.

Y. Naya and W. A. Suzuki, Integrating what and when across the primate medial temporal lobe, Science, vol.333, issue.6043, pp.773-776, 2011.

M. Nelson and J. Rinzel, Bower and Beeman. The book of Genesis, vol.4, pp.27-51, 1995.

S. N'guyen, C. Thurat, and B. Girard, Saccade learning with concurrent cortical and subcortical basal ganglia loops, Frontiers in Computational Neuroscience, vol.8, p.48, 2014.

Y. Niv, N. Daw, D. Joel, and P. Dayan, Tonic dopamine: opportunity costs and the control of response vigor, Psychopharmacology (Berl), vol.191, issue.3, pp.507-520, 2007.

M. Oja, S. Kaski, and T. Kohonen, Bibliography of self-organizing map papers: 1998-2001 addendum, 2003.

J. O'keefe and J. Dostrovsky, 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.

J. O'keefe and N. Nadel, The hippocampus as a cognitive map, 1978.

R. C. O'reilly and M. J. Frank, Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia, Neural Comp, vol.18, issue.2, pp.283-328, 2006.

R. C. O'reilly, S. A. Herd, and W. M. Pauli, Computational models of cognitive control, Current Opinion in Neurobiology, vol.20, issue.2, 2010.

R. C. O'reilly and J. W. Rudy, Computational principles of learning in the neocortex and hippocampus, Hippocampus, vol.10, issue.4, pp.389-397, 2000.

S. Palminteri, M. Khamassi, M. Joffily, and G. Coricelli, Medial prefrontal cortex and the adaptive regulation of reinforcement learning parameters, Nature Communications, vol.6, p.8096, 2015.

B. A. Pearlmutter, Gradient calculations for dynamic recurrent neural networks: a survey, IEEE Trans Neural Netw, vol.6, issue.5, pp.1212-1228, 1995.

W. Penfield and E. Boldrey, Somatic motor and sensory representation in the cerebral cortex as studied by electrical stimulation, Brain, p.60, 1937.

R. Pfeifer, J. Bongard, and S. Grand, How the body shapes the way we think: a new view of intelligence, 2007.

B. E. Pfeiffer and D. J. Foster, Hippocampal place-cell sequences depict future paths to remembered goals, Nature, vol.497, issue.7447, pp.74-79, 2013.

T. Prescott, P. Redgrave, and K. Gurney, Layered control architectures in robots and vertebrates, Adaptive Behavior, vol.7, pp.99-127, 1999.

G. J. Quirk, R. U. Muller, J. L. Kubie, and J. Ranck, The positional firing properties of medial entorhinal neurons: description and comparison with hippocampal place cells, The Journal of Neuroscience, vol.12, issue.5, pp.1945-1963, 1992.

P. Redgrave, T. Prescott, and K. Gurney, The basal ganglia: a vertebrate solution to the selection problem?, Neuroscience, vol.89, issue.4, pp.1009-1023, 1999.

A. D. Redish and D. S. Touretzky, Cognitive maps beyond the hippocampus, Hippocampus, vol.7, issue.1, pp.15-35, 1997.

E. Renaudo, B. Girard, R. Chatila, and M. Khamassi, Design of a control architecture for habit learning in robots, Biomimetic and Biohybrid Systems, Third International Conference, Living Machines, pp.249-260, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01312443

E. Renaudo, B. Girard, R. Chatila, and M. Khamassi, Respective advantages and disadvantages of model-based and model-free reinforcement learning in a robotics neuro-inspired cognitive architecture, Procedia Computer Science, vol.71, pp.178-184, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01250157

R. Rescorla and A. Wagner, A theory of pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement, Classical Conditioning II: Current Research and Theory, pp.64-99, 1972.

A. Revel, P. Gaussier, S. Lepretre, and J. Banquet, Planification versus sensory-motor conditioning: what are the issues ?, SAB'98: From animals to animats 5, pp.129-138, 1998.

J. Reynolds, B. Hyland, and J. Wickens, A cellular mechanism of rewardrelated learning, Nature, vol.413, issue.6851, pp.67-70, 2001.

M. Rigotti, O. Barak, M. R. Warden, X. J. Wang, N. D. Daw et al., The importance of mixed selectivity in complex cognitive tasks, Nature, vol.497, issue.7451, pp.585-590, 2013.

M. Roesch, D. Calu, and G. Schoenbaum, Dopamine neurons encode the better option in rats deciding between differently delayed or sized rewards, Nat Neurosci, vol.10, issue.12, pp.1615-1624, 2007.

E. T. Rolls and S. M. Mara, View-responsive neurons in the primate hippocampal complex, Hippocampus, vol.5, issue.5, pp.409-424, 1995.

N. P. Rougier and Y. Boniface, Dynamic Self-Organising Map, Neurocomputing, vol.74, issue.11, pp.1840-1847, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00495827

S. Russell and P. Norvig, Artificial Intelligence -A Modern Approach, 2003.

A. Samsonovich and B. L. Mcnaughton, Path integration and cognitive mapping in a continuous attractor neural network model, Journal of Neuroscience, 1997.

T. Schaul, J. Quan, I. Antonoglou, and D. Silver, Prioritized experience replay, 2015.

N. Schmajuk, A neural network approach to hippocampal function in classical conditioning, Behavioral Neuroscience, vol.105, issue.1, pp.82-110, 1991.

N. Schmajuk and A. Thieme, Purposive behavior and cognitive mapping: a neural network model, Biological Cybernetic, vol.67, pp.165-174, 1992.

W. Schultz, P. Dayan, M. , and P. , A neural substrate of prediction and reward, Science, vol.275, issue.5306, pp.1593-1599, 1997.

E. L. Schwartz, Computational Neuroscience, 1990.

W. B. Scoville and B. Milner, Loss of recent memory after bilateral hippocampal lesions, Journal of neurology, vol.20, issue.1, p.11, 1957.

J. R. Searle, Minds, brains, and programs, Behavioral and Brain Sciences, vol.3, pp.417-457, 1980.

D. Servan-schreiber, H. Printz, and J. Cohen, A network model of catecholamine effects: gain, signal-to-noise ratio, and behavior, Science, vol.249, issue.4971, pp.892-895, 1990.

P. E. Sharp, Comparison of the timing of hippocampal and subicular spatial signals: implications for path integration, Hippocampus, vol.9, issue.2, pp.158-172, 1999.

M. Stephenson-jones, E. Samuelsson, J. Ericsson, B. Robertson, and S. Grillner, Evolutionary conservation of the basal ganglia as a common vertebrate mechanism for action selection, Current Biology, vol.21, issue.13, pp.1081-1091, 2011.

R. Sun and F. Alexandre, Connectionist-Symbolic Integration : From Unified to Hybrid Approaches, 1997.

R. Sutton and A. Barto, Reinforcement Learning: An Introduction. Cambridge, 1998.

M. Tessier-lavigne and C. S. Goodman, The molecular biology of axon guidance, Science, p.274, 1996.

T. J. Teyler and P. Discenna, The hippocampal memory indexing theory, Behavioral neuroscience, vol.100, issue.2, p.147, 1986.

R. F. Thompson, The neurobiology of learning and memory, Science, vol.233, issue.4767, pp.941-947, 1986.

E. Tolman, Cognitive maps in rats and men, The Psychological Review, vol.55, issue.4, pp.189-208, 1948.

D. S. Touretzky and A. D. Redish, Theory of rodent navigation based on interacting representations of space, Hippocampus, vol.6, issue.3, pp.247-270, 1996.

A. Treves and E. Rolls, Computational analysis of the role of the hippocampus in memory, Hippocampus, vol.4, issue.3, pp.374-391, 1994.

H. Uylings, H. Groenewegen, and B. Kolb, Do rats have a prefrontal cortex?, Behav Brain Res, vol.146, issue.1-2, pp.3-17, 2003.

M. Van-der-meer, Z. Kurth-nelson, and A. D. Redish, Information processing in decision-making systems, The Neuroscientist, vol.18, issue.4, pp.342-359, 2012.

P. Voorn, L. Vanderschuren, H. Groenewegen, T. Robbins, and C. Pennartz, Putting a spin on the dorsal-ventral divide of the striatum, Trends Neurosci, vol.27, issue.8, pp.468-474, 2004.

H. Wan, D. Touretzky, A. ;. Redish, P. Smolensky, D. Touretzky et al., Towards a computational theory of rat navigation, Proc. of the, pp.11-19, 1993.

J. X. Wang, Z. Kurth-nelson, D. Tirumala, H. Soyer, J. Z. Leibo et al., Learning to reinforcement learn, 2016.

L. Wang, X. Li, S. S. Hsiao, F. A. Lenz, M. Bodner et al., Differential roles of delay-period neural activity in the monkey dorsolateral prefrontal cortex in visual-haptic crossmodal working memory, Proc. Natl. Acad. Sci. U.S.A, vol.112, issue.2, pp.214-219, 2015.

D. J. Willshaw and C. Malsburg, How patterned neural connections can be set up by self-organization, Proceedings of the Royal Society London B, 0194.

D. L. Yamins and J. J. Dicarlo, Using goal-driven deep learning models to understand sensory cortex, Nature Neuroscience, vol.19, issue.3, pp.356-365, 2016.

H. Yin and B. Knowlton, The role of the basal ganglia in habit formation, Nat Rev Neurosci, vol.7, issue.6, pp.464-476, 2006.

D. Zipser, A computational model of hippocampal place fields, Behavioral neuroscience, vol.99, issue.5, pp.1006-1018, 1985.