R. A. Adams, K. E. Stephan, H. R. Brown, . Christopher-d-frith, and . Friston, The Computational Anatomy of Psychosis, Frontiers in Psychiatry, vol.4, p.47, 2013.

A. Alvernhe, F. Sargolini, and B. Poucet, Rats Build and Update Topological Representations through Exploration, Animal Cognition, vol.15, issue.3, pp.359-68, 2012.
DOI : 10.1007/s10071-011-0460-z
URL : https://hal.archives-ouvertes.fr/hal-01384122

,

R. Ambrose, B. E. Ellen, D. J. Pfeiffer, and . Foster, Reverse Replay of Hippocampal Place Cells Is Uniquely Modulated by Changing Reward, Neuron, vol.91, issue.5, pp.1124-1160, 2016.

,

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.287-99, 2000.

,

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.

K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. Bharath, Deep Reinforcement Learning: A Brief Survey, IEEE Signal Processing Magazine, 2017.

D. Ball, S. Heath, M. Milford, G. Wyeth, and J. Wiles, A Navigating Rat Animat, Proceedings of the 12th International Conference on the Synthesis and Simulation of Living Systems, vol.40, pp.271-83, 2010.

A. Barrera, G. Tejera, M. Llofriu, and A. Weitzenfeld, Learning Spatial Localization: From Rat Studies to Computational Models of the Hippocampus, Spatial Cognition & Computation, vol.15, issue.1, pp.27-59, 2015.

A. Barrera and A. Weitzenfeld, Biologically-Inspired Robot Spatial Cognition Based on Rat Neurophysiological Studies, Autonomous Robots, vol.25, issue.1-2, pp.147-69, 2008.

,

F. C. Bartett, A Study in Experimental and Social Psychology, 1932.

L. E. Baum and T. Petrie, Statistical Inference for Probabilistic Functions of Finite State Markov Chains, The Annals of Mathematical Statistics, vol.37, issue.6, pp.1554-63, 1966.

D. Bendor and M. Wilson, Biasing the Content of Hippocampal Replay during Sleep, Nature Neuroscience, vol.15, issue.10, pp.1439-1483, 2012.

N. Burgess, M. Recce, and J. Keefe, A Model of Hippocampal Function, Neural Networks, vol.7, issue.6-7, pp.80159-80164, 1994.

K. Caluwaerts, M. Staffa, S. Nguyen, C. Grand, L. Dollé et al., A Biologically Inspired Meta-Control Navigation System for the Psikharpax Rat Robot, Bioinspiration and Biomimetics, vol.7, issue.2, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01000945

M. F. Carr, P. Shantanu, L. Jadhav, and . Frank, Hippocampal Replay in the Awake State: A Potential Substrate for Memory Consolidation and Retrieval, Nature Neuroscience, 2011.

L. A. Cenquizca and L. W. Swanson, Spatial Organization of Direct Hippocampal Field CA1 Axonal Projections to the Rest of the Cerebral Cortex, Brain Research Reviews, 2007.

D. Chen, C. Y. Jun, C. Lee, P. Park, and . Mendes, Parallelizing Simulated Annealing Algorithms Based on High-Performance Computer, Journal of Global Optimization, vol.39, issue.2, pp.261-89, 2007.

J. R. Chumbley, J. Raymond, . Dolan, and . Friston, Attractor Models of Working Memory and Their Modulation by Reward, Biological Cybernetics, vol.98, issue.1, pp.11-18, 2008.

V. Cutsuridis and M. Hasselmo, Spatial Memory Sequence Encoding and Replay During Modeled Theta and Ripple Oscillations, Cognitive Computation, vol.3, issue.4, pp.554-74, 2011.
DOI : 10.1007/s12559-011-9114-3

,

V. Cutsuridis and J. Taxidis, Deciphering the Role of CA1 Inhibitory Circuits in Sharp Wave-Ripple Complexes, Frontiers in Systems Neuroscience, vol.7, p.13, 2013.

T. J. Davidson, F. Kloosterman, and M. Wilson, Hippocampal Replay of Extended Experience, Neuron, vol.63, issue.4, pp.497-507, 2009.

B. Delatour and M. Witter, Projections from the Parahippocampal Region to the Prefrontal Cortex in the Rat: Evidence of Multiple Pathways, The European Journal of Neuroscience, vol.15, issue.8, pp.1400-1407, 2002.

K. Diba and G. Buzsáki, Forward and Reverse Hippocampal Place Cell Sequences during Ripples SUPPL, Nature Neuroscience, vol.10, pp.1241-1283, 2007.

, Forward and Reverse Hippocampal Place-Cell Sequences during Ripples, Nature Neuroscience, vol.10, issue.10, pp.1241-1283, 2007.

L. Dollé, D. Sheynikhovich, B. Girard, R. Chavarriaga, and A. Guillot, Path Planning versus Cue Responding: A Bio-Inspired Model of Switching between Navigation Strategies, Biological Cybernetics, vol.103, issue.4, pp.299-317, 2010.

P. Dominey and . Ford, A Shared System for Learning Serial and Temporal Structure of Sensori-Motor Sequences? Evidence from Simulation and Human Experiments, Influences of Temporal Organization on Sequence Learning and Transfer: Comments on Stadler, vol.73, issue.73, pp.234-282, 1993.

P. Dominey, M. Ford, J. Arbib, and . Joseph, A Model of Corticostriatal Plasticity for Learning Oculomotor Associations and Sequences, Journal of Cognitive Neuroscience, vol.7, issue.3, pp.311-347, 1995.

P. Dominey, T. Ford, M. Inui, and . Hoen, Neural Network Processing of Natural Language: II. Towards a Unified Model of Corticostriatal Function in Learning Sentence Comprehension and NonLinguistic Sequencing, Brain and Language, vol.109, issue.2-3, pp.80-92, 2009.

,

P. Dominey, F. Ford, and . Ramus, Neural Network Processing of Natural Language: I. Sensitivity to Serial, Temporal and Abstract Structure of Language in the Infant, Language and Cognitive Processes, vol.15, issue.1, pp.87-127, 2000.
URL : https://hal.archives-ouvertes.fr/hal-00260032

M. Dorigo and L. Gambardella, Ant Colonies for the Travelling Salesman Problem, Bio Systems, vol.43, issue.2, pp.1708-1713, 1997.

R. Dugad and U. Desai, A Tutorial on Hidden Markov Models, pp.1-16, 1996.

M. J. Edwards, H. Adams, I. Brown, . Pareés, and . Friston, A Bayesian Account of 'Hysteria', Brain : A Journal of Neurology, vol.135, pp.3495-3512, 2012.

,

H. Eichenbaum, Time Cells in the Hippocampus: A New Dimension for Mapping Memories, Nature Reviews Neuroscience, 2014.

P. Enel, E. Procyk, R. Quilodran, and P. F. Dominey, Dynamical Mixed Selectivity in Reservoir Computing and Primate Prefrontal Cortex

D. R. Euston, M. Tatsuno, and B. Mcnaughton, Fast-Forward Playback of Recent Memory Sequences in Prefrontal Cortex during Sleep, Science, vol.318, issue.5853, pp.1147-50, 2007.

D. Filliat and J. Meyer, Global Localization and Topological Map-Learning for Robot Navigation, Proceedings of the Seventh International Conference on Simulation of Adaptive Behavior on From Animals to Animats, pp.131-171, 2002.
URL : https://hal.archives-ouvertes.fr/hal-00655477

S. Fine, Y. Singer, and N. Tishby, Hierarchical Hidden Markov Model: Analysis and Applications, Machine Learning, vol.32, issue.1, pp.41-62, 1998.

D. J. Foster and M. A. Wilson, Reverse Replay of Behavioural Sequences in Hippocampal Place Cells during the Awake State, Nature, vol.440, issue.7084, pp.680-83, 2006.

D. Foster, J. , and J. Knierim, Sequence Learning and the Role of the Hippocampus in Rodent Navigation, Current Opinion in Neurobiology, vol.22, issue.2, pp.294-300, 2012.

,

K. Friston, The Free-Energy Principle: A Rough Guide to the Brain?, Neural Networks : The Official Journal of the International Neural Network Society, vol.16, issue.9, pp.293-301, 2003.

K. Friston, M. Breakspear, and G. Deco, Perception and Self-Organized Instability, Frontiers in Computational Neuroscience, vol.6, p.44, 2012.

K. Friston, T. Fitzgerald, F. Rigoli, P. Schwartenbeck, J. Odoherty et al., Active Inference and Learning, Neuroscience & Biobehavioral Reviews, vol.68, pp.862-79, 2016.

K. J. Friston, R. Rosch, T. Parr, C. Price, and H. Bowman, Deep Temporal Models and Active Inference, Neuroscience & Biobehavioral Reviews, vol.77, pp.388-402, 2017.

K. J. Friston, J. Daunizeau, and S. J. Kiebel, Reinforcement Learning or Active Inference?, PloS One, vol.4, issue.7, p.6421, 2009.

P. Gaussier, A. Revel, J. P. Banquet, and V. Babeau, 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.
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.2-4, pp.52-58, 1995.

F. Glover and R. Martí, Tabu Search, Tabu Search, pp.1-16, 1986.
URL : https://hal.archives-ouvertes.fr/hal-01389283

D. E. Goldberg and J. H. Holland, Genetic Algorithms and Machine Learning, Machine Learning, 1988.

C. Goller and A. Kuchler, Learning Task-Dependent Distributed Representations By\nbackpropagation through Structure, Proceedings of International Conference on Neural Networks (ICNN'96) 1, 1996.

K. Greff, S. Van-steenkiste, and J. Schmidhuber, Neural Expectation Maximization, 2017.

A. Guazzelli, F. J. Corbacho, M. Bota, and M. A. Arbib, Affordances. Motivations, and the World Graph Theory, Adaptive Behavior, vol.6, issue.3/4, pp.435-71, 1998.

,

A. S. Gupta, A. A. Matthijs, D. S. Van-der-meer, A. Touretzky, and . David-redish, Hippocampal Replay Is Not a Simple Function of Experience, Neuron, vol.65, issue.5, pp.695-705, 2010.

D. O. Hebb, The Organization of Behaviour, 1949.

H. Hendriks-jansen, Catching Ourselves in the Act : Situated Activity, Interactive Emergence, Evolution, and Human Thought, Complex Adaptive Systems, 1996.

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. P. 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

K. L. Hoffman and B. L. Mcnaughton, Coordinated Reactivation of Distributed Memory Traces in Primate Neocortex, Science, vol.297, issue.5589, pp.2070-73, 2002.

J. Holland and H. Reitman, Cognitive Systems Based on Adaptive Algorithms, ACM SIGART Bulletin, issue.63, p.49, 1977.

S. P. Jadhav, P. Caleb-kemere, L. M. Walter-german, and . Frank, Awake Hippocampal Sharp-Wave Ripples Support Spatial Memory, Science, vol.336, issue.6087, pp.1454-58, 2012.

,

H. Jaeger, The 'Echo State' Approach to Analysing and Training Recurrent Neural Networkswith an Erratum Note, Controlling Recurrent Neural Networks by Conceptors Controlling Recurrent Neural NetWorks by Conceptors, vol.148, 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.

,

D. Ji and M. Wilson, Coordinated Memory Replay in the Visual Cortex and Hippocampus during Sleep, Nature Neuroscience, vol.10, issue.1, pp.100-107, 2007.

A. Johnson and D. Crowe, Revisiting Tolman, His Theories and Cognitive Maps, Cognitive Critique, vol.1, pp.43-72, 2008.

L. W. Jong, B. De, G. M. Gereke, J. M. Martin, and . Fellous, The Traveling Salesrat: Insights into the Dynamics of Efficient Spatial Navigation in the Rodent, Journal of Neural Engineering, vol.8, issue.6, 2011.

D. Jurafsky and J. Martin, Hidden Markov Models, Speech and Language Processing, 2017.

M. P. Karlsson and L. Frank, Awake Replay of Remote Experiences in the Hippocampus, Nature Neuroscience, vol.12, issue.7, pp.913-931, 2009.

S. J. Kiebel, J. Katharina-von-kriegstein, . Daunizeau, and . Friston, Recognizing Sequences of Sequences, PLoS Computational Biology, vol.5, issue.8, 2009.

S. Kirkpatrick, C. Gelatt, and M. Vecchi, Optimization by Simulated Annealing, Science, vol.220, issue.4598, pp.671-80, 1983.

H. S. Kudrimoti, C. Barnes, and B. Mcnaughton, Reactivation of Hippocampal Cell Assemblies: Effects of Behavioral State, Experience, and EEG Dynamics, The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, vol.19, issue.10, pp.4090-4101, 1999.

C. S. Lansink, J. V. Pieter-m-goltstein, . Lankelma, N. Ruud, . Joosten et al., Behavioral/Systems/Cognitive Preferential Reactivation of Motivationally Relevant Information in the Ventral Striatum, 2018.

A. K. Lee and M. A. Wilson, Memory of Sequential Experience in the Hippocampus during Slow Wave Sleep, Neuron, vol.36, issue.6, pp.1096-1102, 2002.

M. Luko?evi?ius, A Practical Guide to Applying Echo State Networks, Neural Networks: Tricks of the Trade, 2012.

M. Luko?evi?ius and H. Jaeger, Reservoir Computing Approaches to Recurrent Neural Network Training, Computer Science Review, vol.3, pp.127-176, 2009.

S. Ma and C. Ji, Fast Training of Recurrent Networks Based on the EM Algorithm, IEEE Transactions on Neural Networks, vol.9, issue.1, pp.11-26, 1998.

W. Maass, T. Natschläger, H. Markram, and W. Maass, Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations, Neural Computation, vol.14, issue.11, pp.2531-60, 2002.

A. A. Markov, Essai d'une Recherche Statistique Sur Le Texte Du Roman 'Eugène Oneguine, Bull. Acad. Imper. Sci. St. Petersburg, vol.7, 1913.

D. Marr, A Simple Theory for Archicortex, Philosophical Transactions of the Royal Society B: Biological Sciences, 1971.

J. L. Mcclelland, R. Bruce-l-mcnaughton, and . Reilly, Why There Are Complementary Learning Systems in the Hippocampus and Neocortex: Insights Form the Successes and Failures of Connectionist Models of Learning and Memory, Psychological Review, vol.102, issue.3, pp.419-57, 1995.

M. Milford and G. Wyeth, Persistent Navigation and Mapping Using a Biologically Inspired Slam System, International Journal of Robotics Research, vol.29, issue.9, pp.1131-53, 2010.

M. Nádasdy, H. Zoltán, A. Hirase, J. Czurkó, G. Csicsvari et al., Replay and Time Compression of Recurring Spike Sequences in the Hippocampus, The Journal of Neuroscience, vol.19, issue.21, pp.9497-9507, 1999.

D. Nikoli?, S. Häusler, W. Singer, and W. Maass, Distributed Fading Memory for Stimulus Properties in the Primary Visual Cortex, PLoS Biology, vol.7, issue.12, p.1000260, 2009.

K. Pearson, Mathematical Contributions to the Theory of Evolution. III. Regression, Heredity, and Panmixia, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.187, issue.0, pp.253-318, 1896.

C. M. Pennartz, The Ventral Striatum in Off-Line Processing: Ensemble Reactivation during Sleep and Modulation by Hippocampal Ripples, Journal of Neuroscience, vol.24, issue.29, pp.6446-56, 2004.

A. Peyrache, M. Khamassi, K. Benchenane, S. I. Wiener, and F. P. Battaglia, Replay of Rule-Learning Related Neural Patterns in the Prefrontal Cortex during Sleep, Nature Neuroscience, vol.12, issue.7, pp.919-945, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00551868

G. Pezzulo, C. Kemere, A. A. Matthijs, and . Van-der-meer, Internally Generated Hippocampal Sequences as a Vantage Point to Probe Future-Oriented Cognition, Annals of the New York Academy of Sciences, 2017.

G. Pezzulo, A. A. Matthijs, C. S. Van-der-meer, . Lansink, M. A. Cyriel et al., Internally Generated Sequences in Learning and Executing Goal-Directed Behavior, Trends in Cognitive Sciences, vol.18, issue.12, pp.647-57, 2014.

G. Pezzulo, F. Rigoli, and K. J. Friston, Hierarchical Active Inference: A Theory of Motivated Control, Trends in Cognitive Sciences, 1967.

. Adams-&-company,

D. Popa, S. Duvarci, A. T. Popescu, C. Léna, and D. Paré, Coherent Amygdalocortical Theta Promotes Fear Memory Consolidation during Paradoxical Sleep, Proceedings of the National Academy of Sciences of the United States of America, vol.107, pp.6516-6535, 2010.

A. R. Preston and H. Eichenbaum, Interplay of Hippocampus and Prefrontal Cortex in Memory, Current Biology, 2013.

G. V. Puskorius and L. A. Feldkamp, Neurocontrol of Nonlinear Dynamical Systems with Kalman Filter Trained Recurrent Networks, IEEE Transactions on Neural Networks, vol.5, issue.2, pp.279-97, 1994.

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

S. Ribeiro, D. Gervasoni, E. S. Soares, Y. Zhou, S. Lin et al., Long-Lasting Novelty-Induced Neuronal Reverberation during Slow-Wave Sleep in Multiple Forebrain Areas, PLoS Biology, vol.2, issue.1, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00113898

M. Rigotti, O. Barak, X. Melissa-r-warden, N. D. Wang, . Daw et al., The Importance of Mixed Selectivity in Complex Cognitive Tasks, Nature, vol.497, issue.7451, pp.585-90, 2013.

M. Rigotti, D. Ben-dayan-rubin, X. Wang, and S. Fusi, Internal Representation of Task Rules by Recurrent Dynamics: The Importance of the Diversity of Neural Responses, Frontiers in Computational Neuroscience, vol.4, p.24, 2010.

T. D. Sanger, Optimal Unsupervised Learning in a Single-Layer Linear Feedforward Neural Network, Neural Networks, vol.2, issue.6, pp.459-73, 1989.

D. L. Schacter, R. L. Donna-rose-addis, and . Buckner, Episodic Simulation of Future Events, Annals of the New York Academy of Sciences, vol.1124, issue.1, pp.39-60, 2008.

C. Schwindel, B. L. Daniela, and . Mcnaughton, Hippocampal-Cortical Interactions and the Dynamics of Memory Trace Reactivation, Progress in Brain Research, vol.193, pp.163-77, 2011.

P. E. Sharp, T. Hugh, M. Blair, and . Brown, Neural Network Modeling of the Hippocampal Formation Spatial Signals and Their Possible Role in Navigation: A Modular Approach, Hippocampus, vol.6, issue.6, pp.720-754, 1996.

J. D. Shin and S. P. Jadhav, Multiple Modes of Hippocampal-prefrontal Interactions in Memory-Guided Behavior, Current Opinion in Neurobiology, 2016.

A. C. Singer and L. M. Frank, Rewarded Outcomes Enhance Reactivation of Experience in the Hippocampus, Neuron, vol.64, issue.6, pp.910-931, 2009.

K. E. Stephan, O. Andreea, S. Diaconescu, and . Iglesias, Bayesian Inference, Dysconnectivity and Neuromodulation in Schizophrenia, Brain, 2016.

K. Stephan, W. D. Enno, R. J. Penny, E. M. Moran, . Den-ouden et al., Ten Simple Rules for Dynamic Causal Modeling, NeuroImage, vol.49, issue.4, pp.3099-3109, 2010.

R. Stickgold and M. P. Walker, Sleep-Dependent Memory Consolidation and Reconsolidation, Sleep Medicine, vol.8, issue.4, pp.331-374, 2007.

G. R. Sutherland and B. Mcnaughton, Memory Trace Reactivation in Hippocampal and Neocortical Neuronal Ensembles, Current Opinion in Neurobiology, vol.10, issue.2, pp.79-88, 2000.

I. Sutskever, . Sutton, S. Richard, and A. Barto, Training Recurrent Neural Networks, vol.101, 1998.

J. Tani and S. Nolfi, Learning to Perceive the World as Articulated: An Approach for Hierarchical Learning in Sensory-Motor Systems, Neural Networks, vol.12, issue.7-8, pp.1131-1172, 1999.

, , p.60

M. Tatsuno, P. Lipa, and B. L. Mcnaughton, Methodological Considerations on the Use of Template Matching to Study Long-Lasting Memory Trace Replay, Journal of Neuroscience, vol.26, issue.42, pp.10727-10769, 2006.

E. C. Tolman, Cognitive Maps in Rats and Men, Psychological Review, vol.55, issue.4, pp.189-208, 1948.

J. Unkelbach, S. Yi, and J. Schmidhuber, An EM Based Training Algorithm for Recurrent Neural Networks, Artificial Neural Networks-ICANN 2009, pp.964-974, 2009.

,

V. Valton, L. Romaniuk, J. D. Steele, S. Lawrie, and P. Seriès, Comprehensive Review: Computational Modelling of Schizophrenia, Neuroscience and Biobehavioral Reviews, 2017.

R. P. Vertes, B. Walter, K. Hoover, C. Szigeti-buck, and . Leranth, Nucleus Reuniens of the Midline Thalamus: Link between the Medial Prefrontal Cortex and the Hippocampus, Brain Research Bulletin, vol.71, issue.6, pp.601-610, 2007.

A. Viterbi, Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm, IEEE Transactions on Information Theory, vol.13, issue.2, pp.260-69, 1967.

L. R. Welch, Hidden Markov Models and the Baum-Welch Algorithm, IEEE Information Theory Society Newsletter, vol.53, issue.4, pp.10-13, 2003.

P. J. Werbos, Backpropagation Through Time: What It Does and How to Do It, Proceedings of the IEEE, vol.78, issue.10, pp.1550-60, 1990.

A. M. Wikenheiser and A. D. Redish, Decoding the Cognitive Map: Ensemble Hippocampal Sequences and Decision Making, Current Opinion in Neurobiology, vol.32, pp.8-15, 2014.

,

S. W. Wilson, The Animat Path to AI, From Animals to Animats, vol.1, pp.15-21, 1991.

Y. Yamashita and J. Tani, Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment, PLOS Computational Biology, vol.4, issue.11, 2008.

J. Yang, X. Shi, M. Marchese, and Y. Liang, An Ant Colony Optimization Method for Generalized TSP Problem, Progress in Natural Science, vol.18, issue.11, pp.1417-1439, 2008.