Physical Symbol Systems*, Cognitive Science, vol.19, issue.3, pp.135-183, 1980. ,
DOI : 10.1207/s15516709cog0402_2
Minds, brains, and programs, Behavioral and Brain Sciences, vol.9, issue.03, pp.417-424, 1980. ,
DOI : 10.1080/00201747908601876
The symbol grounding problem, Physica D: Nonlinear Phenomena, vol.42, issue.1-3, pp.335-346, 1990. ,
DOI : 10.1016/0167-2789(90)90087-6
Being there: Putting brain, body, and world together again, 1997. ,
ORO, a knowledge management platform for cognitive architectures in robotics, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.3548-3553, 2010. ,
DOI : 10.1109/IROS.2010.5649547
Cognitive developmental robotics as a new paradigm for the design of humanoid robots, Robotics and Autonomous Systems, vol.37, issue.2-3, pp.185-193, 2001. ,
DOI : 10.1016/S0921-8890(01)00157-9
ARTIFICIAL INTELLIGENCE: Autonomous Mental Development by Robots and Animals, Science, vol.291, issue.5504, pp.599-600, 2001. ,
DOI : 10.1126/science.291.5504.599
Developmental robotics: a survey, Connection Science, vol.1, issue.4, pp.151-190, 2003. ,
DOI : 10.2307/1131322
The origins of intelligence in children, 1952. ,
DOI : 10.1037/11494-000
Play and Its Role in the Mental Development of the Child, Journal of Russian and East European Psychology, vol.5, issue.3, pp.6-18, 1967. ,
DOI : 10.2753/RPO1061-040505036
Theories of developmental psychology, 2010. ,
Foundations for a New Science of Learning, Science, vol.325, issue.5938, pp.284-288, 2009. ,
DOI : 10.1126/science.1175626
Some Basic Principles of Developmental Robotics, IEEE Transactions on Autonomous Mental Development, vol.1, issue.2, pp.122-130, 2009. ,
DOI : 10.1109/TAMD.2009.2029989
A Survey of the Ontogeny of Tool Use: From Sensorimotor Experience to Planning, IEEE Transactions on Autonomous Mental Development, vol.5, issue.1, pp.18-45, 2013. ,
DOI : 10.1109/TAMD.2012.2209879
The Theory of Affordances, 1977. ,
URL : https://hal.archives-ouvertes.fr/hal-00692033
Emergent structuring of interdependent affordance learning tasks, 4th International Conference on Development and Learning and on Epigenetic Robotics, 2014. ,
DOI : 10.1109/DEVLRN.2014.6983028
Structural bootstrapping at the sensorimotor level for the fast acquisition of action knowledge for cognitive robots, 2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL), pp.1-8, 2013. ,
DOI : 10.1109/DevLrn.2013.6652537
Behavior-Grounded Representation of Tool Affordances, Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp.3060-3065, 2005. ,
DOI : 10.1109/ROBOT.2005.1570580
Affordance learning from range data for multi-step planning, International Conference on Epigenetic Robotics, 2009. ,
Learning relational affordance models for robots in multi-object manipulation tasks, 2012 IEEE International Conference on Robotics and Automation, pp.4373-4378, 2012. ,
DOI : 10.1109/ICRA.2012.6225042
Bootstrapping paired-object affordance learning with learned single-affordance features, 4th International Conference on Development and Learning and on Epigenetic Robotics, 2014. ,
DOI : 10.1109/DEVLRN.2014.6983026
The mechanics of embodiment: A dialog on embodiment and computational modeling Embodied and grounded cognition, p.196, 2011. ,
Learning Deep Architectures for AI, Machine Learning, pp.1-127, 2009. ,
DOI : 10.1561/2200000006
Deep learning, Nature, vol.9, issue.7553, pp.436-444, 2015. ,
DOI : 10.1007/s10994-013-5335-x
Modeling language and cognition with deep unsupervised learning: a tutorial overview, Frontiers in Psychology, vol.4, 2013. ,
DOI : 10.3389/fpsyg.2013.00515
Emergence of a 'visual number sense' in hierarchical generative models, Nature Neuroscience, vol.16, issue.2, pp.194-196, 2012. ,
DOI : 10.1038/nn.2996
A Deep Learning Neural Network for Number Cognition: A bi-cultural study with the iCub, 2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), 2015. ,
DOI : 10.1109/DEVLRN.2015.7346165
Multilayer feedforward networks are universal approximators, Neural Networks, vol.2, issue.5, pp.359-366, 1989. ,
DOI : 10.1016/0893-6080(89)90020-8
Shallow versus Deep Sum-Product Networks, Advances in Neural Information Processing Systems (NIPS), pp.666-674, 2011. ,
Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, issue.11, pp.2278-2324, 1998. ,
DOI : 10.1109/5.726791
Learning long-term dependencies with gradient descent is difficult, IEEE Transactions on Neural Networks, vol.5, issue.2, pp.157-166, 1994. ,
DOI : 10.1109/72.279181
Gradient flow in recurrent nets: the difficulty of learning long-term dependencies , " in A field guide to dynamical recurrent neural networks, 2001. ,
Understanding the difficulty of training deep feedforward neural networks, International Conference on Artificial Intelligence and Statistics (AISTATS), pp.249-256, 2010. ,
Reducing the Dimensionality of Data with Neural Networks, Science, vol.313, issue.5786, pp.504-507, 2006. ,
DOI : 10.1126/science.1127647
Exploring speaker-specific characteristics with deep learning, The 2011 International Joint Conference on Neural Networks, pp.103-110, 2011. ,
DOI : 10.1109/IJCNN.2011.6033207
An introduction to support vector machines and other kernel-based learning methods, 2000. ,
DOI : 10.1017/CBO9780511801389
Discriminative feature extraction with Deep Neural Networks, The 2010 International Joint Conference on Neural Networks (IJCNN), pp.1-8, 2010. ,
DOI : 10.1109/IJCNN.2010.5596329
Representation Learning: A Review and New Perspectives, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.8, pp.1798-1828, 2013. ,
DOI : 10.1109/TPAMI.2013.50
Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems (NIPS) 25, pp.1106-1114, 2012. ,
Learning a better representation of speech soundwaves using restricted boltzmann machines, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.5884-5887, 2011. ,
DOI : 10.1109/ICASSP.2011.5947700
Acoustic Modeling Using Deep Belief Networks, IEEE Transactions on Audio, Speech, and Language Processing, vol.20, issue.1, pp.14-22, 2012. ,
DOI : 10.1109/TASL.2011.2109382
The difficulty of training deep architectures and the effect of unsupervised pre-training, International Conference on Artificial Intelligence and Statistics (AISTATS), 2009. ,
An exact mapping between the variational renormalization group and deep learning, 2014. ,
Deep learning and the information bottleneck principle, 2015 IEEE Information Theory Workshop (ITW), 2015. ,
DOI : 10.1109/ITW.2015.7133169
Deep learning via hessian-free optimization, Proceedings of the 27th International Conference on Machine Learning (ICML), pp.735-742, 2010. ,
Deep Big Multilayer Perceptrons for Digit Recognition, Neural Networks Tricks of the Trade, vol.86, issue.11, pp.581-598, 2012. ,
DOI : 10.1109/5.726791
Multi-column deep neural network for traffic sign classification, Neural Networks, vol.32, pp.333-338, 2012. ,
DOI : 10.1016/j.neunet.2012.02.023
What Regularized Auto-Encoders Learn from the Data Generating Distribution, pp.4246-4250, 2013. ,
A Practical Guide to Training Restricted Boltzmann Machines, Momentum, vol.79, issue.7, 2010. ,
DOI : 10.1073/pnas.79.8.2554
Gated softmax classification, Advances in Neural Information Processing Systems, pp.1603-1611, 2010. ,
Deep machine learninga new frontier in artificial intelligence research Computational Intelligence Magazine Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, Proceedings of the 26th Annual International Conference on Machine Learning (ICML), pp.13-18, 2009. ,
Extracting and composing robust features with denoising autoencoders, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.1096-1103, 2008. ,
DOI : 10.1145/1390156.1390294
Improving neural networks by preventing coadaptation of feature detectors, 2012. ,
Dropout: A simple way to prevent neural networks from overfitting, The Journal of Machine Learning Research, vol.15, issue.1, pp.1929-1958, 2014. ,
Contractive Auto-Encoders: Explicit Invariance During Feature Extraction, Proceedings of the 28th International Conference on Machine Learning (ICML), pp.833-840, 2011. ,
Higher Order Contractive Auto-Encoder, ECML/PKDD, pp.2011-645 ,
DOI : 10.1016/S0042-6989(97)00169-7
Efficient sparse coding algorithms, Advances in Neural Information Processing Systems, p.801, 2007. ,
Information processing in dynamical systems: foundations of harmony theory, " in Parallel distributed processing: explorations in the microstructure of cognition, pp.194-281, 1986. ,
A Learning Algorithm for Boltzmann Machines*, Cognitive Science, vol.85, issue.1, pp.147-169, 1985. ,
DOI : 10.1207/s15516709cog0901_7
Probabilistic graphical models: principles and techniques, 2009. ,
The Recurrent Temporal Restricted Boltzmann Machine, Advances in Neural Information Processing Systems (NIPS) 21, pp.1601-1608, 2008. ,
Training Products of Experts by Minimizing Contrastive Divergence, Neural Computation, vol.22, issue.8, pp.1771-1800, 2002. ,
DOI : 10.1162/089976600300015385
A unified energy-based framework for unsupervised learning, International Conference on Artificial Intelligence and Statistics, pp.371-379, 2007. ,
Generalized denoising auto-encoders as generative models, Advances in Neural Information Processing Systems, pp.899-907, 2013. ,
Generative class-conditional autoencoders, 2014. ,
Deep Boltzmann Machines, Proceedings of the International Conference on Artificial Intelligence and Statistics, pp.448-455, 2009. ,
Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines, Neural Computation, vol.17, issue.6, pp.1473-1492, 2010. ,
DOI : 10.1007/3-540-47969-4_30
Gradient-based learning of higher-order image features, 2011 International Conference on Computer Vision ,
DOI : 10.1109/ICCV.2011.6126419
A sensorimotor account of vision and visual consciousness, Behavioral and Brain Sciences, vol.24, issue.05, pp.939-973, 2001. ,
DOI : 10.1017/S0140525X01000115
Grounding object perception in a naive agents sensorimotor experience, ICDL-EPIROB, 2015. ,
Learning image representations equivariant to ego-motion, 2015. ,
Learning Phrase Representations using RNN Encoder???Decoder for Statistical Machine Translation, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014. ,
DOI : 10.3115/v1/D14-1179
URL : https://hal.archives-ouvertes.fr/hal-01433235
The Architecture of Cognitive Control in the Human Prefrontal Cortex, Science, vol.302, issue.5648, pp.1181-1185, 2003. ,
DOI : 10.1126/science.1088545
The Development of Embodied Cognition: Six Lessons from Babies, Artificial Life, vol.45, issue.3, pp.13-29, 2005. ,
DOI : 10.1126/science.134.3491.1692
Hearing lips and seeing voices, Nature, vol.65, issue.5588, pp.246-248, 1976. ,
DOI : 10.1038/264746a0
Rubber hands 'feel' touch that eyes see, Nature, vol.391, issue.6669, p.756, 1998. ,
DOI : 10.1038/35784
Deep unsupervised network for multimodal perception, representation and classification, Robotics and Autonomous Systems, vol.71, 2014. ,
DOI : 10.1016/j.robot.2014.11.005
URL : https://hal.archives-ouvertes.fr/hal-01083521
Object Learning Through Active Exploration, IEEE Transactions on Autonomous Mental Development, vol.6, issue.1, pp.56-72, 2014. ,
DOI : 10.1109/TAMD.2013.2280614
URL : https://hal.archives-ouvertes.fr/hal-00919694
Recent advances in deep learning for speech research at Microsoft, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.8604-8608, 2013. ,
DOI : 10.1109/ICASSP.2013.6639345
Recurrent deep neural networks for robust speech recognition, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.5532-5536, 2014. ,
DOI : 10.1109/ICASSP.2014.6854661
ChaLearn gesture challenge: Design and first results, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp.1-6, 2012. ,
DOI : 10.1109/CVPRW.2012.6239178
Multi-scale Deep Learning for Gesture Detection and Localization, Computer Vision- ECCV 2014 Workshops, pp.474-490, 2014. ,
DOI : 10.1007/978-3-319-16178-5_33
URL : https://hal.archives-ouvertes.fr/hal-01419792
Algorithms for manifold learning University of California, Tech. Rep, 2005. ,
Sample complexity of testing the manifold hypothesis, Advances in Neural Information Processing Systems (NIPS) 23, 2010. ,
The Manifold Tangent Classifier, Advances in Neural Information Processing Systems 24, pp.2294-2302, 2011. ,
Learning Deep Representations via Multiplicative Interactions between Factors of Variation, Workshop NIPS, 2013. ,
Data clustering: a review, ACM Computing Surveys, vol.31, issue.3, pp.264-323, 1999. ,
DOI : 10.1145/331499.331504
Some methods for classification and analysis, Berkeley Symposium on Mathematical Statistics and Probability, pp.281-297, 1967. ,
Hierarchical Grouping to Optimize an Objective Function, Journal of the American Statistical Association, vol.58, issue.301, pp.236-244, 1963. ,
DOI : 10.1007/BF02289263
Self-organized formation of topologically correct feature maps, Biological Cybernetics, vol.13, issue.1, pp.59-69, 1982. ,
DOI : 10.1007/BF00337288
The mnist database of handwritten digits, 1998. ,
Learning a repertoire of actions with deep neural networks, 4th International Conference on Development and Learning and on Epigenetic Robotics, pp.1-6, 2014. ,
DOI : 10.1109/DEVLRN.2014.6982986
URL : https://hal.archives-ouvertes.fr/hal-01065741
Learning Multilevel Distributed Representations for High-dimensional Sequences, Proceeding of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007. ,
Modeling deep temporal dependencies with recurrent " grammar cells, Advances in neural information processing systems, pp.1925-1933, 2014. ,
From Actions to Goals and Vice-Versa: Theoretical Analysis and Models of the Ideomotor Principle and TOTE, Anticipatory Behavior in Adaptive Learning Systems, pp.73-93, 2007. ,
DOI : 10.1007/978-3-540-74262-3_5
Anticipatory Behavior: Exploiting Knowledge About the Future to Improve Current Behavior, Anticipatory behavior in adaptive learning systems, pp.1-10, 2003. ,
DOI : 10.1007/978-3-540-45002-3_1
Thinking as the control of imagination: a conceptual framework for goal-directed systems Whatever next? predictive brains, situated agents, and the future of cognitive science, Psychological Research Behavioral and Brain Sciences, vol.73, issue.36 03, pp.559-577, 2009. ,
Predictive coding under the free-energy principle, Philosophical Transactions of the Royal Society B: Biological Sciences, vol.335, issue.6188, pp.1211-1221, 2009. ,
DOI : 10.1038/335311a0
Le sens du mouvement, 1997. ,
On intelligence, 2007. ,
Anticipation by multimodal association through an artificial mental imagery process, Connection Science, vol.27, issue.1, pp.1-21, 2014. ,
Coordinating with the Future: The Anticipatory Nature of Representation, Minds and Machines, pp.179-225, 2008. ,
DOI : 10.1007/s11023-008-9095-5
Efferent influences on receptors in knowledge formation, Psycoloquy, vol.9, p.41, 1998. ,
Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects, Nature Neuroscience, vol.2, issue.1, pp.79-87, 1999. ,
DOI : 10.1038/4580
Learning to generate articulated behavior through the bottom-up and the top-down interaction processes, Neural Networks, vol.16, issue.1, pp.11-23, 2003. ,
DOI : 10.1016/S0893-6080(02)00214-9
Anticipation-Based Temporal Sequences Learning in Hierarchical Structure, IEEE Transactions on Neural Networks, vol.18, issue.2, pp.344-358, 2007. ,
DOI : 10.1109/TNN.2006.884681
Gated autoencoders with tied input weights, International Conference on Machine Learning, pp.1-6, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00817035
Modeling human motion using binary latent variables, Advances in neural information processing systems, pp.1345-1352, 2006. ,
Factored conditional restricted Boltzmann Machines for modeling motion style, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.1025-1032, 2009. ,
DOI : 10.1145/1553374.1553505
Two distributed-state models for generating high-dimensional time series, The Journal of Machine Learning Research, vol.12, pp.1025-1068, 2011. ,
Neural Networks for Sequential Data: a Pre???training Approach based on Hidden Markov Models, Neurocomputing, vol.169, 2015. ,
DOI : 10.1016/j.neucom.2014.11.081
LSTM can solve hard long time lag problems, Advances in Neural Information Processing Systems 9: Proceedings of the 1996 Conference, p.473, 1997. ,
Learning to Forget: Continual Prediction with LSTM, Neural Computation, vol.3, issue.10, pp.2451-2471, 2000. ,
DOI : 10.1162/neco.1990.2.4.490
Bidirectional LSTM networks for improved phoneme classification and recognition, Artificial Neural Networks: Formal Models and Their Applications? ICANN 2005, pp.799-804, 2005. ,
Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition, 2014. ,
Sequence labelling in structured domains with hierarchical recurrent neural networks, IJCAI, pp.774-779, 2007. ,
Generating text with recurrent neural networks, Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp.1017-1024, 2011. ,
A clockwork rnn, 2014. ,
Learning longer memory in recurrent neural networks, " arXiv preprint arXiv:1412, 2014. ,
Learning to encode motion using spatio-temporal synchrony, 2013. ,
Factored four way conditional restricted Boltzmann machines for activity recognition, Pattern Recognition Letters, vol.66, 2015. ,
DOI : 10.1016/j.patrec.2015.01.013
URL : http://repository.liv.ac.uk/2010100/1/ffwcrbm_preprint-2.pdf
Combinations of muscle synergies in the construction of a natural motor behavior, Nature Neuroscience, vol.6, issue.3, pp.300-308, 2003. ,
DOI : 10.1038/nn1010
Movement imitation with nonlinear dynamical systems in humanoid robots, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), 2002. ,
DOI : 10.1109/ROBOT.2002.1014739
Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors, Neural Computation, vol.2010, issue.11, pp.328-373, 2013. ,
DOI : 10.1109/AT-EQUAL.2009.32
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.365.6845
Abstract, Paladyn, Journal of Behavioral Robotics, vol.4, issue.1, pp.49-61, 2013. ,
DOI : 10.2478/pjbr-2013-0003
Learning compact parameterized skills with expanded function approximators, Proceedings of the IEEE-RAS International Conference on Humanoid Robots (Humanoids), pp.1-7, 2013. ,
DOI : 10.1109/humanoids.2013.7030008
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.698.8260
Many regression algorithms, one unified model: A review, Neural Networks, vol.69, pp.60-79, 2015. ,
DOI : 10.1016/j.neunet.2015.05.005
URL : https://hal.archives-ouvertes.fr/hal-01162281
Task adaptation through exploration and action sequencing, 2009 9th IEEE-RAS International Conference on Humanoid Robots, 2009. ,
DOI : 10.1109/ICHR.2009.5379568
Reinforcement Learning to adjust Robot Movements to New Situations, Robotics: Science and Systems VI, 2010. ,
DOI : 10.15607/RSS.2010.VI.005
Deep auto-encoder neural networks in reinforcement learning, The 2010 International Joint Conference on Neural Networks (IJCNN), pp.1-8, 2010. ,
DOI : 10.1109/IJCNN.2010.5596468
Evolving deep unsupervised convolutional networks for vision-based reinforcement learning, Proceedings of the 2014 conference on Genetic and evolutionary computation, GECCO '14, pp.541-548, 2014. ,
DOI : 10.1145/2576768.2598358
Least square policy iteration, Journal of Machine Learning Research, vol.4, pp.1107-1149, 2003. ,
Path integral policy improvement with covariance matrix adaptation, Proceedings of the 29th International Conference on Machine Learning (ICML), pp.1-8, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00789391
Deep learning in neural networks: An overview, Neural Networks, vol.61, pp.85-117, 2015. ,
DOI : 10.1016/j.neunet.2014.09.003
Human-level control through deep reinforcement learning, Nature, vol.101, issue.7540, pp.529-533, 2015. ,
DOI : 10.1038/nature14236
Continuous control with deep reinforcement learning, 2015. ,
Learning neural network policies with guided policy search under unknown dynamics, Advances in Neural Information Processing Systems, pp.1071-1079, 2014. ,
End-to-end training of deep visuomotor policies, 2015. ,
Intrinsically Motivated Learning of Real-World Sensorimotor Skills with Developmental Constraints, Intrinsically motivated learning in natural and artificial systems, pp.303-365, 2013. ,
DOI : 10.1007/978-3-642-32375-1_13
URL : https://hal.archives-ouvertes.fr/hal-00788611
Curious model-building control systems, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks, pp.1458-1463, 1991. ,
DOI : 10.1109/IJCNN.1991.170605
Intrinsic Motivation Systems for Autonomous Mental Development, IEEE Transactions on Evolutionary Computation, vol.11, issue.2, pp.265-286, 2007. ,
DOI : 10.1109/TEVC.2006.890271
Guest Editorial Active Learning and Intrinsically Motivated Exploration in Robots: Advances and Challenges, IEEE Transactions on Autonomous Mental Development, vol.2, issue.2, pp.65-69, 2010. ,
DOI : 10.1109/TAMD.2010.2052419
URL : https://hal.archives-ouvertes.fr/inria-00541788
R-IAC: Robust Intrinsically Motivated Exploration and Active Learning, IEEE Transactions on Autonomous Mental Development, vol.1, issue.3, pp.155-169, 2009. ,
DOI : 10.1109/TAMD.2009.2037513
The construction of reality in the child, Routledge, vol.82, 1954. ,
DOI : 10.1037/11168-000
The interaction of maturational constraints and intrinsic motivations in active motor development, 2011 IEEE International Conference on Development and Learning (ICDL), pp.1-8, 2011. ,
DOI : 10.1109/DEVLRN.2011.6037315
Curriculum learning, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.41-48, 2009. ,
DOI : 10.1145/1553374.1553380
A robust layered control system for a mobile robot, IEEE Journal on Robotics and Automation, vol.2, issue.1, pp.14-23, 1986. ,
DOI : 10.1109/JRA.1986.1087032
ECA: An enactivist cognitive architecture based on sensorimotor modeling, Biologically Inspired Cognitive Architectures, vol.6, pp.46-57, 2013. ,
DOI : 10.1016/j.bica.2013.05.006
URL : https://hal.archives-ouvertes.fr/hal-01339190
Deep Learning for Detecting Robotic Grasps, Robotics: Science and Systems IX, pp.705-724, 2015. ,
DOI : 10.15607/RSS.2013.IX.012
Building high-level features using large scale unsupervised learning, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.8595-8598, 2013. ,
DOI : 10.1109/ICASSP.2013.6639343
Deep Learning of Representations, pp.1-37, 2013. ,
DOI : 10.1007/978-3-642-36657-4_1
Sleep inspires insight, Nature, vol.427, issue.6972, pp.352-355, 2004. ,
DOI : 10.1038/nature02223
Replay of Neuronal Firing Sequences in Rat Hippocampus During Sleep Following Spatial Experience, Science, vol.271, issue.5257, pp.1870-1873, 1996. ,
DOI : 10.1126/science.271.5257.1870
Replay of rule-learning related neural patterns in the prefrontal cortex during sleep, Nature Neuroscience, vol.60, issue.7, pp.919-926, 2009. ,
DOI : 10.2307/2288799
URL : https://hal.archives-ouvertes.fr/hal-00551868
Schemata and Sequential Thought Processes in PDP Models, Parallel Distributed Processing, pp.7-57, 1986. ,
DOI : 10.1016/B978-1-4832-1446-7.50020-0