What Is Optimal about Motor Control?
暂无分享,去创建一个
[1] Karl J. Friston,et al. Free Energy, Value, and Attractors , 2011, Comput. Math. Methods Medicine.
[2] Vicenç Gómez,et al. Optimal control as a graphical model inference problem , 2009, Machine Learning.
[3] D. Wolpert,et al. Principles of sensorimotor learning , 2011, Nature Reviews Neuroscience.
[4] Stefan Schaal,et al. Path integral control and bounded rationality , 2011, 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL).
[5] Gerd Gigerenzer,et al. Heuristic decision making. , 2011, Annual review of psychology.
[6] Feng Rong,et al. Sensorimotor Integration in Speech Processing: Computational Basis and Neural Organization , 2011, Neuron.
[7] Karl J. Friston,et al. Action understanding and active inference , 2011, Biological Cybernetics.
[8] T. Erez,et al. Optimal Limit-Cycle Control recast as Bayesian Inference , 2011 .
[9] Marc Toussaint,et al. Approximate Inference and Stochastic Optimal Control , 2010, ArXiv.
[10] Emanuel Todorov,et al. Inverse Optimal Control with Linearly-Solvable MDPs , 2010, ICML.
[11] P. Dayan,et al. States versus Rewards: Dissociable Neural Prediction Error Signals Underlying Model-Based and Model-Free Reinforcement Learning , 2010, Neuron.
[12] Stefan Schaal,et al. A Generalized Path Integral Control Approach to Reinforcement Learning , 2010, J. Mach. Learn. Res..
[13] Karl J. Friston,et al. Action and behavior: a free-energy formulation , 2010, Biological Cybernetics.
[14] Karl J. Friston. The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.
[15] R. Ivry,et al. The coordination of movement: optimal feedback control and beyond , 2010, Trends in Cognitive Sciences.
[16] David J. Fleet,et al. Optimizing walking controllers , 2009, ACM Trans. Graph..
[17] Karl J. Friston,et al. Reinforcement Learning or Active Inference? , 2009, PloS one.
[18] Opher Donchin,et al. Frontiers in Cellular Neuroscience Cellular Neuroscience Review Article the State Predicting Feedback Controller Compensatory Eye Movements Forward Models and State Estimation in Compensatory Eye Movements , 2022 .
[19] Nando de Freitas,et al. An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward , 2009, AISTATS.
[20] Konrad Paul Kording,et al. Relevance of error: what drives motor adaptation? , 2009, Journal of neurophysiology.
[21] A. G. Feldman. New insights into action–perception coupling , 2009, Experimental Brain Research.
[22] Matthew Botvinick,et al. Goal-directed decision making in prefrontal cortex: a computational framework , 2008, NIPS.
[23] Emanuel Todorov,et al. General duality between optimal control and estimation , 2008, 2008 47th IEEE Conference on Decision and Control.
[24] Karl J. Friston,et al. A Hierarchy of Time-Scales and the Brain , 2008, PLoS Comput. Biol..
[25] Karl J. Friston. Hierarchical Models in the Brain , 2008, PLoS Comput. Biol..
[26] Marc Toussaint,et al. Hierarchical POMDP Controller Optimization by Likelihood Maximization , 2008, UAI.
[27] Hilbert J. Kappen,et al. Graphical Model Inference in Optimal Control of Stochastic Multi-Agent Systems , 2008, J. Artif. Intell. Res..
[28] J. Krakauer,et al. A computational neuroanatomy for motor control , 2008, Experimental Brain Research.
[29] D. Poeppel,et al. Speech perception at the interface of neurobiology and linguistics , 2008, Philosophical Transactions of the Royal Society B: Biological Sciences.
[30] Konrad Paul Kording,et al. Decision Theory: What "Should" the Nervous System Do? , 2007, Science.
[31] Emanuel Todorov,et al. Evidence for the Flexible Sensorimotor Strategies Predicted by Optimal Feedback Control , 2007, The Journal of Neuroscience.
[32] Scott T. Grafton,et al. Evidence for a distributed hierarchy of action representation in the brain. , 2007, Human movement science.
[33] J. Krakauer,et al. Sensory prediction errors drive cerebellum-dependent adaptation of reaching. , 2007, Journal of neurophysiology.
[34] Daniel M Wolpert,et al. Computational principles of sensorimotor control that minimize uncertainty and variability , 2007, The Journal of physiology.
[35] Stefan Schaal,et al. Dynamics systems vs. optimal control--a unifying view. , 2007, Progress in brain research.
[36] Lorenz T. Biegler,et al. Simultaneous dynamic optimization strategies: Recent advances and challenges , 2006, Comput. Chem. Eng..
[37] Karl J. Friston,et al. A free energy principle for the brain , 2006, Journal of Physiology-Paris.
[38] Marc Toussaint,et al. Probabilistic inference for solving discrete and continuous state Markov Decision Processes , 2006, ICML.
[39] Tutut Herawan,et al. Computational and mathematical methods in medicine. , 2006, Computational and mathematical methods in medicine.
[40] M G Paulin,et al. Evolution of the cerebellum as a neuronal machine for Bayesian state estimation , 2005, Journal of neural engineering.
[41] S. Shipp. The importance of being agranular: a comparative account of visual and motor cortex , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.
[42] Florentin Wörgötter,et al. Temporal Sequence Learning, Prediction, and Control: A Review of Different Models and Their Relation to Biological Mechanisms , 2005, Neural Computation.
[43] J. Kelso,et al. The Excitator as a Minimal Model for the Coordination Dynamics of Discrete and Rhythmic Movement Generation , 2005, Journal of motor behavior.
[44] H. Kappen. Linear theory for control of nonlinear stochastic systems. , 2004, Physical review letters.
[45] Jun Tani,et al. Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using RNNPB , 2004, Neural Networks.
[46] E. Todorov. Optimality principles in sensorimotor control , 2004, Nature Neuroscience.
[47] P Ao,et al. LETTER TO THE EDITOR: Potential in stochastic differential equations: novel construction , 2004 .
[48] Konrad Paul Kording,et al. Bayesian integration in sensorimotor learning , 2004, Nature.
[49] Toniann Pitassi,et al. Stochastic Boolean Satisfiability , 2001, Journal of Automated Reasoning.
[50] P. Morasso,et al. Kinematic networks , 1988, Biological Cybernetics.
[51] D. Mumford. On the computational architecture of the neocortex , 2004, Biological Cybernetics.
[52] R. Miall,et al. Connecting mirror neurons and forward models. , 2003, Neuroreport.
[53] Paul F. M. J. Verschure,et al. Environmentally mediated synergy between perception and behaviour in mobile robots , 2003, Nature.
[54] D. Hoffman,et al. Sensorimotor transformations in cortical motor areas , 2003, Neuroscience Research.
[55] Sanjoy K. Mitter,et al. A Variational Approach to Nonlinear Estimation , 2003, SIAM J. Control. Optim..
[56] Jun Tani,et al. Learning to generate articulated behavior through the bottom-up and the top-down interaction processes , 2003, Neural Networks.
[57] Michael I. Jordan,et al. Optimal feedback control as a theory of motor coordination , 2002, Nature Neuroscience.
[58] A. Dickinson,et al. Neuronal coding of prediction errors. , 2000, Annual review of neuroscience.
[59] Rajesh P. N. Rao,et al. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. , 1999 .
[60] Michael I. Jordan. Learning in Graphical Models , 1999, NATO ASI Series.
[61] Nevin Lianwen Zhang,et al. Probabilistic Inference in Influence Diagrams , 1998, Comput. Intell..
[62] R. Guillery,et al. On the actions that one nerve cell can have on another: distinguishing "drivers" from "modulators". , 1998, Proceedings of the National Academy of Sciences of the United States of America.
[63] Geoffrey E. Hinton,et al. A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.
[64] Geoffrey E. Hinton,et al. Using Expectation-Maximization for Reinforcement Learning , 1997, Neural Computation.
[65] Daniel M. Wolpert,et al. Forward Models for Physiological Motor Control , 1996, Neural Networks.
[66] A. G. Feldman,et al. The origin and use of positional frames of reference in motor control , 1995, Behavioral and Brain Sciences.
[67] Michael I. Jordan,et al. An internal model for sensorimotor integration. , 1995, Science.
[68] Geoffrey E. Hinton,et al. The Helmholtz Machine , 1995, Neural Computation.
[69] Frank Jensen,et al. From Influence Diagrams to junction Trees , 1994, UAI.
[70] Daniel Kahneman,et al. Probabilistic reasoning , 1993 .
[71] Christian P. Robert,et al. L'analyse statistique bayésienne , 1993 .
[72] D. Wolpert,et al. Is the cerebellum a smith predictor? , 1993, Journal of motor behavior.
[73] D Mumford,et al. On the computational architecture of the neocortex. II. The role of cortico-cortical loops. , 1992, Biological cybernetics.
[74] Gregory F. Cooper,et al. A Method for Using Belief Networks as Influence Diagrams , 2013, UAI 1988.
[75] Ross D. Shachter. Probabilistic Inference and Influence Diagrams , 1988, Oper. Res..
[76] L. Brown. A Complete Class Theorem for Statistical Problems with Finite Sample Spaces , 1981 .
[77] A G Barto,et al. Toward a modern theory of adaptive networks: expectation and prediction. , 1981, Psychological review.
[78] N. A. Bernshteĭn. The co-ordination and regulation of movements , 1967 .
[79] R Bellman,et al. On the Theory of Dynamic Programming. , 1952, Proceedings of the National Academy of Sciences of the United States of America.
[80] Bayesian State Estimation , 2022 .