What Is Optimal about Motor Control?

[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 .