50 Years Since the Marr, Ito, and Albus Models of the Cerebellum

[1]  Martin T. Wiechert,et al.  Synaptic diversity enables temporal coding of coincident multi-sensory inputs in single neurons , 2015, Nature Neuroscience.

[2]  T. Hirano,et al.  Long-term depression and other synaptic plasticity in the cerebellum , 2013, Proceedings of the Japan Academy. Series B, Physical and biological sciences.

[3]  S. Amari.  Any Target Function Exists in a Neighborhood of Any Sufficiently Wide Random Network: A Geometrical Perspective , 2020, Neural Computation.

[4]  Jesse M. Lingeman,et al.  How Do You Learn to Walk? Thousands of Steps and Dozens of Falls per Day , 2012, Psychological science.

[5]  D. Tank,et al.  Cerebellar disruption impairs working memory during evidence accumulation , 2019, Nature Communications.

[6]  Tadashi Yamazaki,et al.  Modeling memory consolidation during posttraining periods in cerebellovestibular learning , 2015, Proceedings of the National Academy of Sciences.

[7]  Mitsuo Kawato,et al.  Adaptive feedback control models of the vestibulocerebellum and spinocerebellum , 2004, Biological Cybernetics.

[8]  K. Doya,et al.  A unifying computational framework for motor control and social interaction. , 2003, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[9]  Kenji Kawano,et al.  Ocular tracking: behavior and neurophysiology , 1999, Current Opinion in Neurobiology.

[10]  M Ito,et al.  Neurophysiological aspects of the cerebellar motor control system. , 1970, International journal of neurology.

[11]  Mitsuo Kawato,et al.  Cerebellar Activity Evoked By Common Tool-Use Execution And Imagery Tasks: An Fmri Study , 2007, Cortex.

[12]  Tadashi Yamazaki,et al.  Long-term depression as a model of cerebellar plasticity. , 2014, Progress in brain research.

[13]  D. Linden.  The Return of the Spike Postsynaptic Action Potentials and the Induction of LTP and LTD , 1999, Neuron.

[14]  M. Kawato,et al.  Ca2+ Requirements for Cerebellar Long-Term Synaptic Depression: Role for a Postsynaptic Leaky Integrator , 2007, Neuron.

[15]  Masato Inoue,et al.  Error Signals in Motor Cortices Drive Adaptation in Reaching , 2016, Neuron.

[16]  Mitsuo Kawato,et al.  Internal models for motor control and trajectory planning , 1999, Current Opinion in Neurobiology.

[17]  M. Ito,et al.  Cerebellar long-term depression: characterization, signal transduction, and functional roles. , 2001, Physiological reviews.

[18]  Aaron R. Seitz,et al.  Learning what to expect , 2013 .

[19]  Christopher H. Yeo,et al.  Cerebellar Function in Consolidation of a Motor Memory , 2002, Neuron.

[20]  M. Iino,et al.  Cross Talk between Metabotropic and Ionotropic Glutamate Receptor-Mediated Signaling in Parallel Fiber-Induced Inositol 1,4,5-Trisphosphate Production in Cerebellar Purkinje Cells , 2004, The Journal of Neuroscience.

[21]  Riccardo Zucca,et al.  Number of Spikes in Climbing Fibers Determines the Direction of Cerebellar Learning , 2013, The Journal of Neuroscience.

[22]  Mitsuo Kawato,et al.  A computational model of four regions of the cerebellum based on feedback-error learning , 2004, Biological Cybernetics.

[23]  R. A. Hensbroek,et al.  Intraburst and Interburst Signaling by Climbing Fibers , 2007, The Journal of Neuroscience.

[24]  William Heffley,et al.  Classical conditioning drives learned reward prediction signals in climbing fibers across the lateral cerebellum , 2019, bioRxiv.

[25]  R. Huganir,et al.  Reevaluating the Role of LTD in Cerebellar Motor Learning , 2011, Neuron.

[26]  D. Wolpert,et al.  The cerebellum is involved in predicting the sensory consequences of action , 1999, Neuroreport.

[27]  Sten Grillner,et al.  Striatal cellular properties conserved from lampreys to mammals , 2011, The Journal of physiology.

[28]  Allison M Okamura,et al.  Predicting and correcting ataxia using a model of cerebellar function. , 2014, Brain : a journal of neurology.

[29]  J. Wickens,et al.  Dopamine reverses the depression of rat corticostriatal synapses which normally follows high-frequency stimulation of cortex In vitro , 1996, Neuroscience.

[30]  Catherine J. Stoodley,et al.  The Theory and Neuroscience of Cerebellar Cognition. , 2019, Annual review of neuroscience.

[31]  F. A. Miles,et al.  Plasticity in the vestibulo-ocular reflex: a new hypothesis. , 1981, Annual review of neuroscience.

[32]  D. Wolpert,et al.  Internal models in the cerebellum , 1998, Trends in Cognitive Sciences.

[33]  George J. Augustine,et al.  Graded Control of Climbing-Fiber-Mediated Plasticity and Learning by Inhibition in the Cerebellum , 2018, Neuron.

[34]  E. Gardner,et al.  Optimal storage properties of neural network models , 1988 .

[35]  N. Yozbatiran,et al.  Diffusion tensor imaging of the human cerebellar pathways and their interplay with cerebral macrostructure , 2015, Front. Neuroanat..

[36]  Nathaniel B. Sawtell,et al.  Neural Mechanisms for Predicting the Sensory Consequences of Behavior: Insights from Electrosensory Systems. , 2017, Annual review of physiology.

[37]  J. Simpson,et al.  Microcircuitry and function of the inferior olive , 1998, Trends in Neurosciences.

[38]  伊藤 正男.  The cerebellum : brain for an implicit self , 2012 .

[39]  Aleksandra Badura,et al.  Cerebellar Granule Cells: Dense, Rich and Evolving Representations , 2017, Current Biology.

[40]  João Fayad,et al.  A quantitative framework for whole-body coordination reveals specific deficits in freely walking ataxic mice , 2015, eLife.

[41]  Johannes Schmidt-Hieber.  Nonparametric regression using deep neural networks with ReLU activation function , 2020 .

[42]  Mitsuo Kawato,et al.  The neural and cognitive architecture for learning from a small sample , 2018, Current Opinion in Neurobiology.

[43]  T. Holy,et al.  Sensory Experience Remodels Genome Architecture in Neural Circuit to Drive Motor Learning , 2019, Nature.

[44]  Reza Shadmehr,et al.  Encoding of error and learning to correct that error by the Purkinje cells of the cerebellum , 2018, Nature Neuroscience.

[45]  R. Llinás,et al.  Electrophysiological properties of in vitro Purkinje cell dendrites in mammalian cerebellar slices. , 1980, The Journal of physiology.

[46]  Yan Yang,et al.  Duration of complex-spikes grades Purkinje cell plasticity and cerebellar motor learning , 2014, Nature.

[47]  M. Kawato,et al.  Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm , 2022 .

[48]  Mitsuo Kawato,et al.  Multiple Model-Based Reinforcement Learning , 2002, Neural Computation.

[49]  R. Weale.  Vision. A Computational Investigation Into the Human Representation and Processing of Visual Information. David Marr , 1983 .

[50]  K Kawano,et al.  Inverse‐Dynamics Representation of Eye Movements by Cerebellar Purkinje Cell Activity during Short‐Latency Ocular‐Following Responses , 1996, Annals of the New York Academy of Sciences.

[51]  E. D’Angelo,et al.  Beyond parallel fiber LTD: the diversity of synaptic and non-synaptic plasticity in the cerebellum , 2001, Nature Neuroscience.

[52]  V. Han,et al.  Reversible Associative Depression and Nonassociative Potentiation at a Parallel Fiber Synapse , 2000, Neuron.

[53]  O. Oscarsson,et al.  Projections to lateral vestibular nucleus from cerebellar climbing fiber zones , 1978, Experimental Brain Research.

[54]  K. Doya,et al.  Unsupervised learning of granule cell sparse codes enhances cerebellar adaptive control , 2001, Neuroscience.

[55]  Hiroshi Imamizu,et al.  Human cerebellar activity reflecting an acquired internal model of a new tool , 2000, Nature.

[56]  Zhenyu Gao,et al.  Distributed synergistic plasticity and cerebellar learning , 2012, Nature Reviews Neuroscience.

[57]  Nicolas Le Novère,et al.  DARPP-32 Is a Robust Integrator of Dopamine and Glutamate Signals , 2006, PLoS Comput. Biol..

[58]  S. Nagao,et al.  Motor learning in common marmosets: Vestibulo-ocular reflex adaptation and its sensitivity to inhibitors of Purkinje cell long-term depression , 2014, Neuroscience Research.

[59]  Professor Dr. John C. Eccles,et al.  The Cerebellum as a Neuronal Machine , 1967, Springer Berlin Heidelberg.

[60]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[61]  Yusuke Murayama,et al.  Unravelling cerebellar pathways with high temporal precision targeting motor and extensive sensory and parietal networks , 2012, Nature Communications.

[62]  Johannes Schmidt-Hieber,et al.  Nonparametric regression using deep neural networks with ReLU activation function , 2017, The Annals of Statistics.

[63]  M. Farrant,et al.  Climbing‐fibre activation of NMDA receptors in Purkinje cells of adult mice , 2007, The Journal of physiology.

[64]  S. Itohara,et al.  Memory trace of motor learning shifts transsynaptically from cerebellar cortex to nuclei for consolidation , 2006, Neuroscience.

[65]  Michael N. Economo,et al.  A cortico-cerebellar loop for motor planning , 2018, Nature.

[66]  M. Kawato,et al.  Computational studies on acquisition and adaptation of ocular following responses based on cerebellar synaptic plasticity. , 2002, Journal of neurophysiology.

[67]  D. Marr.  A theory of cerebellar cortex , 1969, The Journal of physiology.

[68]  Shogo Ohmae,et al.  Climbing fibers encode a temporal-difference prediction error during cerebellar learning in mice , 2015, Nature Neuroscience.

[69]  Yoshikazu Isomura,et al.  Modular organization of cerebellar climbing fiber inputs during goal-directed behavior , 2019, eLife.

[70]  Masao Ito,et al.  Long-lasting depression of parallel fiber-Purkinje cell transmission induced by conjunctive stimulation of parallel fibers and climbing fibers in the cerebellar cortex , 1982, Neuroscience Letters.

[71]  Thomas M. Cover,et al.  Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition , 1965, IEEE Trans. Electron. Comput..

[72]  B. Widrow,et al.  Stationary and nonstationary learning characteristics of the LMS adaptive filter , 1976, Proceedings of the IEEE.

[73]  A. Takemura,et al.  Visual inputs to cerebellar ventral paraflocculus during ocular following responses. , 1996, Progress in brain research.

[74]  T Tyrrell,et al.  Cerebellar cortex: its simulation and the relevance of Marr's theory. , 1992, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[75]  Jun Morimoto,et al.  The eMOSAIC model for humanoid robot control , 2012, Neural Networks.

[76]  M. Kawato,et al.  Modular organization of internal models of tools in the human cerebellum , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[77]  Emanuel Todorov,et al.  Iterative Linear Quadratic Regulator Design for Nonlinear Biological Movement Systems , 2004, ICINCO.

[78]  Jeanette Kotaleski,et al.  Transient Calcium and Dopamine Increase PKA Activity and DARPP-32 Phosphorylation , 2006, PLoS Comput. Biol..

[79]  M. Kawato,et al.  Shared neural correlates for language and tool use in Broca's area , 2009, Neuroreport.

[80]  Jennifer L. Raymond,et al.  Depressed by Learning—Heterogeneity of the Plasticity Rules at Parallel Fiber Synapses onto Purkinje Cells , 2018, The Cerebellum.

[81]  Michael Häusser,et al.  Multimodal sensory integration in single cerebellar granule cells in vivo , 2015, eLife.

[82]  Kenji Doya,et al.  What are the computations of the cerebellum, the basal ganglia and the cerebral cortex? , 1999, Neural Networks.

[83]  J. Krakauer,et al.  Sensory prediction errors drive cerebellum-dependent adaptation of reaching. , 2007, Journal of neurophysiology.

[84]  Masao Ito,et al.  Climbing fibre induced depression of both mossy fibre responsiveness and glutamate sensitivity of cerebellar Purkinje cells , 1982, The Journal of physiology.

[85]  R. Nicoll,et al.  Excitatory synaptic currents in Purkinje cells , 1990, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[86]  Abigail L. Person,et al.  Theoretically Sparse, Empirically Dense: New Views on Cerebellar Granule Cells , 2018, Trends in Neurosciences.

[87]  Zhanmin Lin,et al.  Cerebellar modules operate at different frequencies , 2014, eLife.

[88]  Samy Bengio,et al.  Understanding deep learning requires rethinking generalization , 2016, ICLR.

[89]  T. Ebner,et al.  Force field effects on cerebellar Purkinje cell discharge with implications for internal models , 2006, Nature Neuroscience.

[90]  M. Kawato,et al.  Inositol 1,4,5-Trisphosphate-Dependent Ca2+ Threshold Dynamics Detect Spike Timing in Cerebellar Purkinje Cells , 2005, The Journal of Neuroscience.

[91]  Michael I. Jordan,et al.  Forward Models: Supervised Learning with a Distal Teacher , 1992, Cogn. Sci..

[92]  M. Kawato,et al.  Inverse-dynamics model eye movement control by Purkinje cells in the cerebellum , 1993, Nature.

[93]  A H Guenther,et al.  IEEE (Institute of Electrical and Electronics Engineers) International Pulsed Power Conference on (2nd) Held in Lubbock, Texas on 12-14 June 1979 (Digest of Technical Papers) , 1980 .

[94]  Andrew K. Wise,et al.  Systematic Regional Variations in Purkinje Cell Spiking Patterns , 2014, PloS one.

[95]  Kenji Doya,et al.  Symbolization and Imitation Learning of Motion Sequence Using Competitive Modules , 2006 .

[96]  M. Kawato,et al.  Electrical coupling controls dimensionality and chaotic firing of inferior olive neurons , 2019, bioRxiv.

[97]  Mati Joshua,et al.  Cerebellar climbing fibers encode expected reward size , 2019, bioRxiv.

[98]  H. Markram,et al.  Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997, Science.

[99]  Wulfram Gerstner,et al.  Spike-timing dependent plasticity , 2010, Scholarpedia.

[100]  Masao Ito.  Cerebellar circuitry as a neuronal machine , 2006, Progress in Neurobiology.

[101]  Henrik Jörntell,et al.  Sensory transmission in cerebellar granule cells relies on similarly coded mossy fiber inputs , 2009, Proceedings of the National Academy of Sciences.

[102]  Kazuyuki Aihara,et al.  The role of chaotic resonance in cerebellar learning , 2010, Neural Networks.

[103]  Daniel M. Wolpert,et al.  Hierarchical MOSAIC for movement generation , 2003 .

[104]  K. Obata,et al.  Monosynaptic inhibition of the intracerebellar nuclei induced from the cerebellar cortex , 1964, Experientia.

[105]  M. Garwicz,et al.  Anatomical and physiological foundations of cerebellar information processing , 2005, Nature Reviews Neuroscience.

[106]  Michael I. Jordan,et al.  Optimal feedback control as a theory of motor coordination , 2002, Nature Neuroscience.

[107]  Gordon M. Shepherd,et al.  Neocortical Lamination: Insights from Neuron Types and Evolutionary Precursors , 2017, Front. Neuroanat..

[108]  P. Strick,et al.  Cerebellum and nonmotor function. , 2009, Annual review of neuroscience.

[109]  Mitsuo Kawato,et al.  Expansion coding and computation in the cerebellum: 50 years after the Marr–Albus codon theory , 2019, The Journal of physiology.

[110]  Sumio Watanabe.  Algebraic Geometry and Statistical Learning Theory , 2009 .

[111]  M. Yamamoto,et al.  Topographical representation of vestibulo-ocular reflexes in rabbit cerebellar flocculus , 1982, Neuroscience.

[112]  Jennifer L. Raymond,et al.  Timing Rules for Synaptic Plasticity Matched to Behavioral Function , 2018, Neuron.

[113]  M. Kawato,et al.  Encoding of movement dynamics by Purkinje cell simple spike activity during fast arm movements under resistive and assistive force fields. , 2007, Journal of neurophysiology.

[114]  Masato Inoue,et al.  Motor Error in Parietal Area 5 and Target Error in Area 7 Drive Distinctive Adaptation in Reaching , 2018, Current Biology.

[115]  Richard Apps,et al.  Cerebellar cortical organization: a one-map hypothesis , 2009, Nature Reviews Neuroscience.

[116]  A. Bastian.  Learning to predict the future: the cerebellum adapts feedforward movement control , 2006, Current Opinion in Neurobiology.

[117]  D. Angelaki,et al.  Neural Representation of Orientation Relative to Gravity in the Macaque Cerebellum , 2013, Neuron.

[118]  Kamran Khodakhah,et al.  Cerebellar modulation of the reward circuitry and social behavior , 2019, Science.

[119]  Ben Deverett,et al.  Cerebellar granule cells acquire a widespread predictive feedback signal during motor learning , 2017, Nature Neuroscience.

[120]  M. Kawato,et al.  Internal forward models in the cerebellum: fMRI study on grip force and load force coupling. , 2003, Progress in brain research.

[121]  S. G. Lisberger,et al.  Motor learning in a recurrent network model based on the vestibulo–ocular reflex , 1992, Nature.

[122]  Mitsuo Kawato,et al.  Recognition of manipulated objects by motor learning with modular architecture networks , 1991, Neural Networks.

[123]  W. Skaggs,et al.  The Cerebellum , 2016 .

[124]  Terence D. Sanger,et al.  A Cerebellar Computational Mechanism for Delay Conditioning at Precise Time Intervals , 2020, Neural Computation.

[125]  Naveen Sendhilnathan,et al.  Neural Correlates of Reinforcement Learning in Mid-lateral Cerebellum , 2020, Neuron.

[126]  Yoshiko Kojima,et al.  Encoding of action by the Purkinje cells of the cerebellum , 2015, Nature.

[127]  S. Wang,et al.  Coincidence detection in single dendritic spines mediated by calcium release , 2000, Nature Neuroscience.

[128]  Masao Ito,et al.  Reassessment of long-term depression in cerebellar Purkinje cells in mice carrying mutated GluA2 C terminus , 2016, Proceedings of the National Academy of Sciences.

[129]  Ranran L French,et al.  Cerebellar Purkinje cells control eye movements with a rapid rate code that is invariant to spike irregularity , 2019, eLife.

[130]  Internal Models , 2020, Encyclopedia of Creativity, Invention, Innovation and Entrepreneurship.

[131]  R. Northcutt,et al.  Understanding Vertebrate Brain Evolution1 , 2002, Integrative and comparative biology.

[132]  Martina Sgritta,et al.  Hebbian Spike-Timing Dependent Plasticity at the Cerebellar Input Stage , 2017, The Journal of Neuroscience.

[133]  Jessica X. Brooks,et al.  The Primate Cerebellum Selectively Encodes Unexpected Self-Motion , 2013, Current Biology.

[134]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[135]  Mitsuo Kawato,et al.  MOSAIC Model for Sensorimotor Learning and Control , 2001, Neural Computation.

[136]  Y. Dan,et al.  Spike timing-dependent plasticity: a Hebbian learning rule. , 2008, Annual review of neuroscience.

[137]  Masahiko Watanabe,et al.  Structure–Function Relationships between Aldolase C/Zebrin II Expression and Complex Spike Synchrony in the Cerebellum , 2015, The Journal of Neuroscience.

[138]  H. Jörntell,et al.  Questioning the role of sparse coding in the brain , 2015, Trends in Neurosciences.

[139]  Jerome Carriot,et al.  Learning to expect the unexpected: rapid updating in primate cerebellum during voluntary self-motion , 2015, Nature Neuroscience.

[140]  G. Allen,et al.  Cerebrocerebellar communication systems. , 1974, Physiological reviews.

[141]  J. Raymond,et al.  Timing Rules for Synaptic Plasticity Matched to Behavioral Function , 2016, Neuron.

[142]  Gordon Cheng,et al.  Learning to select primitives and generate sub-goals from practice , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[143]  Mati Joshua,et al.  Coordinated cerebellar climbing fiber activity signals learned sensorimotor predictions , 2018, bioRxiv.

[144]  N. Yamaguchi,et al.  Pathology of the cerebellar dentate and interpositus nuclei in Joseph disease: a morphometric investigation , 1992, Journal of the Neurological Sciences.

[145]  Andrei Khilkevich,et al.  A cerebellar adaptation to uncertain inputs , 2018, Science Advances.

[146]  Xiaofeng Lu,et al.  Anatomical evidence for the involvement of medial cerebellar output from the interpositus nuclei in cognitive functions , 2012, Proceedings of the National Academy of Sciences.

[147]  Richard Apps,et al.  Heterogeneity of Purkinje cell simple spike–complex spike interactions: zebrin‐ and non‐zebrin‐related variations , 2017, The Journal of physiology.

[148]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[149]  Henrik Jörntell,et al.  Properties of Somatosensory Synaptic Integration in Cerebellar Granule Cells In Vivo , 2006, The Journal of Neuroscience.

[150]  M. Häusser,et al.  Initiation and spread of sodium action potentials in cerebellar purkinje cells , 1994, Neuron.

[151]  M. Jeannerod,et al.  Plasticity of the vestibulo-ocular reflex. , 1979, Acta oto-rhino-laryngologica Belgica.

[152]  Timothy J. Ebner,et al.  Modulation of sensory prediction error in Purkinje cells during visual feedback manipulations , 2018, Nature Communications.

[153]  Mitsuo Kawato,et al.  Heterarchical reinforcement-learning model for integration of multiple cortico-striatal loops: fMRI examination in stimulus-action-reward association learning , 2006, Neural Networks.

[154]  Masaki Tanaka,et al.  Entrained neuronal activity to periodic visual stimuli in the primate striatum compared with the cerebellum , 2019, eLife.

[155]  J. Simpson,et al.  Visual climbing fiber input to rabbit vestibulo-cerebellum: a source of direction-specific information. , 1974, Brain research.

[156]  Taiji Suzuki,et al.  Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality , 2018, ICLR.

[157]  Thomas D. Mrsic-Flogel,et al.  Cerebellar contribution to preparatory activity in motor neocortex , 2018 .

[158]  D. Armstrong,et al.  Activity patterns of cerebellar cortical neurones and climbing fibre afferents in the awake cat. , 1979, The Journal of physiology.

[159]  D. Wolpert,et al.  Central cancellation of self-produced tickle sensation , 1998, Nature Neuroscience.

[160]  Greg Wayne,et al.  A temporal basis for predicting the sensory consequences of motor commands in an electric fish , 2014, Nature Neuroscience.

[161]  Javier F. Medina,et al.  Computer simulation of cerebellar information processing , 2000, Nature Neuroscience.

[162]  Miltiadis Boboulos.  Automation and Robotics , 2010 .

[163]  M. Häusser,et al.  Propagation of action potentials in dendrites depends on dendritic morphology. , 2001, Journal of neurophysiology.

[164]  Stefan Schaal,et al.  Forward models in visuomotor control. , 2002, Journal of neurophysiology.

[165]  Michael I. Jordan,et al.  An internal model for sensorimotor integration. , 1995, Science.

[166]  Jun Morimoto,et al.  Acquisition of stand-up behavior by a real robot using hierarchical reinforcement learning , 2000, Robotics Auton. Syst..

[167]  Mitsuo Kawato,et al.  Systems Biology Perspectives on Cerebellar Long-Term Depression , 2008, Neurosignals.

[168]  R Angus Silver,et al.  The Contribution of Single Synapses to Sensory Representation in Vivo , 2008, Science.

[169]  Chris I. De Zeeuw,et al.  High Frequency Burst Firing of Granule Cells Ensures Transmission at the Parallel Fiber to Purkinje Cell Synapse at the Cost of Temporal Coding , 2013, Front. Neural Circuits.

[170]  J. Kotaleski,et al.  Subcellular interactions between parallel fibre and climbing fibre signals in purkinje cells predict sensitivity of classical conditioning to interstimulus interval , 2002, Integrative physiological and behavioral science : the official journal of the Pavlovian Society.

[171]  Nathaniel B Sawtell,et al.  Neural mechanisms for filtering self-generated sensory signals in cerebellum-like circuits , 2011, Current Opinion in Neurobiology.

[172]  Tracy M. Yamawaki,et al.  The reptilian brain , 2015, Current Biology.

[173]  Dmitry Berenson,et al.  What Happened at the DARPA Robotics Challenge Finals , 2018 .

[174]  M. Sakurai.  Synaptic modification of parallel fibre‐Purkinje cell transmission in in vitro guinea‐pig cerebellar slices. , 1987, The Journal of physiology.

[175]  Masaki Tanaka,et al.  Cerebellar Roles in Self-Timing for Sub- and Supra-Second Intervals , 2017, The Journal of Neuroscience.

[176]  Sarah E. Criscimagna-Hemminger,et al.  Cerebellar Contributions to Reach Adaptation and Learning Sensory Consequences of Action , 2012, The Journal of Neuroscience.

[177]  Thomas D. Mrsic-Flogel,et al.  Cerebellar Contribution to Preparatory Activity in Motor Neocortex , 2018, Neuron.

[178]  M. Häusser,et al.  High-fidelity transmission of sensory information by single cerebellar mossy fibre boutons , 2007, Nature.

[179]  Jan Evangelista Purkinje.  Purkinje Cells , 2002 .

[180]  J. Albus.  A Theory of Cerebellar Function , 1971 .

[181]  Ruben Portugues,et al.  Sensorimotor Representations in Cerebellar Granule Cells in Larval Zebrafish Are Dense, Spatially Organized, and Non-temporally Patterned , 2017, Current Biology.

[182]  Mitsuo Kawato,et al.  Cerebellar supervised learning revisited: biophysical modeling and degrees-of-freedom control , 2011, Current Opinion in Neurobiology.

[183]  George J Augustine,et al.  Precise Control of Movement Kinematics by Optogenetic Inhibition of Purkinje Cell Activity , 2014, The Journal of Neuroscience.

[184]  R. Ivry,et al.  The Cerebellum: Adaptive Prediction for Movement and Cognition , 2017, Trends in Cognitive Sciences.

[185]  Mitsuo Kawato,et al.  Adaptive coupling of inferior olive neurons in cerebellar learning , 2013, Neural Networks.

[186]  D. Nicholson,et al.  Addition of inhibition in the olivocerebellar system and the ontogeny of a motor memory , 2003, Nature Neuroscience.

[187]  M. Kawato,et al.  New insights into olivo-cerebellar circuits for learning from a small training sample , 2017, Current Opinion in Neurobiology.

[188]  Mitsuo Kawato,et al.  Nitric Oxide Regulates Input Specificity of Long-Term Depression and Context Dependence of Cerebellar Learning , 2006, PLoS Comput. Biol..

[189]  Mitsuo Kawato,et al.  Equilibrium-Point Control Hypothesis Examined by Measured Arm Stiffness During Multijoint Movement , 1996, Science.

[190]  Soichi Nagao,et al.  Effects of reversible pharmacological shutdown of cerebellar flocculus on the memory of long-term horizontal vestibulo-ocular reflex adaptation in monkeys , 2010, Neuroscience Research.

[191]  Douglas R. Wylie,et al.  More on climbing fiber signals and their consequence(s) , 1996 .

[192]  T. Hirano,et al.  Depression and potentiation of the synaptic transmission between a granule cell and a Purkinje cell in rat cerebellar culture , 1990, Neuroscience Letters.

[193]  A. Takemura,et al.  Neural activity in the dorsal medial superior temporal area of monkeys represents retinal error during adaptive motor learning , 2017, Scientific Reports.

[194]  T. Hirano,et al.  Occurrence of long-term depression in the cerebellar flocculus during adaptation of optokinetic response , 2018, eLife.

[195]  Shane A. Heiney,et al.  Chromatin remodeling inactivates activity genes and regulates neural coding , 2016, Science.

[196]  M. Häusser,et al.  Encoding of Oscillations by Axonal Bursts in Inferior Olive Neurons , 2009, Neuron.

[197]  Masao Ito.  The molecular organization of cerebellar long-term depression , 2002, Nature Reviews Neuroscience.

[198]  Yasmine El-Shamayleh,et al.  Selective Optogenetic Control of Purkinje Cells in Monkey Cerebellum , 2017, Neuron.

[199]  P. Bromberg.  Structure-Function Relationships , 1999 .

[200]  Tatsuya Kimura,et al.  Cerebellar complex spikes encode both destinations and errors in arm movements , 1998, Nature.

[201]  Katrina Y. Choe,et al.  Circuit Mechanisms Underlying Motor Memory Formation in the Cerebellum , 2015, Neuron.

[202]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[203]  M. Ito,et al.  The cerebellar-evoked monosynaptic inhibition of Deiters' neurones , 1964, Experientia.

[204]  Alberto Bemporad,et al.  The explicit linear quadratic regulator for constrained systems , 2003, Autom..

[205]  Reza Shadmehr,et al.  Computational nature of human adaptive control during learning of reaching movements in force fields , 1999, Biological Cybernetics.

[206]  Mitsuo Kawato,et al.  Computational study on monkey VOR adaptation and smooth pursuit based on the parallel control-pathway theory. , 2002, Journal of neurophysiology.

[207]  D. Wolpert,et al.  Is the cerebellum a smith predictor? , 1993, Journal of motor behavior.

[208]  Joseph E LeDoux,et al.  Parallels between cerebellum- and amygdala-dependent conditioning , 2002, Nature Reviews Neuroscience.

[209]  Shogo Ohmae,et al.  Temporally Specific Sensory Signals for the Detection of Stimulus Omission in the Primate Deep Cerebellar Nuclei , 2013, The Journal of Neuroscience.

[210]  D. Linden,et al.  Long-term synaptic depression. , 1995, Annual review of neuroscience.

[211]  M. Kawato,et al.  Temporal firing patterns of Purkinje cells in the cerebellar ventral paraflocculus during ocular following responses in monkeys I. Simple spikes. , 1998, Journal of neurophysiology.

[212]  Zhanmin Lin,et al.  Excitatory Cerebellar Nucleocortical Circuit Provides Internal Amplification during Associative Conditioning , 2016, Neuron.

[213]  Jennifer L Raymond,et al.  Computational Principles of Supervised Learning in the Cerebellum. , 2018, Annual review of neuroscience.

[214]  Terrence J. Sejnowski,et al.  Unsupervised Learning , 2018, Encyclopedia of GIS.

[215]  C. C. Bell,et al.  Effect of electric organ discharge on ampullary receptors in a mormyrid , 1978, Brain Research.

[216]  A. Joyner,et al.  Morphology, molecular codes, and circuitry produce the three-dimensional complexity of the cerebellum. , 2007, Annual review of cell and developmental biology.

[217]  Joshua G. Hale,et al.  Using Humanoid Robots to Study Human Behavior , 2000, IEEE Intell. Syst..

[218]  Masaki Tanaka,et al.  Different contributions of preparatory activity in the basal ganglia and cerebellum for self-timing , 2018, eLife.

[219]  伊藤 正男.  The cerebellum and neural control , 1984 .

[220]  Abigail L. Person,et al.  Cerebellar Loops: A Review of the Nucleocortical Pathway , 2013, The Cerebellum.

[221]  M. Yuzaki,et al.  Interneuronal NMDA receptors regulate long‐term depression and motor learning in the cerebellum , 2018, The Journal of physiology.

[222]  D M Wolpert,et al.  Multiple paired forward and inverse models for motor control , 1998, Neural Networks.

[223]  Eduardo Ros,et al.  Distributed Circuit Plasticity: New Clues for the Cerebellar Mechanisms of Learning , 2016, The Cerebellum.

[224]  G S Brindley,et al.  Nerve net models of plausible size that perform many simple learning tasks , 1969, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[225]  M. Fujita,et al.  Adaptive filter model of the cerebellum , 1982, Biological Cybernetics.

[226]  Wei Zhang,et al.  Long-Term Depression at the Mossy Fiber–Deep Cerebellar Nucleus Synapse , 2006, The Journal of Neuroscience.

[227]  G. Bi,et al.  Synaptic modification by correlated activity: Hebb's postulate revisited. , 2001, Annual review of neuroscience.

[228]  M. Barrot,et al.  Clusters of cerebellar Purkinje cells control their afferent climbing fiber discharge , 2013, Proceedings of the National Academy of Sciences.

[229]  Adam W Hantman,et al.  Convergence of pontine and proprioceptive streams onto multimodal cerebellar granule cells , 2013, eLife.

[230]  P. Greengard,et al.  Dichotomous Dopaminergic Control of Striatal Synaptic Plasticity , 2008, Science.

[231]  渡邊 澄夫.  Algebraic geometry and statistical learning theory , 2009 .

[232]  S. M. Morton,et al.  Mechanisms of cerebellar gait ataxia , 2008, The Cerebellum.

[233]  James S. Albus,et al.  New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)1 , 1975 .

[234]  M. Horne,et al.  Comparison of the basal ganglia in rats, marmosets, macaques, baboons, and humans: Volume and neuronal number for the output, internal relay, and striatal modulating nuclei , 2002, The Journal of comparative neurology.

[235]  Yasushi Nakada,et al.  Normal motor learning during pharmacological prevention of Purkinje cell long-term depression. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[236]  Germund Hesslow,et al.  Simple and Complex Spike Firing Patterns in Purkinje Cells During Classical Conditioning , 2008, The Cerebellum.

[237]  Soichi Nagao,et al.  Tandem internal models execute motor learning in the cerebellum , 2018, Proceedings of the National Academy of Sciences.

[238]  I. Sugihara,et al.  Convergence of unisensory-evoked signals via multiple pathways to the cerebellum , 2019, bioRxiv.

[239]  Keiko Tanaka,et al.  Experimental and computational aspects of signaling mechanisms of spike‐timing‐dependent plasticity , 2009, HFSP journal.

[240]  Magnus Borga,et al.  Hierarchical Reinforcement Learning , 1993 .

[241]  L. Luo,et al.  Cerebellar granule cells encode the expectation of reward , 2017, Nature.

[242]  Tracy M. Yamawaki,et al.  Evolution of pallium, hippocampus, and cortical cell types revealed by single-cell transcriptomics in reptiles , 2018, Science.

[243]  H J G Gundersen,et al.  No change in neuron numbers in the dentate nucleus of patients with schizophrenia estimated with a new stereological method – the smooth fractionator , 2004, Journal of anatomy.

[244]  M. Häusser,et al.  Predictive and reactive reward signals conveyed by climbing fiber inputs to cerebellar Purkinje cells , 2019, Nature Neuroscience.

[245]  Mitsuo Kawato,et al.  Feedback-error-learning neural network for trajectory control of a robotic manipulator , 1988, Neural Networks.

[246]  M. Ito,et al.  Pharmacological properties of the postsynaptic inhibition by Purkinje cell axons and the action of γ-aminobutyric acid on Deiters neurones , 2004, Experimental Brain Research.

[247]  Junichiro Yoshimoto,et al.  A Kinetic Model of Dopamine- and Calcium-Dependent Striatal Synaptic Plasticity , 2010, PLoS Comput. Biol..

[248]  Mitsuo Kawato,et al.  MOSAIC for Multiple-Reward Environments , 2008 .

[249]  M. Kawato,et al.  Exploration of Signal Transduction Pathways in Cerebellar Long-Term Depression by Kinetic Simulation , 2001, The Journal of Neuroscience.

[250]  Tadashi Yamazaki,et al.  Revisiting a Theory of Cerebellar Cortex , 2019, Neuroscience Research.

[251]  Claude-Nicolas Fiechter,et al.  Efficient reinforcement learning , 1994, COLT '94.

[252]  M. Mauk,et al.  A Mechanism for Savings in the Cerebellum , 2001, The Journal of Neuroscience.

[253]  N. Ramnani.  The primate cortico-cerebellar system: anatomy and function , 2006, Nature Reviews Neuroscience.

[254]  Masao Ito.  Control of mental activities by internal models in the cerebellum , 2008, Nature Reviews Neuroscience.

[255]  Cathrin B. Canto,et al.  Role of Synchronous Activation of Cerebellar Purkinje Cell Ensembles in Multi-joint Movement Control , 2015, Current Biology.

[256]  Debra Bourne.  Simple and complex , 2015 .

[257]  M. Kawato,et al.  A hierarchical neural-network model for control and learning of voluntary movement , 2004, Biological Cybernetics.

[258]  M. Kawato,et al.  Metacognition facilitates the exploitation of unconscious brain states , 2019, bioRxiv.

[259]  M. Kawato,et al.  Temporal firing patterns of Purkinje cells in the cerebellar ventral paraflocculus during ocular following responses in monkeys II. Complex spikes. , 1998, Journal of neurophysiology.

[260]  Surya Ganguli,et al.  Shared Cortex-Cerebellum Dynamics in the Execution and Learning of a Motor Task , 2019, Cell.

[261]  Akiyo Takahashi,et al.  Optogenetic Control of Synaptic AMPA Receptor Endocytosis Reveals Roles of LTD in Motor Learning , 2018, Neuron.