New insights into olivo-cerebellar circuits for learning from a small training sample

[1]  Kazuyuki Aihara,et al.  Reproduction of distance matrices and original time series from recurrence plots and their applications , 2008 .

[2]  Yuan Chang Leong,et al.  Dynamic Interaction between Reinforcement Learning and Attention in Multidimensional Environments , 2017, Neuron.

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

[4]  Mitsuo Kawato,et al.  Computation in the Cerebellum , 2013, Neural Networks.

[5]  Liang Meng,et al.  A sequential Monte Carlo approach to estimate biophysical neural models from spikes , 2011, Journal of neural engineering.

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

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

[8]  Elena Leznik,et al.  Electrotonically Mediated Oscillatory Patterns in Neuronal Ensembles: An In Vitro Voltage-Dependent Dye-Imaging Study in the Inferior Olive , 2002, The Journal of Neuroscience.

[9]  Jun Morimoto,et al.  Hierarchical reinforcement learning for motion learning: learning 'stand-up' trajectories , 1998, Adv. Robotics.

[10]  S. Koekkoek,et al.  Spatiotemporal firing patterns in the cerebellum , 2011, Nature Reviews Neuroscience.

[11]  P. Dean,et al.  The cerebellar microcircuit as an adaptive filter: experimental and computational evidence , 2010, Nature Reviews Neuroscience.

[12]  Eduardo Ros,et al.  Distributed Cerebellar Motor Learning: A Spike-Timing-Dependent Plasticity Model , 2016, Front. Comput. Neurosci..

[13]  Mitsuo Kawato,et al.  The Roles of the Olivocerebellar Pathway in Motor Learning and Motor Control. A Consensus Paper , 2017, The Cerebellum.

[14]  M. Kano,et al.  Spike timing-dependent selective strengthening of single climbing fibre inputs to Purkinje cells during cerebellar development , 2013, Nature Communications.

[15]  Masato Okada,et al.  Estimation of Intracellular Calcium Ion Concentration by Nonlinear State Space Modeling and Expectation-Maximization Algorithm for Parameter Estimation , 2010 .

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

[17]  Henk Nijmeijer,et al.  State and Parameter Estimation for Canonic Models of Neural oscillators , 2010, Int. J. Neural Syst..

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

[19]  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 .

[20]  K. Doya,et al.  Chaos may enhance information transmission in the inferior olive. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[21]  J. Raymond,et al.  Elimination of climbing fiber instructive signals during motor learning , 2009, Nature Neuroscience.

[22]  Erik De Schutter,et al.  Complex Parameter Landscape for a Complex Neuron Model , 2006, PLoS Comput. Biol..

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

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

[25]  Mitsuo Kawato,et al.  Role of the olivo-cerebellar complex in motor learning and control , 2013, Front. Neural Circuits.

[26]  Claudia Clopath,et al.  Modeled changes of cerebellar activity in mutant mice are predictive of their learning impairments , 2016, Scientific Reports.

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

[28]  Sergey Levine,et al.  Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection , 2016, Int. J. Robotics Res..

[29]  A. Bastian Moving, sensing and learning with cerebellar damage , 2011, Current Opinion in Neurobiology.

[30]  Timothy A. Blenkinsop,et al.  Modulation of Purkinje cell complex spike waveform by synchrony levels in the olivocerebellar system , 2014, Front. Syst. Neurosci..

[31]  Rhea R. Kimpo,et al.  Cerebellar Purkinje cell activity drives motor learning , 2013, Nature Neuroscience.

[32]  E. Boyden,et al.  Selective Engagement of Plasticity Mechanisms for Motor Memory Storage , 2006, Neuron.

[33]  R. Llinás,et al.  Structural study of inferior olivary nucleus of the cat: morphological correlates of electrotonic coupling. , 1974, Journal of neurophysiology.

[34]  Javier F. Medina,et al.  Timing Mechanisms in the Cerebellum: Testing Predictions of a Large-Scale Computer Simulation , 2000, The Journal of Neuroscience.

[35]  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.

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

[37]  T. Hoogland,et al.  Behavioral Correlates of Complex Spike Synchrony in Cerebellar Microzones , 2014, The Journal of Neuroscience.

[38]  Sean R. Anderson,et al.  Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum , 2015, Front. Neurorobot..

[39]  R. Llinás,et al.  Dynamic organization of motor control within the olivocerebellar system , 1995, Nature.

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

[41]  Anil K. Jain,et al.  Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[42]  Henk Nijmeijer,et al.  Observers for canonic models of neural oscillators , 2009, 0905.0149.

[43]  J. Deuchars,et al.  Role of Olivary Electrical Coupling in Cerebellar Motor Learning , 2008, Neuron.

[44]  W. Regehr,et al.  Inhibitory Regulation of Electrically Coupled Neurons in the Inferior Olive Is Mediated by Asynchronous Release of GABA , 2009, Neuron.

[45]  Masa-aki Sato,et al.  Segmental Bayesian estimation of gap-junctional and inhibitory conductance of inferior olive neurons from spike trains with complicated dynamics , 2015, Front. Comput. Neurosci..

[46]  Joshua B. Tenenbaum,et al.  Human-level concept learning through probabilistic program induction , 2015, Science.

[47]  Benjamin F. Grewe,et al.  High-speed in vivo calcium imaging reveals neuronal network activity with near-millisecond precision , 2010, Nature Methods.

[48]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[49]  David Pfau,et al.  Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data , 2016, Neuron.

[50]  Haruhiko Nishimura,et al.  Chaotic Resonance in Coupled Inferior Olive Neurons with the Llinás Approach Neuron Model , 2016, Neural Computation.

[51]  Yosef Yarom,et al.  Oscillatory activity, phase differences, and phase resetting in the inferior olivary nucleus , 2013, Front. Syst. Neurosci..

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

[53]  R. Llinás,et al.  Electrophysiology of mammalian inferior olivary neurones in vitro. Different types of voltage‐dependent ionic conductances. , 1981, The Journal of physiology.

[54]  Timothy A. Blenkinsop,et al.  Block of Inferior Olive Gap Junctional Coupling Decreases Purkinje Cell Complex Spike Synchrony and Rhythmicity , 2006, The Journal of Neuroscience.

[55]  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.

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

[57]  C. Stosiek,et al.  In vivo two-photon calcium imaging of neuronal networks , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[58]  Masao Ito Error detection and representation in the olivo-cerebellar system , 2013, Front. Neural Circuits.

[59]  G. Hesslow,et al.  Inhibition of the inferior olive during conditioned responses in the decerebrate ferret , 1996, Experimental Brain Research.

[60]  H. Jörntell Cerebellar Synaptic Plasticity and the Credit Assignment Problem , 2014, The Cerebellum.

[61]  Mitsuo Kawato,et al.  Inter-module credit assignment in modular reinforcement learning , 2003, Neural Networks.

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

[63]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[64]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[65]  Yosef Yarom,et al.  Cerebellar Inhibitory Input to the Inferior Olive Decreases Electrical Coupling and Blocks Subthreshold Oscillations , 2014, Neuron.

[66]  Chris I. De Zeeuw,et al.  Climbing Fiber Input Shapes Reciprocity of Purkinje Cell Firing , 2013, Neuron.

[67]  John Porrill,et al.  At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters , 2015, PLoS Comput. Biol..

[68]  K. Doya,et al.  Electrophysiological properties of inferior olive neurons: A compartmental model. , 1999, Journal of neurophysiology.

[69]  M. Mauk,et al.  Mechanisms of cerebellar learning suggested by eyelid conditioning , 2000, Current Opinion in Neurobiology.

[70]  Leslie G. Valiant,et al.  A general lower bound on the number of examples needed for learning , 1988, COLT '88.

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

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

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

[74]  M. Kawato,et al.  Multivoxel neurofeedback selectively modulates confidence without changing perceptual performance , 2016, Nature Communications.

[75]  Vladimir I. Nekorkin,et al.  Modeling inferior olive neuron dynamics , 2002, Neural Networks.

[76]  Rieko Osu,et al.  CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm , 2008, The Journal of Neuroscience.

[77]  Timothy J. Ebner,et al.  The Errors of Our Ways: Understanding Error Representations in Cerebellar-Dependent Motor Learning , 2015, The Cerebellum.

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

[79]  Michael Häusser,et al.  Dendritic Calcium Signaling Triggered by Spontaneous and Sensory-Evoked Climbing Fiber Input to Cerebellar Purkinje Cells In Vivo , 2011, The Journal of Neuroscience.

[80]  Masa-aki Sato,et al.  Reconstruction of two-dimensional movement trajectories from selected magnetoencephalography cortical currents by combined sparse Bayesian methods , 2011, NeuroImage.

[81]  Richard Kempter,et al.  State-dependencies of learning across brain scales , 2015, Front. Comput. Neurosci..

[82]  V. Han,et al.  NMDA Receptor Activation Strengthens Weak Electrical Coupling in Mammalian Brain , 2014, Neuron.

[83]  Nicolas Brunel,et al.  A Cerebellar Learning Model of Vestibulo-Ocular Reflex Adaptation in Wild-Type and Mutant Mice , 2014, The Journal of Neuroscience.

[84]  Martin Garwicz,et al.  A unifying model for timing of walking onset in humans and other mammals , 2009, Proceedings of the National Academy of Sciences.

[85]  Idan Segev,et al.  The Generation of Phase Differences and Frequency Changes in a Network Model of Inferior Olive Subthreshold Oscillations , 2012, PLoS Comput. Biol..

[86]  Anthony G. Hudetz,et al.  Functional and Topological Conditions for Explosive Synchronization Develop in Human Brain Networks with the Onset of Anesthetic-Induced Unconsciousness , 2016, Front. Comput. Neurosci..

[87]  Michael L. Mack,et al.  Dynamic updating of hippocampal object representations reflects new conceptual knowledge , 2016, Proceedings of the National Academy of Sciences.

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

[89]  C. I. Zeeuw,et al.  Motor Learning and the Cerebellum , 2015 .

[90]  E. J. Lang,et al.  GABAergic and glutamatergic modulation of spontaneous and motor-cortex-evoked complex spike activity. , 2002, Journal of neurophysiology.

[91]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[92]  R. Llinás,et al.  Role of gap junctions in synchronized neuronal oscillations in the inferior olive. , 2005, Journal of neurophysiology.

[93]  Timothy A. Blenkinsop,et al.  Synchrony is Key: Complex Spike Inhibition of the Deep Cerebellar Nuclei , 2015, The Cerebellum.

[94]  W. T. Thach,et al.  Purkinje cell activity during motor learning , 1977, Brain Research.

[95]  R. Llinás,et al.  Experimentally determined chaotic phase synchronization in a neuronal system. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[96]  James M. Bower,et al.  A Comparative Survey of Automated Parameter-Search Methods for Compartmental Neural Models , 1999, Journal of Computational Neuroscience.

[97]  Kazuyuki Aihara,et al.  Quantitative Modeling of Spatio-Temporal Dynamics of inferior Olive Neurons with a Simple Conductance-Based Model , 2010, Int. J. Bifurc. Chaos.

[98]  Idan Segev,et al.  Low-amplitude oscillations in the inferior olive: a model based on electrical coupling of neurons with heterogeneous channel densities. , 1997, Journal of neurophysiology.

[99]  Benjamin F. Grewe,et al.  High-speed in vivo calcium imaging reveals neuronal network activity with near-millisecond precision , 2010, Nature Methods.

[100]  Kazuyuki Aihara,et al.  Solution to the inverse problem of estimating gap-junctional and inhibitory conductance in inferior olive neurons from spike trains by network model simulation , 2013, Neural Networks.

[101]  Masa-aki Sato,et al.  Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns , 2008, NeuroImage.

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

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

[104]  R. Llinás,et al.  Electrotonic coupling between neurons in cat inferior olive. , 1974, Journal of neurophysiology.

[105]  David Haussler,et al.  Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications , 1992, Inf. Comput..