Cerebellar spiking engine: Towards objet model abstraction in manipulation

This paper presents how a plausible cerebellum-like architecture can abstract corrective models in the framework of a robot control task when manipulating objects that significantly affect the dynamics of the system. The presented scheme is adequate to control non-stiff-joint robots with low-power actuators which involve controlling systems with high inertial components. We evaluate the way in which the cerebellum stores a model in the granule layer, how its microstructure can efficiently abstract models and deliver accurate corrective torques for increasing precision during object manipulation. Particularly we study how input sensory-motor representations can enhance model abstraction capabilities during accurate movements, making use of explicit (model-related input labels) and implicit model representations (sensory signals). Finally we focus on how our cerebellum model (using a temporal correlation kernel) properly deals with transmission delays in sensory-motor pathways.

[1]  Etienne Burdet,et al.  Evaluation of parametric and nonparametric nonlinear adaptive controllers , 1998, Robotica.

[2]  J C Houk,et al.  The role of the cerebellum in modulating voluntary limb movement commands. , 2002, Archives italiennes de biologie.

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

[4]  D Colquhoun,et al.  Deactivation and desensitization of non‐NMDA receptors in patches and the time course of EPSCs in rat cerebellar granule cells. , 1996, The Journal of physiology.

[5]  R C Miall,et al.  The cerebellum, predictive control and motor coordination. , 2007, Novartis Foundation symposium.

[6]  G. Bi,et al.  Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type , 1998, The Journal of Neuroscience.

[7]  Stefan Schaal,et al.  Incremental Online Learning in High Dimensions , 2005, Neural Computation.

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

[9]  Daniel A. Braun,et al.  Motor Task Variation Induces Structural Learning , 2009, Current Biology.

[10]  Eduardo Ros,et al.  Event-driven simulation of neural population synchronization facilitated by electrical coupling , 2007, Biosyst..

[11]  M. Häusser,et al.  Compartmental models of rat cerebellar Purkinje cells based on simultaneous somatic and dendritic patch‐clamp recordings , 2001, The Journal of physiology.

[12]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[13]  E. D’Angelo,et al.  Synaptic excitation of individual rat cerebellar granule cells in situ: evidence for the role of NMDA receptors. , 1995, The Journal of physiology.

[14]  Jean-Jacques E Slotine,et al.  The intermediate cerebellum may function as a wave-variable processor , 1996, Neuroscience Letters.

[15]  Zoubin Ghahramani,et al.  Variational Inference for Bayesian Mixtures of Factor Analysers , 1999, NIPS.

[16]  S. Lisberger,et al.  Neural Learning Rules for the Vestibulo-Ocular Reflex , 1998, The Journal of Neuroscience.

[17]  Eduardo Ros,et al.  A real-time spiking cerebellum model for learning robot control , 2008, Biosyst..

[18]  Sethu Vijayakumar,et al.  Implications of different classes of sensorimotor disturbance for cerebellar-based motor learning models , 2009, Biological Cybernetics.

[19]  Eduardo Ros,et al.  Event-Driven Simulation Scheme for Spiking Neural Networks Using Lookup Tables to Characterize Neuronal Dynamics , 2006, Neural Computation.

[20]  V Taglietti,et al.  Ionic mechanism of electroresponsiveness in cerebellar granule cells implicates the action of a persistent sodium current. , 1998, Journal of neurophysiology.

[21]  E. D'Angelo,et al.  Different proportions of N-methyl-d-aspartate and non-N-methyl-d-aspartate receptor currents at the mossy fibre-granule cell synapse of developing rat cerebellum , 1993, Neuroscience.

[22]  Jianfeng Feng,et al.  Is the integrate-and-fire model good enough?--a review , 2001, Neural Networks.

[23]  Carl E. Rasmussen,et al.  In Advances in Neural Information Processing Systems , 2011 .

[24]  Thierry Nieus,et al.  LTP regulates burst initiation and frequency at mossy fiber-granule cell synapses of rat cerebellum: experimental observations and theoretical predictions. , 2006, Journal of neurophysiology.

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

[26]  Dieter Jaeger,et al.  No Parallel Fiber Volleys in the Cerebellar Cortex: Evidence from Cross-Correlation Analysis between Purkinje Cells in a Computer Model and in Recordings from Anesthetized Rats , 2003, Journal of Computational Neuroscience.

[27]  M Kawato,et al.  Internal models for motor control. , 2007, Novartis Foundation symposium.

[28]  Naiphinich Kotchabhakdi,et al.  Developmental Changes of Inhibitory Synaptic Currents in Cerebellar Granule Neurons: Role of GABAA Receptor α6 Subunit , 1996, The Journal of Neuroscience.

[29]  Angelo Arleo,et al.  Modeling Synaptic Transmission and Quantifying Information Transfer in the Granular Layer of the Cerebellum , 2005, IWANN.

[30]  Sethu Vijayakumar,et al.  Load estimation and control using learned dynamics models , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[31]  Alex M. Andrew,et al.  Spiking Neuron Models: Single Neurons, Populations, Plasticity , 2003 .

[32]  A G Barto,et al.  Prediction of complex two-dimensional trajectories by a cerebellar model of smooth pursuit eye movement. , 1997, Journal of neurophysiology.

[33]  Mark Farrant,et al.  Differences in Synaptic GABAA Receptor Number Underlie Variation in GABA Mini Amplitude , 1997, Neuron.

[34]  Nigel Crook,et al.  Special edition of BioSystems: Information processing in cells and tissues , 2008, Biosyst..

[35]  V Taglietti,et al.  Theta-Frequency Bursting and Resonance in Cerebellar Granule Cells: Experimental Evidence and Modeling of a Slow K+-Dependent Mechanism , 2001, The Journal of Neuroscience.

[36]  Erik De Schutter,et al.  Synchronization of Purkinje Cell Pairs Along the Parallel Fiber Axis: A Modeling Study , 2002, Neurocomputing.

[37]  Hong Liu,et al.  DLR-Hand II: next generation of a dextrous robot hand , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[38]  T. Bliss,et al.  Long‐lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path , 1973, The Journal of physiology.

[39]  J. Randall Flanagan,et al.  Flexible Representations of Dynamics Are Used in Object Manipulation , 2008, Current Biology.

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

[41]  D. W. Joyce,et al.  Kinematic cues in perceptual weight judgement and their origins in box lifting , 2007, Psychological research.

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

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

[44]  Juliette Gardner.  Genesis , 1985 .

[45]  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.

[46]  J M Bower,et al.  The Role of Synaptic and Voltage-Gated Currents in the Control of Purkinje Cell Spiking: A Modeling Study , 1997, The Journal of Neuroscience.

[47]  D. Rossi,et al.  Spillover-Mediated Transmission at Inhibitory Synapses Promoted by High Affinity α6 Subunit GABAA Receptors and Glomerular Geometry , 1998, Neuron.

[48]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..