Motor control in a meta-network with attractor dynamics.

A neural-network module with attractor dynamics has been shown recently to be robust to stochastic noise in external and internal signals, and to converge rapidly onto an output signal that is an unbiased and efficient representation of the environment. We suggest here a modular network architecture with attractor dynamics that can compute the time-varying signals that are presumably required to control arm movements. The architecture is composed of several linked modules and implements the joint torque control of a planar biomechanical model of the arm, in the presence of external dynamic perturbations. The meta-network is robust to noise and to the unreliable availability of some signals and can provide feedback correction for unexpected external perturbations.

[1]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[2]  Jacques Droulez,et al.  Does the brain use sliding variables for the control of movements? , 1997, Biological Cybernetics.

[3]  Peter E. Latham,et al.  Optimal computation with attractor networks , 2003, Journal of Physiology-Paris.

[4]  M. Shadlen,et al.  Microstimulation of visual cortex affects the speed of perceptual decisions , 2003, Nature Neuroscience.

[5]  W. Newsome,et al.  Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. , 2001, Journal of neurophysiology.

[6]  E. Bizzi,et al.  Stability analysis of nonlinear muscle dynamics using contraction theory , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[7]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[8]  Jean-Jacques E. Slotine,et al.  On Contraction Analysis for Non-linear Systems , 1998, Autom..

[9]  J. Gold,et al.  Banburismus and the Brain Decoding the Relationship between Sensory Stimuli, Decisions, and Reward , 2002, Neuron.

[10]  A. Pouget,et al.  Efficient computation and cue integration with noisy population codes , 2001, Nature Neuroscience.

[12]  Nicolas Brunel,et al.  Firing Rate of the Noisy Quadratic Integrate-and-Fire Neuron , 2003, Neural Computation.

[13]  Nicolas Brunel,et al.  Hebbian Learning of Context in Recurrent Neural Networks , 1996, Neural Computation.

[14]  Nicolas Brunel,et al.  A Continuous Attractor Network Model Without Recurrent Excitation: Maintenance and Integration in the Head Direction Cell System , 2005, Journal of Computational Neuroscience.

[15]  Xiao-Jing Wang,et al.  Cortico–basal ganglia circuit mechanism for a decision threshold in reaction time tasks , 2006, Nature Neuroscience.

[16]  Nicolas Brunel,et al.  Mutual Information, Fisher Information, and Population Coding , 1998, Neural Computation.

[17]  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.

[18]  Emilio Salinas,et al.  Gain Modulation A Major Computational Principle of the Central Nervous System , 2000, Neuron.

[19]  David S. Touretzky,et al.  The reaching task: evidence for vector arithmetic in the motor system? , 1994, Biological Cybernetics.

[20]  Timothy D. Hanks,et al.  Microstimulation of macaque area LIP affects decision-making in a motion discrimination task , 2006, Nature Neuroscience.

[21]  M. Shadlen,et al.  A role for neural integrators in perceptual decision making. , 2003, Cerebral cortex.

[22]  R. Andersen,et al.  Multimodal representation of space in the posterior parietal cortex and its use in planning movements. , 1997, Annual review of neuroscience.

[23]  Richard H R Hahnloser,et al.  Double-ring network model of the head-direction system. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[25]  John F. Kalaska,et al.  Spatial coding of movement: A hypothesis concerning the coding of movement direction by motor cortical populations , 1983 .

[26]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

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

[28]  A. Schwartz,et al.  Motor cortical activity during drawing movements: population representation during lemniscate tracing. , 1999 .

[29]  Jean-Jacques E. Slotine,et al.  Modularity, evolution, and the binding problem: a view from stability theory , 2001, Neural Networks.

[30]  Yoshua Bengio,et al.  Credit Assignment through Time: Alternatives to Backpropagation , 1993, NIPS.