The Main Sequence of Saccades Optimizes Speed-accuracy Trade-off
Biological Cybernetics
Regularized Least Absolute Deviations Regression and an Efficient Algorithm for Parameter Tuning
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
2007 Special Issue: The cerebellum as a liquid state machine
Neural Networks
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Several studies have suggested different cost functions to explain the kinematic characteristics of saccades. However, these studies do not present any neural implementation of the optimization procedure they use. Instead, they are based on optimal control theory approaches that provide a global analytical solution rather than a local adaptation scheme. In this study, we propose a model comprised of an open-loop neural controller and an adaptation unit. The neural controller receives the initial target position as input. The adaptation unit, which is the neural interpretation of a simple cost function, evaluates the optimality of this controller and induces weight changes in the controller via a local learning rule. Realistic saccades are obtained with the proposed model. We speculate that the superior colliculus and the cerebellum behave quite similar to our model's neural controller and adaptation unit.