Show Reference: "New Supervised Learning Theory Applied to Cerebellar Modeling for Suppression of Variability of Saccade End Points"

New Supervised Learning Theory Applied to Cerebellar Modeling for Suppression of Variability of Saccade End Points Neural Computation, Vol. 25, No. 6. (21 March 2013), pp. 1440-1471, doi:10.1162/neco_a_00448 by Masahiko Fujita
@article{fujita-2013,
    abstract = {A new supervised learning theory is proposed for a hierarchical neural network with a single hidden layer of threshold units, which can approximate any continuous transformation, and applied to a cerebellar function to suppress the end-point variability of saccades. In motor systems, feedback control can reduce noise effects if the noise is added in a pathway from a motor center to a peripheral effector; however, it cannot reduce noise effects if the noise is generated in the motor center itself: a new control scheme is necessary for such noise. The cerebellar cortex is well known as a supervised learning system, and a novel theory of cerebellar cortical function developed in this study can explain the capability of the cerebellum to feedforwardly reduce noise effects, such as end-point variability of saccades. This theory assumes that a Golgi-granule cell system can encode the strength of a mossy fiber input as the state of neuronal activity of parallel fibers. By combining these parallel fiber signals with appropriate connection weights to produce a Purkinje cell output, an arbitrary continuous input-output relationship can be obtained. By incorporating such flexible computation and learning ability in a process of saccadic gain adaptation, a new control scheme in which the cerebellar cortex feedforwardly suppresses the end-point variability when it detects a variation in saccadic commands can be devised. Computer simulation confirmed the efficiency of such learning and showed a reduction in the variability of saccadic end points, similar to results obtained from experimental data.},
    author = {Fujita, Masahiko},
    day = {21},
    doi = {10.1162/neco\_a\_00448},
    journal = {Neural Computation},
    keywords = {ann, learning, model, saccades, supervised-learning},
    month = mar,
    number = {6},
    pages = {1440--1471},
    posted-at = {2013-05-14 08:43:54},
    priority = {2},
    publisher = {MIT Press},
    title = {New Supervised Learning Theory Applied to Cerebellar Modeling for Suppression of Variability of Saccade End Points},
    url = {http://dx.doi.org/10.1162/neco\_a\_00448},
    volume = {25},
    year = {2013}
}

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Fujita presents a supervised ANN model for learning to either generate a continuous time series from an input signal, or to generate a continuous function of the continuous integral of a time series.

Fujita models saccade suppression of endpoint variability by the cerebellum using their supervised ANN model for learning a continuous function of the integral of an input time series.

He assumes that the input activity originates from the SC and that the correction signal is supplied by sensory feedback.

In his model, Fujita abstracts away from the population coding present in the multi-sensory/motor layers of the SC.

The superior colliculus sends motor commands to cerebellum and reticular formation in the brainstem.