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} }

See the CiteULike entry for more info, PDF links, BibTex etc.

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.⇒