title: New supervised learning theory applied to cerebellar modeling for suppression of variability of saccade end points.
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.
authors.affiliation: Ciel Laboratory of Brain Science, Kodaira, Tokyo 187-0021, Japan. hkfujita.ciel.lab@m.ieice.org
authors.forename: Masahiko
authors.initials: M
authors.lastname: Fujita
mesh_terms: D000222:Adaptation, Physiological; D002525:Cerebellar Cortex / Q000166:cytology*; D003198:Computer Simulation; D006801:Humans; D007266:Inhibition, Psychological; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009434:Neural Pathways / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D012438:Saccades / Q000502:physiology*