Neural Comput - New supervised learning theory applied to cerebellar modeling for suppression of variability of saccade end points.

Tópicos

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Resumo

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.

Resumo Limpo

new supervis learn theori propos hierarch neural network singl hidden layer threshold unit can approxim continu transform appli cerebellar function suppress endpoint variabl saccad motor system feedback control can reduc nois effect nois ad pathway motor center peripher effector howev reduc nois effect nois generat motor center new control scheme necessari nois cerebellar cortex well known supervis learn system novel theori cerebellar cortic function develop studi can explain capabl cerebellum feedforward reduc nois effect endpoint variabl saccad theori assum golgigranul cell system can encod strength mossi fiber input state neuron activ parallel fiber combin parallel fiber signal appropri connect weight produc purkinj cell output arbitrari continu inputoutput relationship can obtain incorpor flexibl comput learn abil process saccad gain adapt new control scheme cerebellar cortex feedforward suppress endpoint variabl detect variat saccad command can devis comput simul confirm effici learn show reduct variabl saccad end point similar result obtain experiment data

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