The paper presents a new version of a GMDH type algorithm able to perform an automatic model structure synthesis, robust model parameter estimation and model validation in presence of outliers. This algorithm allows controlling the complexity – number and maximal power of terms – in the models and provides stable results and computational efficiency. The performance of this algorithm is demonstrated on artificial and real data sets. As an example we present an application to the study of the association between clinical symptoms of Parkinsons disease and temporal patterns of neuronal activity recorded in the subthalamic nucleus of human patients.