A nonparametric quanti¢cation of neural response ¢eld structures

摘要: The response ¢elds of higher cortical neurons are usually approximated with smooth mathematical functions for the purpose population parameterization or theoretical modeling. We used instead two nonparametric methods (principal component analysis and independent analysis), which provided a basis ¢eld clustering. Although both performed satisfactorily, principal space is more straightforward to calculate. It also gave clear preference toward smallest number functional classes.Clustering was K-means superparamagnetic clustering algorithms similar results.We show that shapes eigenvectors remain consistent regardless data sets size. This ¢nding re£ects fact were generated by same neural network encode underlying process. NeuroReport 17:963^967 � c 2006 Lippincott Williams & Wilkins.