A nonparametric quanti¢cation of neural response ¢eld structures

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