Iterative Kernel PCA for image modeling. In contrast to other patch-based modeling approaches such as PCA, ICA or sparse coding, KPCA is capable of capturing nonlinear interactions of the basis elements of the image. The original form of KPCA, however, can be only applied to strongly restricted image classes due to the limited number of training examples that can be processed. We therefore propose a new iterative method for performing KPCA, the Kernel Hebbian Algorithm (project details).

Computational modeling of fly tangential neurons. Tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. We examine whether a simplified model of these neurons can be used to predict the measured motion sensitivities in the fly. The model is tested in a robotic self-motion estimation task. (project details).