Correlation-based feature detection using pulsed neural networks

The feature extraction and detection in visual scenes set up the basis for robust image processing and scene analysis. While the receptive fields of simple cells in the visual cortex are modeled by Gabor functions, simple cells are commonly treated as linear filters. In this paper, we demonstrate how the non-linear operations on pulses like correlation, synchronization and detection of decorrelation can be used for implementation of feature detectors. Using essentially two data-driven adaption rules dependent on dendritic currents and to membrane potentials, linear detection of intensity gradients can be realized. As a technical application, a feature detector sensitive to orientation is presented.