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We set up a combined modelof sparse coding bottom-up feature detectors and a subsequent attractor with horizontalw eights. It is trained with filtered grey-scale natural images. We find the following results on the connectivity: (i) the bottom-up connections establish a topographic map where orientation and frequency are represented in an ordered fashion, but phase randomly. (ii) the lateral connections display local excitation and surround inhibition in the feature spaces of position, orientation and frequency. The results on the attractor activations after an interrupted relaxation of the attractor cells as a response to a stimulus are: (i) Contrast-response curves measured as responses to sine gratings increase sharply at low contrasts, but decrease at higher contrasts (as reported for cells which are adapted to low contrasts [1]). (ii) Orientation tuning curves of the attractor cells are more peaked than those of the feature cells. They have reasonable contrast invariant tuning widths, however, the regime of gain (along the contrast axis) is small before saturation is reached. (iii) The optimalresp onse is roughly phase invariant, if the attractor is trained to predict its input when images move slightly.