To conclude, the RF-LISSOM model described in this article demonstrates that cortical self-organization can be based on short-range excitation, long-range inhibition, and Hebbian weight adaptation. Starting from random-strength connections, neurons develop localized receptive fields and lateral interaction profiles cooperatively and simultaneously. Such self-organization stores long-range activity correlations between feature-selective cells into the lateral connections. During visual processing, this information is used to eliminate redundant information, and enhance the selectivity of cortical cells. The recurrent lateral interactions introduce a strong nonlinearity into cortical cell responses that makes cells not just linear filters, but more like feature detectors whose selectivity is invariant to contrast. The self-organizing processes could remain active even in the adult animal, continually adapting cortical response properties to match the visual environment. The model demonstrates that not only cortical structure, but also cortical response characteristics could be emergent properties driven by activity-dependent self-organization.