This article presents a general approach for employing lesion analysis to address the fundamental challenge of localizing functions in a neural system. We describe functional contribution analysis (FCA), which assigns contribution values to the elements of the network such that the ability to predict the network's performance in response to multilesions is maximized. The approach is thoroughly examined on neurocontroller networks of evolved autonomous agents. The FCA portrays a stable set of neuronal contributions and accurate multilesion predictions that are significantly better than those obtained based on the classical single lesion approach. It is also used for a detailed synaptic analysis of the neurocontroller connectivity network, delineating its main functional backbone. The FCA provides a quantitative way of measuring how the network functions are localized and distributed among its elements. Our results question the adequacy of the classical single lesion analysis traditionally used in neuroscience and show that using lesioning experiments to decipher even simple neuronal systems requires a more rigorous multilesion analysis.