Network structure governs Drosophila brain functionality.

How intelligence emerges in living beings is a fundamental but largely unanswered question in neuroscience. To address this challenge, we leveraged the largest available data set of adult Drosophila connectome, and constructed a comprehensive computational framework using simplified neuronal activation mechanisms to mimic the observed activation behavior within the connectome. The results revealed that even with rudimentary neuronal activation mechanisms, models grounded in real neuronal network structures can generate activation patterns strikingly analogous to those observed in the actual brain. A significant discovery was the consistency of activation patterns across various neuronal dynamic models with the same network structure. This consistency results therefore underscore the pivotal role of network topology in neural information processing, but challenge the prevailing view that solely relies on neuron count or complex individual neuron dynamics. Further analysis demonstrated a near-complete separation of the visual and the olfactory systems at the network level. Moreover, we found that the network distance, rather than spatial distance, is the primary determinant of activation patterns, and also that a reconnect rate of at least 1