Nerve fiber networks connecting different brain regions are the structural foundation of brain dynamics and function. Neural connectomes have been frequently described as binary networks, in basic terms of present or absent connections. By contrast, recent approaches have provided more detailed characterizations of connectomes with weighted-strength connections. However, the topological analysis of weighted networks still has methodological limitations. As a consequence, many investigations of neural network are performed on unweighted networks obtained via binarization, and the functional impact of the unweighted versus the weighted neural networks is unclear. Here we show, for the widespread case of excitable dynamics, that the excitation patterns observed in weighted and unweighted networks are nearly identical, if an appropriate threshold is selected. We generalize this observation to different excitable models, and formally predict the network threshold from the intrinsic model features. The network-binarizing capacity of excitable dynamics suggests that neural activity patterns may primarily depend on binary topological properties of neural networks. Moreover, it appears reasonable to assess topological properties of weighted networks with the standard graph-theoretical tools for binarized networks. Our findings have implications for the functional interpretation of connectome data in neuroscience as well as diverse other systems governed by excitable dynamics.