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Recent results have shown the importance of the freely diffusing gas nitric oxide (NO) in modulation of synaptic activity. This paper presents a review of current research into the role of diffusing neuromodulators in both real and artificial neural networks. Firstly, we describe a model of NO diffusion from realistic structures, and detail several important results highlighting the role of source structure in the diffusion process. Secondly, we review the application of such processes to artificial neural networks used for robot control. Evidence is presented that such networks are more amenable to the evolutionary computation approach. We conclude with a discussion of future work, including a potential analytical framework for such networks.