Various models have been developed to shed light on neuronal mechanisms of homeostatic plasticity (HP). We focus on one such model implemented on continuous-time-recurrent neural networks. Though this HP mechanism encourages oscillatory dynamics by preventing node saturation, it was curiously detrimental to behavioral fitness when compared to non-plastic networks on several tasks (Williams, 2004, 2005). When we set out to explain this result, we discovered a type of oscillation that depends on HP’s continued regulation of circuit parameters. If HP is turned off, oscillation stops. This suggests that HP can play an enabling role in central pattern generation which has not been explored in modelling or experimental contexts. We first situate this phenomenon within the space of possibilities for HP’s involvement in oscillation. Then, we show that these “HP-enabled” oscillations are extraordinarily common in random circuits of various sizes. Finally, we describe how the degree of timescale separation between HP and neural dynamics affects HP-enabled oscillation. This analysis suggests promising avenues for dialogue between modeling and experiment.