In healthy women, fluctuations in hormones including progesterone and oestradiol lead to functional changes in the brain over the course of each menstrual cycle. Though considerable attention has been directed towards understanding changes in human cognition over the menstrual cycle, changes in underlying processes such as neural plasticity have largely only been studied in animals. In this study we explored predictive coding and repetition suppression via the roving mismatch negativity paradigm as a model of short-term plasticity (Garrido, Kilner, Kiebel, et al., 2009), and Hebbian learning via visual sensory long-term potentiation (LTP) as a model of long-term plasticity (Teyler et al., 2005). Electroencephalography (EEG) was recorded in 20 females during their early follicular and mid-luteal phases. Event-related potential (ERP) analyses were complemented with dynamic causal modelling (DCM) to characterise changes in the underlying neural architecture. More sustained variability in the ERP response to a change in tone during the luteal phase are interpreted as a delayed habituation of the P3a component in the luteal relative to the follicular phase. The additional increased forward connection strength over tone repetitions compared to the follicular phase suggests that, in this phase, females may be less efficient when processing deviations from predicted sensory input (error). In contrast, there appears to be no reliable change in sensory LTP. This suggests that predictive coding, but not Hebbian plasticity is modified in the mid-luteal compared to the follicular phase, at least at the days of the menstrual cycle tested. This finding implicates the human menstrual cycle in complex changes in neural plasticity and provides further evidence for the importance of considering the menstrual cycle when including females in electrophysiological research.