While inhibition is a building block of biological neural networks, its role in synaptic plasticity is yet to be fully understood. So far its role has only been addressed in rate and spike-timing based models (Wilmes et al., 2016; Agnes & Vogels,2024; Miehl & Gjorgijeva, 2022). Yet, experimental results show that the hyperpolarising effect of inhibition on the sub-threshold membrane potential can alter the expression of plasticity at excitatory synapses (Hayama et al. 2013; Paille et al. 2013). To address this gap, we explore the effects of inhibition in three voltage-dependent models: the voltage-dependent plasticity model by Clopath et al. (2010), its variation as presented in Meissner-Bernard et al (2020) and our new Voltage-Dependent Pathway model (VDP). All models vary in the treatment of the post- and presynaptic activity during plasticity induction. We find differences in sub-threshold fluctuation sensitivity and inhibitory regulation of plasticity due to the operations applied to the postsynaptic membrane potential in the induction mechanism. The sensitivity to fast fluctuations decreases with increasingly strong filtering of the membrane potential. Simultaneously, the promotion of depression during a Spike-Timing-Dependent Plasticity (STDP) by inhibitory inputs increases. The inhibitory regulation of STDP in voltage-dependent plasticity models increases the competition between laterally connected neurons of the same afferent pathway compared to standard STDP models. Simulations of fully-connected, fully-plastic EI networks indicate that the strength of inhibitory regulation of voltage-dependent plasticity strongly influences the quality of learned representations in the excitatory population. While all plasticity models lead to the development of input representations in the excitatory population, e.g. receptive fields from naturalistic input statistics, the quantity and overlap of the learned structures show distinct differences between them. This work predicts that competition facilitated by inhibitory plasticity regulation is sufficient for the development of functional networks, and suggests that the degree of sub-threshold sensitivity plays a crucial role in determining the structure of the learned representations.