Synaptic plasticity has a fundamental importance in the context of memory capacity of neural networks. Former studies suggest that inhibitory synaptic plasticity has a crucial regulatory role in neural networks, but its effects have been widely unexplored. In this study, we utilize the model of Vogels et al. to investigate memory capacity in spiking neural networks. As Volker already presented last week, the model is a spiking neural network, where spike-time dependent inhibitory synaptic plasticity at excitatory cells balances excitation and inhibition. Patterns are learned through one-shot Hebbian learning and we measured memory capacity by the signal-to-noise ratio during the recall phase. Building on the work of Alex Metaxas, we explored how adding spike-time dependent plasticity to connections other than the already plastic inhibitory to excitatory connections, affects memory capacity.