Arbor is a software library that serves to efficiently simulate large-scale networks of neurons with detailed morphological structure. It combines facilitated customization of neuronal and synaptic mechanisms with high-performance computing, enabling to use diverse backend architectures such as multi-core CPU and GPU systems [1]. On the one hand, many computational and experimental studies show the importance of diverse plasticity processes in shaping the dynamics of neuronal networks, being the basis of cognitive processes such as learning and memory. On the other hand, in recent years, studies show that many intracellular processes, especially in the dendrite, play an important role influencing single-neuron dynamics. However, due to the computational complexity, understanding the influence of the interplay between dendrites and synaptic processes on network dynamics and thus, on cognitive functions, remains beyond reach. To enable the modeling of large-scale networks of morphological neurons with different plasticity processes, we have extended Arbor by features that allow the simulation of different types of spike-driven plasticity paradigms. To demonstrate the new features, we provide examples of different computational models that we have implemented – simulating the phenomena of spike-timing-dependent plasticity (STDP) [2] and spike-driven homeostasis, stochastic calcium-based plasticity [3], heterosynaptic calcium-based plasticity [4], and synaptic tagging and capture (two-phase synaptic plasticity) [5]. Using these examples, we consider dynamics at different levels, from single synapses up to large recurrent networks. We further cross-validate all of our Arbor model implementations by comparing to other simulators, specifically to Brian 2 and to custom-developed stand-alone simulators. Finally, in an explorative investigation, we show how the power of Arbor can be harnessed to investigate the impact of the morphological structure of neurons in a network. In summary, our work contributes an important tool for the computational modeling of synaptic plasticity in networks of morphological neurons. Moreover, it demonstrates how novel insights into the functional implications of neuronal morphology at the network level can be obtained. In the future, Arbor's new functionality can enable a wide variety of studies that investigate the impact of synaptic plasticity, in conjunction with neuronal morphology, in large networks.