Large-scale neuromorphic systems demand a sophisticated communication infrastructure to support several functionalities like configuration of neuron-and-synapse blocks, pulse stimulation, routing and tracing of the neural activity. This infrastructure is usually implemented around custom-designed FPGA systems. Performance requirements for these systems significantly increase when emulating the biological counterpart at an accelerated timescale. In this paper we characterize the capability of such a system to provide accurate long-term stimulation for emulated spiking networks and tracing of their activity. The design has a capacity of 1 billion timestamped events for both stimulation and tracing with a time resolution up to 48 ns. We characterize the communication flow in terms of throughput, transmission delay, packet loss and jitter. The results show that the implementation meets the needs of learning experiments, which is an important issue for state-of-the-art neuromorphic systems.