The high level of realism of spiking neuron networks and their complexity require a considerable computational resources limiting the size of the realized networks. Consequently, the main challenge in building complex and biologically accurate spiking neuron network is largely set by the high computational and data transfer demands. In this thesis, I implement several efficient models of the spiking neuron with characteristics such as axon conduction delays and spike timing-dependent plasticity in a real-time data-flow learning network. With the performance analysis, the trade-offs between the biophysical accuracy and computation complexity are defined for the different models. The experimental results indicate that the proposed real-time data-flow learning network architecture allows the capacity of over 1,188 (max.6,300, depending on the model complexity) biophysically accurate neurons in a single FPGA device.