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In this paper, this possibility is explored presenting a modular and efficient FPGA design of an in silico spiking neural network exploiting the Izhikevich model. Field programmable gate arrays (FPGAs) can improve flexibility when simple neuronal models are required, obtaining good accuracy, real-time performance, and the possibility to create a hybrid system without any custom hardware, just programming the hardware to achieve the required functionality. For closed-loop experiments with biological neuronal networks interfaced with in silico modeled networks, several technological challenges need to be faced, from the low-level interfacing between the living tissue and the computational model to the implementation of the latter in a suitable form for real-time processing. The reason is essentially due to the design of innovative neuroprostheses where biological cell assemblies of the brain can be substituted by artificial ones. In the last years, the idea to dynamically interface biological neurons with artificial ones has become more and more urgent. 3Neuroengineering and Bio-nano Technology Lab, Dibris, University of Genova, Genova, Italy.2Information Engineering Unit, PolComIng Department, University of Sassari, Sassari, Italy.1EOLab - Microelectronics and Bioengineering Lab, Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy.Danilo Pani 1 *, Paolo Meloni 1, Giuseppe Tuveri 1, Francesca Palumbo 2, Paolo Massobrio 3 and Luigi Raffo 1