For comprehensive understanding of how neurons communicate with each other, new tools need to be developed that can accurately reproduce and mimic the behaviour of such neurons in real-time. The proposed design in this thesis models an Inferior Olivary Nucleus network on an FPGA device, with a maximised amount of simulated neurons for the given FPGA family type. This has been achieved by the usage of a highly pipelined hybrid neuron network, which executes optimally scheduled floating-point operations that, together with open source IP, has resulted in a cost-effective solutions, capable of simulating responses faster or on par with their biological counterparts.