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Biophysically accurate neuron models have emerged as a very useful tool for neuroscience research. These models are based on solving differential equations that govern membrane potentials and spike generation. The level of detail that needs to be presented in the model to accurately emulate the behaviour of an organic cell is still an open question, although the timing of the spikes is considered to convey essential information. Models targeting hardware are traditionally based on fixed point implementations and low precision algorithms which incur a significant loss of information. This, in turn, could affect the functionality of a bioelectronic neuroprocessor in an undefined way. In this paper, a 32-bit floating point reconfigurable somatic neuroprocessor is presented targeting an FPGA device for real-time processing. For each individual neuron, the dynamics of ionic channels are described by a set of first order kinetic equations. A dedicated CORDIC unit is developed to solve the nonlinear functions that regulate spike generation. The results have been verified using an experimental setup that combines an FPGA device and a digital-to-analogue converter.