Neuron is the basic computing unit in brain-inspired neural networks. Although a multitude of excellent artificial neurons realized with conventional transistors have been proposed, they might not be energy and area efficient in large-scale networks. The recent discovery of ferroelectricity in hafnium oxide (HfO2) and the related switching phenomena at the nanoscale might provide a solution. This study employs the newly reported accumulative polarization reversal in nanoscale HfO2-based ferroelectric field-effect transistors (FeFETs) to implement two key neuronal dynamics: the integration of action potentials and the subsequent firing according to the biologically plausible all-or-nothing law. We show that by carefully shaping electrical excitations based on the particular nucleation-limited switching kinetics of the ferroelectric layer further neuronal behaviors can be emulated, such as firing activity tuning, arbitrary refractory period and the leaky effect. Finally, we discuss the advantages of an FeFET-based neuron, highlighting its transferability to advanced scaling technologies and the beneficial impact it may have in reducing the complexity of neuromorphic circuits.