The Hodgkin-Huxley model, decades after its first presentation, still remains a reference model in neuroscience as it has successfully reproduced the electrophysiological activity of many organisms. The primary signal in the model represents the membrane potential of a neuron. Our proposal is a parametric and simple representation of this signal based on a Frequency Modulated Möbius multicomponent model which can be defined as a flexible decomposition in waves that describes the signal morphology.

A broad simulation experiment is conducted to show the new model accurately represents the simulated Hodgkin-Huxley signal. Moreover, the model potential to predict the neuron's relevant characteristics, described with parameters of the Hodgkin Huxley model, is shown using different Machine Learning methods. The proposed model is also validated with real data from Squid Giant Axons. The comparison of the parameter configuration between the simulated and real data demonstrated the flexibility of the model as well as interesting differences.