Enhanced Empirical Modeling of Electrophysiological Activity in a Bundle of Myelinated Nerve Fibers: An Open-Source Implementation

The characterization of peripheral myelinated fibers through in-vitro or in-vivo electrophysiological experiments provides valuable insights into the functional aspects of the nerves. The compound action potential waveform, which reflects the electrical activity and signaling properties of the nerves, plays a crucial role in this characterization. However, analyzing and understanding the complex electrophysiological behavior of nerves solely through experiments can be challenging. To overcome this limitation, empirical models have been developed, including physical and analytical models based on differential equations. In this study, we present an improved empirical model of electro-physiological activity in a bundle of myelinated nerve fibers. The model addresses several limitations and incorporates key factors such as non-aligned nodes, occupied space by fibers, arbitrary cross-sectional shapes of the nerve, non-linear conductivity, and non-punctual electrodes. The model has been validated through simulations of the toad sciatic nerve and comparisons with experimental data from the rat infraorbital nerve. The results demonstrate the accurate fit of the model to the recorded CAP waveforms. We have also released an open-source implementation of the model using Python, providing easy accessibility and versatility for the scientific community. The implementation is robust, efficient, and based on trusted libraries, allowing for further advancements and improvements by the community. The model offers a computationally efficient and accessible approach to studying nerve activity, facilitating a better understanding of the electrophysiological behavior of myelinated nerve fibers.