Modelling the memory of unmyelinated axons: Integration of a data-driven approach with physiological memory concept

This study aims to present a simplified and resource-efficient computational model for predicting activity-dependent conduction velocity changes in unmyelinated axons, serving as a complementary tool to Hodgkin-Huxley models. Our approach is based on the concept of 'memory', where the speed of action potentials is modulated by prior activity. We utilized microneurography data from 95 mechano-insensitive C-fibres of healthy human participants, including both sexes, across various stimulation protocols to optimize model parameters. The model incorporates linear long-term and non-linear short-term memory components, effectively predicting propagation speed by convolving the history of recorded action potentials with the memory function. The proposed one-dimensional and two-dimensional memory functions yielded low mean squared errors in predicting the propagation speed of subsequent action potentials. This computational framework provides insights into dynamics of unmyelinated axons under varying conditions, enhancing our understanding of signal processing along the axon and its short-term memory capabilities. Additionally our model demonstrates rapid computation times suitable for real-time applications in electrophysiological experiments. This study introduces a novel model that simulates activity-dependent conduction velocity changes in unmyelinated axons, which is crucial for effective signal processing during conduction. Unlike Hodgkin-Huxley models that are computationally intensive and complex, our approach leverages fibre 'memory' to capture how prior activity influences conduction. With fewer parameters required to fit diverse datasets, including patient data, our highly efficient model enables faster simulations than Hodgkin-Huxley models and facilitates the analysis of spike train propagation over long distances, and it is therefore suitable for modelling peripheral axons that extend up to 1 m. KEY POINTS: Unmyelinated axons, which are present in the peripheral and central nervous system, exhibit conduction velocity changes influenced by previous fibre activity, creating a form of fibre 'memory'. This study presents a novel computational model that predicts conduction velocity changes in unmyelinated axons based on prior activity, providing a faster and more efficient addition to complex Hodgkin-Huxley models. The new model incorporates both linear long-term and non-linear short-term memory components, demonstrating rapid computation times suitable for real-time applications. The model effectively captures the dynamics of nerve fibres, enhancing our understanding of axonal signal processing. This work offers insights into how previous activity influences axonal behaviour, informing future research on neurological disorders associated with altered nerve function.