Modeling the Memory of Unmyelinated Axons: Integration of a Data-Driven Approach with Physiological Memory Concept
In this study, we present a simplified and resource-efficient computational model to predict activity-dependent conduction velocity changes of action in unmyelinated axons. This model serves as a complementary tool to Hodgkin-Huxley models. Our approach is based on the concept of "memory," where the speed of subsequent action potentials is modulated by prior activity. We utilized microneurography data from 95 mechano-insensitive C-fibers 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. By convolving the history of recorded action potentials with the memory function, the model can effectively predict the propagation speed of subsequent action potentials with low mean squared errors for the proposed one-dimensional and two-dimensional memory functions. This computational framework provides insights into the dynamics of unmyelinated axons under varying conditions and thus in signal processing along the axon and the short-term memory of axons. The model's rapid computation times make it suitable for real-time applications in electrophysiological experiments.