Speaker
Description
Depolarization block (DB) occurs when strong depolarization prevents neurons from generating action potentials. It is critically involved in several brain disorders, including epilepsy, migraine, and stroke, but also serves physiological roles, for example in odor encoding. Despite the relevance of DB for brain function and dysfunction, relatively few modelling studies directly address its underlying mechanisms. At the same time, DB states systematically emerge in models of neuronal spiking, bursting, or seizure activity when parameters are varied, suggesting that DB is a robust component of neuronal dynamics.
Here we exploit a rare dataset of whole-cell patch-clamp recordings that reveals a progression of transition patterns from baseline excitability to DB and back. Using dynamical systems theory, we unfold the minimal dynamical structures that enable Type I excitability as in the recorded pyramidal neurons. Coupling this minimal neuron model to slow variables representing collective homeostatic processes naturally reproduces the observed progression. The framework explains DB ubiquity in neuron models and predicts distinct dynamical types of DB with different experimental signatures and responses to perturbations. This frames depolarization block as a nuanced dynamical phenomenon and provides constraints for both experimental and modelling design.
