Speaker
Description
Fast mechanisms responsible for action potential generation are modulated by slower processes that can lead to complex sequences of firing regimes. We use a dataset that provides an unusual window into a progression of transitions in neural activity, from baseline excitability to depolarization block and back. The dataset consists of whole-cell patch-clamp recordings of neurons during potassium-induced spreading depolarization events, which are waves of depolarization that spread across the cortex and involve massive changes in ion concentrations. The peculiarity is that these events are partial: they do not reach all cortical layers, creating a gradient in the perturbation to neural activity.
We apply a phenomenological approach to show how this progression arises naturally from minimal dynamical constraints. On the fast timescale, the neuronal excitability class interpreted through the lens of Unfolding Theory provides a generic bifurcation diagram of essential neuronal dynamics. On the slow timescale, simple homeostatic rules and their interaction with external perturbations (such as the potassium waves) steer the neuron through the diagram, giving rise to the experimentally observed progression. The model predicts other transition sequences, which we identified in the experimental and modeling literature. This frames depolarization block as a nuanced dynamical phenomenon, with implications for modeling choices and experimental design.
