Spike-Threshold Variability Originated from Separatrix-Crossing in Neuronal Dynamics

TL;DR

This paper introduces a separatrix-crossing framework to explain the dynamic variability of neuronal thresholds, linking threshold phenomena to intrinsic state space structures in neuron models.

Contribution

It proposes that threshold variability arises from separatrix-crossing in state space, providing a unified dynamic mechanism for threshold phenomena in neurons.

Findings

Separable existence in neuron models confirmed.

Threshold variation depends on crossing points in state space.

Framework verified in multiple neuron models, including Hodgkin-Huxley.

Abstract

The threshold voltage for action potential generation is a key regulator of neuronal signal transduction, yet the mechanism of its dynamic variation is still not well described. In this paper, we propose that threshold phenomena can be classified as parameter thresholds and state thresholds. Voltage thresholds which belong to the state threshold are determined by the `general separatrix' in state space. We demonstrate that the separatrix generally exists in the state space of neuron models. The general form of separatrix was assumed as the function of both states and stimuli and the previously assumed threshold evolving equation versus time is naturally deduced from the separatrix. In terms of neuron dynamics, the threshold voltage variation, which is affected by different stimuli, is determined by crossing the separatrix at different points in state space. We suggest that the separatrix-crossing mechanism in state space is the intrinsic dynamic mechanism for threshold voltages and post-stimulus threshold phenomena. These proposals are also systematically verified in example models, three of which have analytic separatrices and one is the classic Hodgkin-Huxley model. The separatrix-crossing framework provides an overview of the neuronal threshold and will facilitate understanding of the nature of threshold variability.