Dynamic threshold curves and response precision in forced excitable systems

TL;DR

This paper introduces the dynamic threshold curve (DTC) as a novel tool to quantify and predict the response precision of excitable systems, such as auditory neurons, under periodic forcing and noise, revealing how DTC features influence spike timing.

Contribution

The paper presents the concept of the dynamic threshold curve (DTC) and demonstrates its effectiveness in capturing spike timing variability in excitable systems, advancing understanding of neural response precision.

Findings

DTC accurately predicts spike timing distributions.

Peaks, troughs, and slopes of DTC relate to response precision.

Framework supports modeling of auditory neuron responses.

Abstract

We investigate here various properties of the responses of excitable systems subject to periodic forcing and noise. While the properties of intrinsic oscillators, subject to added periodic signals, are well understood, much less is known about the factors that determine the response precision of excitable units, intrinsically at rest, when activated by periodic forcing and stochastic noise. One motivation for considering this issue comes from the behavior of auditory neurons. These neurons reportedly have the ability to fire spikes in a precise range of phases in response to incoming sound waves, a behavior for which the mechanism is unknown. To account for such a response precision, we introduce the notion of dynamic threshold curve (DTC), which estimates at each time the effective likelihood that noise will subsequently generate a spike. The DTC effectively summarizes, in a single curve, a representation of the response precision of an excitable model, as we demonstrate by showing that the distribution of spike times produced in this setting is well captured by the first passage time of a simple, Gaussian stochastic process to the distance to the DTC. This result shows that peaks and troughs of the DTC, but also their slopes, convey fine information about spike timing in response to noise. In particular, it explains properties of Type 2 and Type 3 excitable cells studied previously and provides a framework to predict the DTC properties necessary to support the response precision of auditory neurons, as we illustrate in a well-established auditory neuron model.