Identification of Bifurcations from Observations of Noisy Biological Oscillators

TL;DR

This paper presents a method to identify and assess the proximity of biological oscillators, like hair bundles, to different bifurcations despite environmental noise, enhancing understanding of their functional behavior.

Contribution

It introduces a novel approach to detect and differentiate bifurcations in noisy biological oscillators, advancing analysis of their dynamic states.

Findings

Successfully distinguishes bifurcation types in noisy data

Quantifies proximity to bifurcations in biological systems

Improves understanding of hair bundle responses

Abstract

Hair bundles are biological oscillators that actively transduce mechanical stimuli into electrical signals in the auditory, vestibular, and lateral-line systems of vertebrates. A bundle's function can be explained in part by its operation near a particular type of bifurcation, a qualitative change in behavior. By operating near different varieties of bifurcation, the bundle responds best to disparate classes of stimuli. We show how to determine the identity of and proximity to distinct bifurcations despite the presence of substantial environmental noise.