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Biophys J. 2016 Aug 23;111(4):798-812. doi: 10.1016/j.bpj.2016.07.027.

Identification of Bifurcations from Observations of Noisy Biological Oscillators.

Author information

1
Laboratory of Sensory Neuroscience, The Rockefeller University, New York, New York.
2
Laboratory of Sensory Neuroscience, The Rockefeller University, New York, New York; Howard Hughes Medical Institute, The Rockefeller University, New York, New York. Electronic address: hudspaj@rockefeller.edu.

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. Using an improved mechanical-load clamp to coerce a hair bundle to traverse different bifurcations, we find that a bundle operates within at least two functional regimes. When coupled to a high-stiffness load, a bundle functions near a supercritical Hopf bifurcation, in which case it responds best to sinusoidal stimuli such as those detected by an auditory organ. When the load stiffness is low, a bundle instead resides close to a subcritical Hopf bifurcation and achieves a graded frequency response-a continuous change in the rate, but not the amplitude, of spiking in response to changes in the offset force-a behavior that is useful in a vestibular organ. The mechanical load in vivo might therefore control a hair bundle's responsiveness for effective operation in a particular receptor organ. Our results provide direct experimental evidence for the existence of distinct bifurcations associated with a noisy biological oscillator, and demonstrate a general strategy for bifurcation analysis based on observations of any noisy system.

PMID:
27558723
PMCID:
PMC5002087
DOI:
10.1016/j.bpj.2016.07.027
[Indexed for MEDLINE]
Free PMC Article

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