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A Consistent Definition of Phase Resetting Using Hilbert Transform.

Sorinel A Oprisan¹

¹Department of Physics and Astronomy, College of Charleston, 66 George Street, Charleston, SC 29424, USA.

International Scholarly Research Notices

|May 30, 2017

Summary

This summary is machine-generated.

This study introduces the Hilbert Transform (HT) to accurately estimate phase resetting curves (PRCs) in neural oscillators. The HT method provides reliable phase resetting estimations, overcoming limitations of previous techniques.

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Area of Science:

Computational Neuroscience
Systems Neuroscience
Biophysics

Background:

Phase resetting curves (PRCs) are crucial for understanding neural oscillator dynamics and predicting network behavior.
Existing methods for estimating PRCs can yield contradictory results due to multiple phase definitions.

Purpose of the Study:

To introduce and validate the Hilbert Transform (HT) as a robust method for estimating the phase resetting curve (PRC) of single neural oscillators.
To address the limitations and inconsistencies associated with traditional PRC estimation techniques.

Main Methods:

Utilized the Hilbert Transform (HT) to define the phase of membrane potential oscillations.
Employed HT amplitude to estimate the PRC of individual neural oscillators.
Analyzed the sensitivity of HT amplitude and instantaneous frequency to perturbations.

Main Results:

HT amplitude and its instantaneous frequency exhibit high sensitivity to membrane potential perturbations.
The phase shift in HT amplitude between pre- and post-stimulus cycles accurately estimates the PRC.
HT-based phase resetting estimations are reliable and free from voltage threshold or isochrone method shortcomings.

Conclusions:

The Hilbert Transform offers an accurate and reliable method for estimating phase resetting curves in neural oscillators.
This approach provides a more consistent and robust understanding of neural oscillator dynamics compared to conventional methods.