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Related Concept Videos

Phase-lead and Phase-lag Controllers01:22

Phase-lead and Phase-lag Controllers

Understanding the working function of different types of controllers can be illustrated with practical analogies, such as adjusting a stereo's volume equalizer. Cranking up the bass involves a phase-lead controller, which functions as a high-pass filter, while increasing the treble uses a phase-lag controller, which acts as a low-pass filter. PD controllers, similar to high-pass filters, enhance the system's response to high-frequency components. PI controllers, akin to low-pass filters, manage...
Time and frequency -Domain Interpretation of Phase-lead Control01:24

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Phase-lead controllers are commonly used in various control systems to enhance response speed and stability. Adjusting the brightness on a television screen offers a practical example of phase-lead control. When contrast is enhanced, a phase-lead controller is employed. Mathematically, phase-lead control is identified when the first parameter is smaller than the second.
The design of phase-lead control involves the strategic placement of poles and zeros to balance steady-state error and system...

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Related Experiment Video

Updated: Jun 17, 2026

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

A computationally efficient adaptive phase response curve estimator for real-time closed-loop neuromodulation.

Theoden I Netoff1, Hafsa Farooqi2

  • 1Department of Biomedical Engineering, University of Minnesota, 312 Church Street SE, 7-105 Nils Hasselmo Hall, Minneapolis, Minnesota, 55455, United States.

Journal of Neural Engineering
|June 15, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive algorithm for real-time phase response curve (PRC) estimation, crucial for closed-loop neuromodulation. The efficient method enables personalized brain stimulation by tracking biological changes without recalibration.

Keywords:
Adaptive SystemsClosed loop systemsNeuromodulationNonstationarityPhase Response Curve

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Last Updated: Jun 17, 2026

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08:08

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Published on: June 24, 2015

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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Area of Science:

  • Computational Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Accurate phase response curve (PRC) models are critical for effective closed-loop neuromodulation.
  • Biological systems exhibit non-stationarity (e.g., due to medication, sleep, plasticity), limiting traditional offline PRC identification methods.
  • Real-time, adaptive algorithms are needed for embedded devices to overcome these limitations.

Purpose of the Study:

  • To develop a computationally efficient, adaptive algorithm for online PRC estimation suitable for embedded devices.
  • To enable real-time tracking of non-stationary neural dynamics for personalized neuromodulation.
  • To facilitate precise stimulation phase selection for synchronization or desynchronization.

Main Methods:

  • A parametric Fourier series model was employed to approximate the PRC.
  • A recursive Least Mean Squares (LMS) rule updated model coefficients based on prediction error after each stimulus.
  • An adaptive learning-rate schedule and spectral weighting were utilized to balance speed, precision, and smoothness.

Main Results:

  • The algorithm demonstrated robust convergence in stochastic neuron models (theta and Hodgkin-Huxley).
  • An optimal adaptive learning-rate schedule was identified, balancing speed-precision trade-offs.
  • The Fourier representation enabled instantaneous derivative calculation for real-time stimulation phase selection without buffering.

Conclusions:

  • The proposed algorithm is computationally efficient, requiring only basic arithmetic, making it ideal for resource-constrained implantable pulse generators.
  • Continuous adaptation allows tracking of non-stationary neural dynamics, supporting personalized closed-loop neuromodulation.
  • This approach eliminates the need for repeated offline recalibration, enhancing the practicality of neuromodulation therapies.