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Related Experiment Video

Updated: Jan 17, 2026

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

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

Published on: June 24, 2015

Shaping robust dynamic inversion control of neural cell dynamics.

Rongting Yue¹, Yen-Che Hsiao¹, Abhishek Dutta¹

¹Department of Electrical and Computer Engineering, University of Connecticut, Storrs, 06269, United States of America.

Biomedical Physics & Engineering Express

|September 17, 2025

Summary

This summary is machine-generated.

This study introduces Shaping Robust Dynamic Inversion (SRDI), a novel control technique for precise neural control. SRDI effectively enforces neuron spiking despite model uncertainty and noise, outperforming existing methods.

Keywords:

error shaping model-based control neural dynamics neuron firing nonlinear robust control voltage spike

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Last Updated: Jan 17, 2026

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Area of Science:

Computational Neuroscience
Nonlinear Control Systems
Biophysics

Background:

Controlling neuronal electrical activity is crucial for understanding brain function and developing neural prosthetics.
Existing methods struggle with model uncertainty and synaptic noise, limiting precise control over neuron spiking.

Purpose of the Study:

To develop a robust nonlinear control technique for enforcing precise membrane potential spiking in single neurons and small networks.
To address challenges posed by model uncertainty and synaptic noise in neural control.

Main Methods:

Propose Shaping Robust Dynamic Inversion (SRDI), a robust nonlinear control technique.
Utilize dynamic inversion of neuronal dynamical systems combined with error surface shaping.
Derive a current control signal to enforce membrane potential spiking.

Main Results:

SRDI successfully achieved controlled neuron spiking in Hodgkin-Huxley, integrate-and-fire, and FitzHugh-Nagumo models.
SRDI demonstrated superior robustness compared to classical dynamic inversion.
SRDI exhibited faster computational time than linear model predictive control.

Conclusions:

SRDI provides precise and efficient neural control by effectively managing nonlinearities, noise, and model uncertainty.
The technique enables controlled timing for single spikes, spike trains, and small neuronal networks.
SRDI represents a significant advancement in robust nonlinear control for neuroscience applications.