Related Experiment Video
Updated: Aug 5, 2025
17:37
Large-scale Recording of Neurons by Movable Silicon Probes in Behaving Rodents
Published on: March 4, 2012
34.8K
Data-Driven Control of Neuronal Networks with Population-Level Measurement
Minh Vu1, Bharat Singhal1, Shen Zeng1
1Department of Electrical and Systems Engineering, Washington University in St. Louis, MO, USA.
Research Square
|March 30, 2023
Summary
This study introduces a novel data-driven method to control complex neuronal networks without needing a full system model. The technique effectively regulates neural synchrony using minimal inputs and outputs.
Area of Science:
- Neuroscience
- Complex Systems
- Control Theory
Background:
- Controlling complex nonlinear neuronal networks is crucial for engineering and science.
- Existing methods often rely on detailed biophysical or simplified models.
- Learning control strategies directly from data without model assumptions is a significant research gap.
Approach:
- Leverages local network dynamics for iterative control learning.
- Avoids the need for constructing a global system model.
- Utilizes a single input and a noisy population-level output measurement.
Key Points:
- Enables effective regulation of synchrony in neuronal networks.
- Demonstrates robustness to system variations.
- Shows generalizability for various physical constraints, like charge-balanced inputs.
Conclusions:
- Presents a novel, model-free approach for neuronal network control.
- Offers a practical solution for regulating neural synchrony with limited measurements.
- Highlights the potential for broader applications in neuroscience and engineering.
