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Updated: Jul 10, 2025

Mechanical Manipulation of Neurons to Control Axonal Development

Mechanical Manipulation of Neurons to Control Axonal Development

Published on: April 10, 2011

Learning to Control Neurons using Aggregated Measurements.

Yao-Chi Yu¹, Vignesh Narayanan¹, ShiNung Ching^1,2

¹Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA.

Proceedings of the ... American Control Conference. American Control Conference

|November 27, 2023

Summary

This summary is machine-generated.

This study introduces a novel reinforcement learning approach for controlling neuron populations using aggregated measurements. This method overcomes limitations of existing techniques by not requiring individual neuron data, enabling scalable neural population control.

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Last Updated: Jul 10, 2025

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Area of Science:

Computational Neuroscience
Control Theory
Machine Learning

Background:

Controlling neuron populations is complex due to underactuation and unknown nonlinear dynamics.
Current deep learning methods require individual neuron feedback, limiting scalability and adaptability.

Purpose of the Study:

To develop a scalable and adaptable control strategy for neuron populations.
To design a control sequence using only population-level aggregated measurements.

Main Methods:

Incorporation of reinforcement learning techniques to derive a bounded, piecewise constant control policy.
Utilizing population-level aggregated measurements instead of individual neuron feedback.
Numerical experiments on finite populations of nonlinear dynamical systems and canonical phase models.

Main Results:

Demonstrated the feasibility of the proposed learning strategy for neuron population control.
The approach effectively controls neuron populations using aggregated data, bypassing individual neuron monitoring.

Conclusions:

The proposed reinforcement learning strategy offers a viable solution for controlling complex neuron populations.
This method enhances scalability and adaptability in neural population control by leveraging aggregated measurements.