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Learning to Control Neurons using Aggregated Measurements
Yao-Chi Yu1, Vignesh Narayanan1, ShiNung Ching1,2
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 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.
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.
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