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State-space optimal feedback control of optogenetically driven neural activity.

M F Bolus1, A A Willats1, C J Rozell2

  • 1Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, United States of America.

Journal of Neural Engineering
|September 15, 2020
PubMed
Summary

Researchers developed a novel feedback control system using optogenetics and state-space models for real-time brain circuit manipulation. This method effectively controlled neuronal firing rates in awake mice, advancing neuroscience research.

Keywords:
closed-loopcontrolestimationfiring ratein vivooptogeneticsstate spacethalamus

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Area of Science:

  • Neuroscience
  • Control Systems Engineering
  • Biomedical Engineering

Background:

  • Advances in optogenetics and neuronal recording enable real-time brain circuit manipulation.
  • Controlling neuronal activity with precision presents significant technical challenges.
  • The thalamocortical circuit in awake mice serves as a model system for studying neural dynamics.

Purpose of the Study:

  • To develop and experimentally validate a state-space optimal control framework for real-time, graded optogenetic manipulation of neuronal activity.
  • To address challenges in achieving precise, continuous control over neuronal firing rates.
  • To demonstrate the applicability of this control approach in the somatosensory thalamus of awake mice.

Main Methods:

  • Closed-loop optogenetic control using channelrhodopsin-2 stimulation in the somatosensory thalamus.
  • State-space linear dynamical system models to approximate light-to-spiking relationships.
  • Linear quadratic optimal control for designing state feedback gains.
  • Parameter-adaptive Kalman filtering for robust state estimation.

Main Results:

  • Effective feedback control of single-neuron firing rate in the thalamus of awake animals was achieved.
  • Graded optical stimulation did not synchronize simultaneously recorded neurons, highlighting population heterogeneity.
  • Simulated multi-output feedback control demonstrated improved population control and generalizability.

Conclusions:

  • This study presents the first experimental application of state-space model-based feedback control for optogenetic stimulation.
  • The combined approach of state-space modeling and linear quadratic optimal control offers a robust framework for complex neural circuit control.
  • This methodology enables adaptive interaction with neural dynamics, facilitating a deeper understanding of brain function and potential therapeutic applications.