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Minimal time spiking in various ChR2-controlled neuron models
Vincent Renault1, Michèle Thieullen2, Emmanuel Trélat3
1Laboratoire de Probabilités et Modèles Aléatoires, CNRS UMR 7599, Sorbonne Universités, UPMC Univ Paris 06, 75005, Paris, France. vincent.renault7@gmail.com.
Journal of Mathematical Biology
|July 1, 2017
Summary
This study explores optogenetics and neuron models to control neural systems. Researchers found optimal control strategies for precise neuron activation, including bang-bang controls for complex systems.
Area of Science:
- Computational Neuroscience
- Control Theory
- Biophysics
Background:
- Optogenetics enables precise control of neuron activity using light.
- Understanding the minimal time to activate neurons is crucial for neural engineering.
- Affine systems present unique challenges in optimal control.
Purpose of the Study:
- To define controlled neuron models using mathematical optogenetics.
- To determine the minimal time control for neuron activation from equilibrium.
- To investigate optimal control strategies for neural systems.
Main Methods:
- Utilizing conductance-based neuron models.
- Applying mathematical modeling of optogenetics.
- Employing geometric optimal control theory and direct computation methods.
- Numerical observation of bang-bang controls for large systems.
Main Results:
- Defined controlled neuron models for optogenetic studies.
- Identified optimal control strategies for achieving the first spike from equilibrium.
- Demonstrated the effectiveness of bang-bang controls in computationally intensive scenarios.
Conclusions:
- Optimal control theory provides a framework for precise neural activation.
- Bang-bang controls are a viable numerical solution for complex neural system control.
- This research advances the understanding of neural system dynamics and control.
