RENAULT Vincent

We use conductance based neuron models and the mathematical modeling of Optogenetics to define controlled neuron models and we address the minimal time control of these affine systems for the first spike from equilibrium. We apply tools of geometric optimal control theory to study singular extremals and we implement a direct method to compute optimal controls. When the system is too large to theoretically investigate the existence of singular optimal controls, we observe numerically the optimal bang-bang controls.

We use conductance based neuron models and the mathematical modeling of Optogenetics to define controlled neuron models and we address the minimal time control of these affine systems for the first spike from equilibrium. We apply tools of geometric optimal control theory to study singular extremals and we implement a direct method to compute optimal controls. When the system is too large to theoretically investigate the existence of singular optimal controls, we observe numerically the optimal bang-bang controls.

In this paper we define an infinite-dimensional controlled piecewise deterministic Markov process (PDMP) and we study an optimal control problem with finite time horizon and unbounded cost. This process is a coupling between a continuous time Markov Chain and a set of semilinear parabolic partial differential equations, both processes depending on the control. We apply dynamic programming to the embedded Markov decision process to obtain existence of optimal relaxed controls and we give some sufficient conditions ensuring the existence of an optimal ordinary control. This study, which constitutes an extension of controlled PDMPs to infinite dimension, is motivated by the control that provides Optogenetics on neuron models such as the Hodgkin-Huxley model. We define an infinite-dimensional controlled Hodgkin-Huxley model as an infinite-dimensional controlled piecewise deterministic Markov process and apply the previous results to prove the existence of optimal ordinary controls for a tracking problem.

In this paper we define an infinite-dimensional controlled piecewise deterministic Markov process (PDMP) and we study an optimal control problem with finite time horizon and unbounded cost. This process is a coupling between a continuous time Markov Chain and a set of semilinear parabolic partial differential equations, both processes depending on the control. We apply dynamic programming to the embedded Markov decision process to obtain existence of optimal relaxed controls and we give some sufficient conditions ensuring the existence of an optimal ordinary control. This study, which constitutes an extension of controlled PDMPs to infinite dimension, is motivated by the control that provides Optogenetics on neuron models such as the Hodgkin-Huxley model. We define an infinite-dimensional controlled Hodgkin-Huxley model as an infinite-dimensional controlled piecewise deterministic Markov process and apply the previous results to prove the existence of optimal ordinary controls for a tracking problem.

The aim of this thesis is to propose different mathematical neuron models that take into account Optogenetics, and study their optimal control. We first define a controlled version of finite-dimensional, deterministic, conductance based neuron models. We study a minimal time problem for a single-input affine control system and we study its singular extremals. We implement a direct method to observe the optimal trajectories and controls. The optogenetic control appears as a new way to assess the capability of conductance-based models to reproduce the characteristics of the membrane potential dynamics experimentally observed. We then define an infinite-dimensional stochastic model to take into account the stochastic nature of the ion channel mechanisms and the action potential propagation along the axon. It is a controlled piecewise deterministic Markov process (PDMP), taking values in an Hilbert space. We define a large class of infinite-dimensional controlled PDMPs and we prove that these processes are strongly Markovian. We address a finite time optimal control problem. We study the Markov decision process (MDP) embedded in the PDMP. We show the equivalence of the two control problems. We give sufficient conditions for the existence of an optimal control for the MDP, and thus, for the initial PDMP as well. The theoretical framework is large enough to consider several modifications of the infinite-dimensional stochastic optogenetic model. Finally, we study the extension of the model to a reflexive Banach space, and then, on a particular case, to a nonreflexive Banach space.

The aim of this thesis is to propose different mathematical models of neurons for Optogenetics and to study their optimal control. We first define a controlled version of finite dimensional deterministic models, called conductance models. We study a minimal time problem for an affine single-input system whose singularities we study. We apply a direct numerical method to observe optimal trajectories and controls. Optogenetic control appears as a new way to judge the ability of conductance models to reproduce the experimentally observed characteristics of the membrane potential dynamics. We then define a stochastic model in infinite dimension to take into account the randomness of ion channel mechanisms and the propagation of action potentials. It is a controlled piecewise deterministic Markov process (PDMP) with values in a Hilbert space. We define a large class of controlled PDMPs in infinite dimension and prove the strongly Markovian character of these processes. We treat an optimal control problem with finite time horizon. We study the Markovian decision process (MDP) included in the PDMP and show the equivalence of the two problems. We give sufficient conditions for the existence of optimal controls for the MDP, and hence the PDMP. We discuss variants for the stochastic Optogenetic model in infinite dimension. Finally, we study the extension of the model to a reflexive Banach space, and then, in a special case, to a non-reflexive Banach space.