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# Optimal control of infinite-dimensional piecewise deterministic Markov processes and application to the control of neuronal dynamics via Optogenetics

• 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.

Mathematics Subject Classification: Primary: 93E20, 60J25, 35K58; Secondary: 49L20, 92C20, 92C45.

 Citation:

• Figure 1.  Simplified four states ChR2 channel : $\varepsilon_1$, $\varepsilon_2$, $e_{12}$, $e_{21}$, $K_{d1}$, $K_{d2}$ and $K_r$ are positive constants

Figure 2.  Simplified ChR2 three states model

Figure 3.  ChR2 three states model

Figure 4.  ChR2 channel : $K_{a1}$, $K_{a2}$, and $K_{d2}$ are positive constants defined by:

Table 1.  Expression of the individual jump rate functions and the Hodgkin-Huxley model

 $\underline {{\rm{In}}\;\;{D_1} = \left\{ {{n_0},{n_1},{n_2},{n_3},{n_4}} \right\}} :$ $\sigma_{n_0,n_1}(v,u) = 4\alpha_n(v)$, $\sigma_{n_1,n_2}(v,u) = 3\alpha_n(v)$, $\sigma_{n_2,n_3}(v,u) = 2\alpha_n(v)$, $\sigma_{n_3,n_4}(v,u) = \alpha_n(v)$ $\sigma_{n_4,n_3}(v,u) = 4\beta_n(v)$, $\sigma_{n_3,n_2}(v,u) = 3\beta_n(v)$, $\sigma_{n_2,n_1}(v,u) = 2\beta_n(v)$, $\sigma_{n_1,n_0}(v,u) = \beta_n(v)$. $\underline {{\rm{In}}\;\;{D_2} = \left\{ {{m_0}{h_1},{m_1}{h_1},{m_2}{h_1},{m_3}{h_1},{m_0}{h_0},{m_1}{h_0},{m_2}{h_0},{m_3}{h_0}} \right\}} :$ : $\sigma_{m_0h_1,m_1h_1}(v,u)=\sigma_{m_0h_0,m_1h_0}(v,u) = 3\alpha_m(v)$, $\sigma_{m_1h_1,m_2h_1}(v,u) =\sigma_{m_1h_0,m_2h_0}(v,u) = 2\alpha_m(v)$, $\sigma_{m_2h_1,m_3h_1}(v,u) = \sigma_{m_2h_0,m_3h_0}(v,u) = \alpha_m(v)$, $\sigma_{m_3h_1,m_2h_1}(v,u) = \sigma_{m_3h_0,m_2h_0}(v,u) = 3\beta_m(v)$, $\sigma_{m_2h_1,m_1h_1}(v,u) =\sigma_{m_2h_0,m_1h_0}(v,u) = 2\beta_m(v)$, $\sigma_{m_1h_1,m_0h_1}(v,u) = \sigma_{m_1h_0,m_0h_0}(v,u) = \beta_m(v)$. $\underline {{\rm{In}}\;\;{D_{ChR2}} = \left\{ {{o_1},{o_2},{c_1},{c_2}} \right\}} :$ $\sigma_{c_1,o_1}(v,u) = \varepsilon_1 u$, $\sigma_{o_1,c_1}(v,u) = K_{d1}$, $\sigma_{o_1,o_2}(v,u) = e_{12}$, $\sigma_{o_2,o_1}(v,u) = e_{21}$ $\sigma_{o_2,c_2}(v,u) = K_{d2}$, $\sigma_{c_2,o_2}(v,u) = \varepsilon_2 u$, $\sigma_{c_2,c_1}(v,u) = K_r$. $\alpha_n(v)=\frac{0.1-0.01v}{e^{1-0.1v}-1}$, $\beta_n(v)=0.125e^{-\frac{v}{80}}$, $\alpha_m(v)=\frac{2.5-0.1v}{e^{2.5-0.1v}-1}$, $\beta_m(v)=4e^{-\frac{v}{18}}$, $\alpha_h(v)=0.07e^{-\frac{v}{20}}$, $\beta_h(v)=\frac{1}{e^{3-0.1v}+1}$. (HH)\left\{ \begin{aligned} C \dot{V}(t)&= \bar{g}_Kn^4(t)(E_K - V(t)) +\bar{g}_{Na}m^3(t)h(t)(E_{Na}-V(t))\\ & \ \ \ \ \ \ \ \ \ + g_L(E_L-V(t)) + I_{ext}(t),\\ \dot{n}(t)&= \alpha_n(V(t))(1-n(t)) - \beta_n(V(t))n(t),\\ \dot{m}(t)&= \alpha_m(V(t))(1-m(t)) - \beta_m(V(t))m(t),\\ \dot{h}(t)&= \alpha_h(V(t))(1-h(t)) - \beta_h(V(t))h(t). \end{aligned} \right.
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