Optimal control with an electrophysiology experiment in the loop
Predicting the response of a system to a specific input is a very important task in engineering and nature in general. To accurately predict the response, we developed a closed-loop algorithm to perform model selection based on the output of a reference system obtained following a known input. We applied our algorithm to identify the most accurate model to predict the output of a lab experiment, which consists of light sensitive cells. Using light signals, one can control the output current of the cell. We integrated the experiment in a closed-loop framework, which brought up a constraint on the computation time. Indeed, the recordings of the output current is limited in time, meaning that the model selection has to be performed as fast as possible. To do that, we leveraged the Julia package Sockets.jl and the high speed intranet to export the computation on a server, where the required code had been precompiled. This allowed us to efficiently identify the most accurate model to use for our experiment.
I obtained my PhD in Theoretical Physics at the Swiss Federal Institute of Technology in Lausanne (EPFL) respectively in 2020. From Feb.2022 to Oct.2024, I was a Director's Postdoc Fellow at the Los Alamos National Laboratory (LANL) and also affiliated with the Center for Nonlinear Studies (CNLS). I am currently a Postdoctoral Research Fellow in the Living Systems Institute at the University of Exeter. My research focuses on complex network-coupled dynamical systems, the identification of their local/global vulnerabilities against external perturbations and their control. I am currently working on open and closed loop algorithms for the control of networks of neurons which are used in experiments.