Ph.D. student under the direction of A. CHAILLET

Thesis title: Modeling and steering brain oscillations based on in vivo optogenetics data
Thesis abstract: Neuronal oscillations are ubiquitous in the brain, both in health and disease. Nonetheless, the precise role of these oscillations is still a matter of debate, and the mechanisms by which they are generated are still poorly understood. Technological advances offer unprecedented ways to acquire and influence these oscillations. Electrode arrays and electrodes with dense recording plots now provide excellent spatiotemporal resolution of local brain activity. Moreover, the recent advent of optogenetics is revolutionizing the way of stimulating brain structures. The combination of electrophysiological recordings and optogenetics is thus particularly appealing to decipher the mechanisms of oscillation generation, their role in brain functioning, and the development of closed-loop strategies to steer brain oscillations (especially pathological ones). This project aims at developing and validating ad hoc methodologies to model, identify, analyze and control brain oscillations with these experimental tools. The challenges in that direction are numerous due to the nonlinear and spatiotemporal nature of the processes involved. To address these challenges, this project proposes to adapt or develop methodologies from control theory to the brain dynamics specificities. The performance of the developed methodologies will be confronted to experimental data of pathological oscillations linked to parkinsonian symptoms, that were collected on healthy and parkinsonian primates under optogenetics in the ANR project SynchNeuro. The aim of this PhD thesis is to develop a methodological framework, based on control engineering, to address three challenges: 1) development of a spatiotemporal model of specific brain oscillations, whose parameters are identified based on experimental data 2) formal analysis of brain oscillations onset and characterization of their dynamics in terms of magnitude, frequency and phase 3) development of closed-loop photostimulation strategies to disrupt, attenuate, or steer brain oscillations.


My research projects and interests

My primary background is in physics and neuroscience (neurophysics, if you will) although I have some practical programming experience as well, dabbling in biosignal analysis and evolutionary robotics in the past.

I am currently working on my PhD thesis under supervision of Antoine Chaillet on application of delayed neural fields models to understand and control deep brain sctructure activity using optical and electric stimulation.



  1. Orłowski, J., Chaillet, A., Sigalotti, M., & Destexhe, A. (2018, December). Adaptive scheme for pathological oscillations disruption in a delayed neuronal population model. In 2018 IEEE Conference on Decision and Control (CDC) (pp. 5189-5194). IEEE.
  2. Orłowski, J., Chaillet, A., Sigalotti, M., & Destexhe, A. (2018, December). Adaptive stimulation strategy for selective brain oscillations disruption in a neuronal population model with delays. Poster presented at 2nd IFAC Conference on Cyber-Physical & Human Systems, Miami, FL.
  3. Orłowski, J., Fleming, J., Sigalotti, M., Destexhe, A., & Chaillet, A. (2018, October). Selective oscillation disruption in a delayed neural field model of STN-GPe loop. Poster presented at Bernstein Conference 2018, Berlin, Germany.
  4. Orłowski, J., Sigalotti, M., Destexhe, A., & Chaillet, A. (2017, June). Adaptive stimulation in a delayed Wilson-Cowan model for disruption of pathological oscillations. Poster presented at Neuroscience Workshop Saclay 2017, Gif-sur-Yvette, France.
  5. Jelisavcic, M., De Carlo, M., Hupkes, E., Eustratiadis, P., Orlowski, J., Haasdijk, E., ... & Eiben, A. E. (2017). Real-world evolution of robot morphologies: A proof of concept. Artificial life, 23(2), 206-235.