Research in my lab combines experimental work, data analysis and computational modeling aimed at developing new insights into psychiatric and neurological phenomena. On the experimental side, we record brain activity using EEG from healthy subjects and from various patient populations. Using mathematical analyses and machine learning techniques we develop neuromarkers to quantify the underlying neural dynamics and help in the diagnosis of patients. Our major focus is on epilepsy and on schizophrenia, but we study other related phenomena. Another line of experimental research in the lab focuses on brain computer interfaces and neurofeedback. We develop new approaches to decode neural activity in real-time and novel neurofeedback treatments.
On the theoretical side, the lab develops and studies computational models of neuronal networks to gain insights into how changes in neural dynamics lead to brain disorders and how neural plasticity may assist in restoring healthy neural dynamics. In particular, we use information theoretic tools to study the evolution of recurrent networks that optimize the representation of information. For example, using this framework, we developed models for tinnitus and for synaesthesia. A major focus of the lab is on the theory of critical brain dynamics, which hypothesizes that the brain operates near a phase-transition, in a manner similar to physical systems near a phase-transition.
The group is highly interdisciplinary, and includes students with computational background, who come from various disciplines such as cognitive sciences, computer science, biomedical engineering, physics and more.
Lab website: Computational Psychiatry Lab