A. Formozov

During my doctorate, I gained experience in programming, computationally-intense data analysis from large-scale, long-term-acquisition experiments, and Monte Carlo simulations, as well as in optics and light detection techniques. In the third year of my doctorate training, I developed a strong interest in neuroscience, particularly in cutting-edge methods for brain-computer interfacing. Before that, I followed the trends in machine learning, including the ethical and human rights aspects of AI. I conducted intensive academic literature research during the last year of my doctorate, intending to discover scientific paths I might take and eventually find a postdoc position in neuroscience after completing my doctorate. The main question driving my curiosity and reshaping my career path was: Can we develop an interface for high-bandwidth information transfer from a neurobiological system to the computer and vice versa? After evaluating different available methods for the interrogation of the brain, I concluded that my previous expertise could be broadly applied to neuroscience. In particular, it can be used for conducting research in data-rich all-optical control of neurobiological systems using optogenetics and neuronal activity reporters. As a part of my transition to neuroscience, I received initial funding to start a small-scale project in Hamburg with Prof. Dr. Peter Soba (Group of Cellular and Molecular Neurophysiology) and Prof. Dr. J. Simon Wiegert (The Research Group Synaptic Wiring and Information Processing). At the same time, I prepared a more comprehensive research proposal supported by the Alexander von Humboldt Foundation, and this postdoc period resulted in several publications and a patent application. It validated the relevance of my choice of professional background and the practical possibility of a transdisciplinary fusion of ideas in neuroscience. Meanwhile, I also attended the Neuromatch Academy computational neuroscience course in 2021 to complement my analysis and simulation skills with neuroscience-specific models and to integrate them into the experimental practice. Previously, in my doctorate work, I gained expertise in the synergistic use of real experiments and their precise and accurate simulations; this is a well-established approach in physics. However, I acknowledge that neuroscience has distinct challenges while converging two methods; in particular, for many simulated systems in neuroscience, qualitative and computational aspects of network dynamics matter more than actual physical precision. Based on this observation, I developed several ideas to reinforce convergence between computational and experimental neuroscience. My recent transition to the Department of Neurophysiology in 2023 gives me a unique opportunity to conduct the research program I am proposing and to pursue a career path towards academic independence. I work towards this goal, contributing to the local interdisciplinary communities (all-optical interrogation of the neurobiological systems, intelligence-in-a-dish, computational neuroscience, and neuromorphic computing) in the Rhine-Neckar Metropolitan Region and at the international level.