Dr. Yurii Vlasov is a Grainger Engineering Breakthroughs Initiative (GEBI) Founder Professor of Engineering at the University of Illinois at Urbana-Champaign. He is tenured with the departments of Electrical and Computer Engineering, Materials Science and Engineering, and Bio-Engineering, as well as Carle Illinois College of Medicine. At the UIUC he established the Integrated NeuroTechnology lab devoted to development of advanced engineering approaches aimed at reverse engineering of the brain circuits. The major themes include development of silicon-based nanofluidic and nanophotonic neural probes, in-vivo neurobiological experiments with massive recording and manipulation of brain activity, and, lastly, development of machine-learning algorithms to analyze large neural datasets.
Our goal is to understand basic principles of cortical computations, from the circuit to systems levels. We focus on understanding how the ethologically-relevant features of a sensory scene are extracted from the raw sensory flow, where this information is parsed, and how it guides complex behavior.
One of our major projects is focused on primary (S1 or barrel cortex) and secondary (S2) somatosensory cortices in rodents that process information from their whiskers. We combine electrophysiology and optogenetics to record neural activity while animals actively navigate in virtual reality and solve behavioral tasks. Correlations of brain activity with animal behavior and choices provide insights on mechanisms of cortical processing.
- Electophysiology with multi-electrode silicon probes to record from a massive number of neurons across many brain regions simultaneously in alive and behaving animals.
- Behaviorial paradigms in virtual reality to study neural circuits in almost natural environment while mice are engaged in goal-directed behavior. Virtual reality systems allow full control over behaviorial tasks and quantitative measurements of resulting behavior.
- Optogenetics to identify and record activity of specific cell types during behavior and for manipulating neural circuits to reverse-engineer their functionality.
- Neuroanatomy leveraging new viral, genetic, and computational tools to provide insights into brain circuits functionality.
- Machine learning based analytical methods to extract dynamical patterns of neural activity that are correlated with animal behavior and choice.
N.Sofroniew, Y.Vlasov, S. Hires, J.Freeman, K.Svoboda, “Neural coding in barrel cortex during whisker-guided locomotion”, eLife;4:e12559 (2015))
Additional Campus Affiliations
Professor, Materials Science and Engineering
Professor, Micro and Nanotechnology Lab
Institute Affiliate, Beckman Institute for Advanced Science and Technology
Professor, Carle Illinois College of Medicine
Founder Professor, Electrical and Computer Engineering
Iftimia, N., Kono, J., Wetzel, C., Ramachandran, S., Vlasov, Y. A., & Zuegel, J. D. (2017). Welcome to CLEO:2017! 2017 Conference on Lasers and Electro-Optics, CLEO 2017 - Proceedings, 2017-January.
Barwicz, T., Taira, Y., Lichoulas, T. W., Boyer, N., Martin, Y., Numata, H., ... Fortier, P. (2016). A novel approach to photonic packaging leveraging existing high-throughput microelectronic facilities. IEEE Journal on Selected Topics in Quantum Electronics, 22(6), . https://doi.org/10.1109/JSTQE.2016.2593637
Barwicz, T., Boyer, N., Janta-Polczynski, A., Morissette, J. F., Thibodeau, Y., Patry, L., ... Fortier, P. (2016). A metamaterial converter centered at 1490nm for interfacing standard fibers to nanophotonic waveguides. In 2016 Optical Fiber Communications Conference and Exhibition, OFC 2016  (2016 Optical Fiber Communications Conference and Exhibition, OFC 2016). Institute of Electrical and Electronics Engineers Inc..
Gill, D. M., Xiong, C., Proesel, J. E., Rosenberg, J. C., Orcutt, J., Khater, M., ... Green, W. M. J. (2016). Demonstration of Error-Free 32-Gb/s Operation From Monolithic CMOS Nanophotonic Transmitters. IEEE Photonics Technology Letters, 28(13), 1410-1413. . https://doi.org/10.1109/LPT.2016.2545525
Gokmen, T., & Vlasov, Y. (2016). Acceleration of deep neural network training with resistive cross-point devices: Design considerations. Frontiers in Neuroscience, 10(JUL), . https://doi.org/10.3389/fnins.2016.00333