Raul-Cristian MUREȘAN

CS I - Systems Neuroscience, Computational Neuroscience, Computer Science

Fields of interest/specialization: Systems Neuroscience, Computational Neuroscience, Computer Science

Raul C. Mureșan, initially trained as a computer engineer and later specialized in Systems Neuroscience, is interested in the neural mechanisms that support sensory perception, cognitive processes, and behaviour. He received his PhD from the Technical University of Cluj-Napoca in collaboration with the Max Planck Institute for Brain Research in Frankfurt am Main, Germany. After a postdoc at Max Planck and Frankfurt Institute for Advanced Studies, he returned to Romania where he consolidated and founded several private research institutes, such as the Transylvanian Institute of Neuroscience, where he currently serves as president. His work focuses primarily on fundamental mechanisms of visual perception, performing experiments in humans and animal models to unravel the processes that support vision. His work also diverges into the development of novel, brain-inspired computational frameworks to power the next generation AI systems. He collaborates with over 26 research groups around the world and is one of the organizers of one of the most prestigious neuroscience summer schools worldwide, the Transylvanian Experimental Neuroscience Summer School (TENSS).

Curriculum Vitae: CV Mureșan

Google Scholar: https://scholar.google.ro/citations?user=97ZOGx0AAAAJ&hl=en

ResearchGate: https://www.researchgate.net/scientific-contributions/Raul-C-Muresan-39505324

Representative works:

Varga L., Moca V.V., Molnár B., Perez-Cervera L., Selim M.K., Díaz-Parra A., Moratal D., Péntek B., Sommer W.H., Muresan R.C., Canals S., Ercsey-Ravasz M. (2024), Brain dynamics supported by a hierarchy of complex correlation patterns defining a robust functional architecture, Cell Systems 15, 1-17.

Gal C., Tincas I., Moca V.V., Ciuparu A., Dan E.L., Smith M.L., Gliga T., Muresan R.C. (2024), Randomness impacts the building of specific priors, visual exploration, and perception in object recognition. Scientific Reports 14, 8527.

Grosu G.F., Hopp A.V., Moca V.V., Barzan H., Ciuparu A., Ercsey-Ravasz M., Winkel M., Linde H., Muresan R.C. (2023), The fractal brain: scale-invariance in structure and dynamics. Cerebral Cortex 33(8):4574–4605.

Barzan H., Ichim A.M., Moca V.V., Muresan R.C. (2022), Time-Frequency Representations of Brain Oscillations: Which One Is Better? Frontiers in Neuroinformatics 16:871904, doi: 10.3389/fninf.2022.871904.

Moca V.V., Barzan H., Nagy-Dabacan A., Muresan R.C. (2021), Time-frequency super-resolution with superlets. Nature Communications 12, 337.

Ciuparu A., Nagy-Dabacan A., Muresan R.C. (2020), Soft++, a multi-parametric non-saturating non-linearity that improves convergence in deep neural architectures. Neurocomputing, 384:376-388.

Ciuparu A. and Muresan R.C. (2016), Sources of bias in single-trial normalization procedures. European Journal of Neuroscience 43(7):861-869.

Moca V.V., Nikolic D., Singer W., Muresan R.C. (2014), Membrane resonance enables stable and robust gamma oscillations. Cerebral Cortex 24(1):119-142.