Research

Research in my group addresses fundamental questions in neuroscience by integrating theoretical approaches—mathematical modeling, statistical physics, and machine learning—with experimental data. We are currently focused on understanding how the visual system supports complex functions such as object recognition and tracking, approaching this problem from complementary perspectives.

Functional Models

Neuroanatomical evidence shows that the visual system consists of multiple interconnected areas arranged in a shallow hierarchy. How is visual information transformed along this hierarchy? Do different areas gradually convert sensory input into higher-level representations, as in deep learning models, or does each area specialize in distinct computations? To explore these questions, we develop and analyze data-driven models of the visual hierarchy.

Mechanistic Models

How do neural responses to visual stimuli emerge from the collective dynamics of neurons? We investigate this process across scales—from dendritic computations within single cells to interactions within local circuits and between cortical areas. Using a physics-inspired approach, we build and test simple models constrained by experimental data to identify the mechanisms underlying visual processing.

Normative Models

Why are neural circuits organized the way they are? Are the distinctive features of brain networks merely biological constraints, or do they confer specific computational advantages? We study these questions using simplified models that test how circuit architecture influences computation, guided by principles from statistical physics.

If you are interested in joining or collaborating with us, or would like to learn more about our work, please get in touch.