"What I cannot create, I cannot understand." - Richard Feynman

Richard Feynman's words deeply resonate with my vision of scientific understanding. I believe we're at a pivotal moment, ready to fully embrace Feynman's philosophy in our quest to understand the brain. As machine learning progresses, artificial systems are increasingly exhibiting cognitive-like behaviors, ranging from advanced sensory perception to decision-making. Unlike the brain, these systems offer us complete transparency into their inner workings, sidestepping the observational challenges the brain poses due to practical constraints. Such models could serve as an invaluable compass, directing us towards understanding the computational mechanics of cognitive processes and the overarching principles of intelligence. Conversely, as new experimental approaches and techniques offer unprecedented insights into biological cognition, neuroscience can pave the way for more human-like and interpretable artificial intelligence, fostering a symbiotic advancement of both fields.

I advocate for neurobiology and machine learning to collaborate closely. My career goal is to contribute to this interdisciplinary endeavor to better understand perception, decision-making and learning in both biological and silicon brains.

To realize my vision, I am focusing my research on the neural circuits underlying multisensory perception and decision-making in rodents. The seamless integration of diverse sensory information with past experiences is crucial to any kind of intelligent behavior. Rodents, with their rich repertoire of behaviors and amenability to advanced neural recording and manipulation techniques, offer a unique platform for testing quantitative theories of perception while advancing the synergy I envisioned above.