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

Richard Feynman's words deeply resonate with my vision of scientific understanding. I believe we are 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 mechanisms of cognitive processes and the overarching principles of intelligence. Conversely, new experimental and analytical techniques offer unprecedented insights into the functioning of the brain. Thanks to these advances I believe 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 realize my vision, I am focusing my research on the neural circuits underlying visuotactile 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.