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The recent surge in Machine Learning (ML) and Artificial Intelligence (AI) studies brought new opportunities and challenges to neuroscience. Here we have far simpler systems than the brain, which we design ourselves, yet we do not fully understand them. Did we unlock some of the mysteries of the mind? Are we on the right track towards a mechanistic theory for cognition, or are we climbing the highest summit we found, hoping to reach the moon? We need more fundamental theory to answer these questions.
These problems are not purely theoretical. Inspired by the brain’s circuits, cutting-edge AI technology can match—and sometimes surpass—human-level performance. However, our methods rely on noisy training of large neural ensembles, and we do not know why they work so well. As a result, our huge corpus of applications is on shaky ground. Fortunately, we have (limited) access to a magical computation machine engineered by millions of years of evolution. To name a few of its merits: it is very good at generalizing, can apply previous experience to new problems, and is quite resilient to manipulations and adversarial intervention. Our state-of-the-art technology still struggles with all these issues. We employ recent advances in ML to understand better how living brains learn and function. Our insights help design tools for better and more reliable technology — a true synergy between neuroscience and AI research.
Tackling these issues requires a combined effort. We rely on our collective knowledge of biology, theoretical physics, and software engineering. We are constantly looking for researchers and students interested and passionate about understanding the brain through an interdisciplinary approach.
Looking for graduate students and postdoc researchers with a background in physics, math, CS, engineering, or computational biology.