Gonzalo de Polavieja, neuroscientist: We tend to follow the few who make clear decisions'

"Gonzalo de Polavieja, 56, is exasperated by the ease with which many people opine on topics without knowing anything about them. A neuroscientist trained at Oxford and Cambridge, with a PhD in quantum physics and a postdoctoral degree in mathematical neurobiology, he is currently on leave from Spain's CSIC research center and directs the Laboratory of Mathematics of Behavior and Intelligence at the Champalimaud Foundation in Lisbon, where he studies how groups of animals including humans organize themselves."

"The scientist speaks cautiously, trying to avoid mistakes with the same discipline he applies to studying neural circuits. After the interview, he wonders whether if his answers did justice to questions on topics he knows less about. In a recent lecture at the summer courses of the Menendez Pelayo International University (UIMP) in Santander, titled Applications of AI in Neuroscience and Behavior, he presented his work with the zebrafish (Danio rerio), an animal model increasingly used in biomedicine and neuroscience."

"Its transparency at early stages allows researchers to observe the brain in action; its nervous system with about 100,000 neurons is simple enough to study as a whole, and it shares more than 80% of its genes with humans. All of this makes it a privileged tool for understanding how neuronal activity translates into observable behaviors such as learning, exploration, or decision-making."

Gonzalo de Polavieja is a neuroscientist trained at Oxford and Cambridge with a PhD in quantum physics and a postdoctoral degree in mathematical neurobiology. He is on leave from Spain's CSIC and directs the Laboratory of Mathematics of Behavior and Intelligence at the Champalimaud Foundation in Lisbon, studying organization of animal and human groups. He applies cautious, disciplined methods and simple mathematical models to link neuronal circuits to behavior. He uses zebrafish as a model because early transparency, a ~100,000-neuron nervous system, and genetic similarity to humans permit whole-brain observation. He views AI as valuable for tackling biological complexity alongside mathematical approaches.

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