Representing Beliefs in a Changing World

Speaker:

Cristina Savin, PhD

Associate Professor of Neural Science and Data Science  

Center for Neural Science, NYU School of Arts & Science

Abstract:

Dr. Cristina Savin’s team in the Artificial & Biological Computation Lab investigates how neural systems represent and update beliefs to navigate uncertainty and change. By integrating tools from machine learning, probabilistic modeling, and experimental neuroscience, her work sheds light on the principles governing learning and memory at the level of neural circuits. Her approach bridges biological insights and computational frameworks, offering new perspectives on how dynamic and flexible cognitive processes are implemented in the brain. In this talk, Dr. Savin will explore how beliefs about the world are encoded and adapted by neural systems in response to shifting environments. She will present recent findings from her lab, demonstrating how probabilistic principles underlie the brain’s ability to manage uncertainty and adapt its internal models. These findings are informed by empirical data and computational simulations, which reveal the key roles of neural plasticity and efficient coding in belief representation. Furthermore, she will discuss parallels between biological and artificial systems, illustrating how insights from neuroscience can inspire advances in artificial neural networks. By examining how neural circuits achieve adaptability and robustness, this talk will offer a deeper understanding of the computational strategies that enable complex decision-making and learning in both natural and engineered systems.

Dr. Cristina Savin earned her Ph.D. in Computational Neuroscience from the Goethe University in Frankfurt, Germany, in 2010. Subsequently, she worked as a postdoctoral researcher at Cambridge University, developing normative models of memory. This was followed by a short stint at ENS in Paris, modeling probabilistic computation in spiking neurons, and an independent research fellowship at the Institute of Science and Technology (IST) Austria, building statistical tools for quantifying learning in multiunit recordings. In 2017, Dr. Savin joined New York University as an Assistant Professor, where she was promoted to Associate Professor in May 2024. Dr. Savin has received several honors, including a Google Faculty Research Award and a Collaborative Research in Computational Neuroscience (CRCNS) R01 grant from the National Institutes of Mental Health.