Short CV

Anna Levina received her PhD in Mathematics from Göttingen University, Germany, in 2008. Afterward, she worked as a postdoctoral researcher at the Max Planck Institute for Dynamics and Self-Organization, the Bernstein Center Göttingen, and ISTA Austria. In 2017, she received a Sofja Kovalevskaja Award that funded her research group at the University of Tübingen, where she became an associate professor in 2025. Her research focuses on developing models of neural population activity, criticality, and self-organization. In particular, she uses tools from mathematics, physics, and machine learning to understand activity in cultures, identify and explain intrinsic timescales in the brain, and uncover rules of synaptic plasticity.

Title of the talk

Simple models and expressive features to understand population dynamics

Abstract
In this talk, Anna Levina will present recent advances in characterizing and modeling population bursting activity in developing neuronal networks in vitro. The presentation will discuss three complementary approaches: a low-dimensional dynamical systems model fitted via simulation-based inference that reveals invariant bursting across excitable, oscillatory, and bistable regimes, leading to a compact "effective excitability" metric capable of discriminating culture types and tracking development; a data-driven framework for quantifying burst shapes using graph-based embeddings, demonstrating that intra-burst temporal structure carries rich phenotypic information beyond traditional summary statistics; and experimental manipulation of excitatory/inhibitory cellular ratios combined with modeling, showing that networks self-organize toward balanced dynamics by adjusting connection numbers rather than synaptic strengths. Together, these results illustrate how simplified models and data-driven descriptions can deepen our understanding of the dynamical principles governing collective activity in neuronal populations and the mechanisms through which networks maintain stable function despite diverse cellular compositions and developmental trajectories.