Time: 4:00pm
Location: 46-3002, Singleton Auditorium (Third floor of MIT Building 46)

Talk Title: How neural circuits acquire structure and use it flexibly

Abstract: Neural circuits acquire structured connectivity during development and learning, yet must use this organization flexibly as sensory inputs, context and behavioral demands change. Understanding how these properties emerge is challenging because multiple plasticity mechanisms, cell types and synaptic dynamics interact across spatial and temporal scales. I combine theoretical analysis and mechanistic modeling with quantitative analysis of large-scale physiological and connectomic data to develop predictive frameworks that link synaptic plasticity, circuit structure and function across these scales. I will first discuss how patterned activity and plasticity generate structured organization from dendritic inputs to recurrent circuits. I will then show how inhibitory cell types and short-term synaptic dynamics shape circuit operating regimes and responses to contextual signals. Finally, I will discuss how the organization of synaptic inputs on individual dendritic branches gives rise to dendritic nonlinearities that enable flexible learning without interference between stored representations. Together, these results show how local biological mechanisms give rise to circuit-level computation and motivate the broader goal of understanding how neural circuits are built, modified by experience and used flexibly to guide behavior.

Bio: Julijana Gjorgjieva is Professor of Computational Neuroscience at the Technical University of Munich (TUM). She received her PhD in Applied Mathematics from the University of Cambridge and subsequently trained as a postdoctoral fellow at Harvard University with Haim Sompolinsky and Markus Meister and Brandeis University with Eve Marder. In 2016, she established an independent research group at the Max Planck Institute for Brain Research in Frankfurt and joined TUM, where she became a tenured professor in 2022. Her research uses theoretical and computational approaches to understand how neural circuits develop, learn, and adapt, with a particular focus on synaptic plasticity, circuit organization, dendritic computation, and feedback. Her work seeks to link mechanisms operating at the level of synapses and local circuits to the emergence of robust and flexible neural computations. She is a recipient of the Eric Kandel Young Neuroscientists Prize awarded by FENS and the Heinz Maier-Leibnitz Prize awarded by the German Research Foundation.