Julijana Gjorgjieva studied mathematics at Harvey Mudd College in California, USA and became interested in neuroscience at the University of Cambridge during a course in Computational Neuroscience while doing Part III of the Mathematical Tripos. After obtaining a PhD in Applied Mathematics at the University of Cambridge in 2011 with Stephen Eglen, she spent five years in the USA as a postdoctoral research fellow at Harvard University with Haim Sompolinsky and Markus Meister and Brandeis University with Eve Marder, supported by grants from the Swartz Foundation and the Burroughs-Wellcome Fund. In 2016, she set up an independent research group at the Max Planck Institute for Brain Research in Frankfurt, Germany and in 2022 became a Professor at the Technical University of Munich, Germany. She has received an ERC Starting Grant in 2018 for her research on theoretical models of neural circuit organization and computation during postnatal development and is further supported by a Human Frontiers Science Program to study plasticity and evolution of sensory systems under different environmental constraints. She is also a member of the steering committee of the Bernstein Network for Computational Neuroscience and has co-chaired the Bernstein Conference in Computational Neuroscience in 2020 and 2021.

Using theoretical and computational approaches, my research investigates the emergence of neural circuit organization, and the implications of this organization on circuit computations and function. Specifically, we combine two complementary approaches: First, we build bottom-up mechanistic models to study how non-random connectivity and activity emerge at the level of synaptic inputs on dendritic branches, micro-circuits and different brain regions. We apply these concepts to understand neural circuit development at very early ages right after an animal is born when patterned spontaneous activity guides cellular and synaptic refinements. We also investigate how neural circuit function is maintained after the onset of sensory experience, especially in the presence of perturbations. Second, we apply top-down normative frameworks to study how computation in the context of evolution arises from the goal of a neural system to maximize information transmission about a sensory stimulus subject to relevant biological constraints. One example is the generation of diverse responses in a population of sensory neurons (such as retinal ganglion cells or auditory nerve fibers) as a function of noise and stimulus statistics. Our work is supported by experimental collaborations based on different animal models, from rodent to fruit fly, allowing us to access individual neural circuit components and test modeling predictions.