Francesca Mastrogiuseppe, Ph.D.

Postdoctoral Fellow, Champalimaud Centre for the Unknown
headshot of Francesca Mastrogiuseppe

Francesca Mastrogiuseppe is a postdoctoral fellow at the Champalimaud Centre for the Unknown in Lisbon, Portugal. She obtained a Ph.D. in theoretical neuroscience from École Normale Supérieure in Paris under the guidance of Srdjan Ostojic. She then joined the Gatsby Computational Neuroscience Unit at University College London, where she worked as a postdoc together with Peter Latham. During that time, she established novel mathematical tools to unravel the link between circuitry and computation in neural network models of behavioral tasks. In her current research, Mastrogiuseppe is advised by Christian Machens. She uses data analysis and statistical tools to study how interactions among brain areas within the cortical hierarchy mediate sensory processing and behavior.

Project: Mechanisms of learning and computations in cortical neural networks

The mammalian cortex consists of millions of neurons, densely and recurrently interconnected within and across areas. From the orchestrated activity within this large network, complex behavioral outputs arise. What are the mechanisms that govern the acquisition and execution of cortical computations? How are these mechanisms shaped by innate cortical structures, such as long-range anatomical projections and local excitatory/inhibitory circuitry? Recent transformations in neurotechnologies permit recording neural activity and tracing anatomical connectivity at the brain-wide scale, which offers unprecedented chances to shed light on these questions. The goal of our research will be to deliver theoretical tools for guiding the interpretation of these datasets and constraining hypotheses on the mechanisms that underlie learning and computations in the cortex. A central tool of our work will be neural network models, that we will build and analyze borrowing ideas from artificial intelligence, dynamical systems, and statistical physics. To interface models with large-scale datasets, we will develop novel statistical tools and perform data analysis in collaboration with experimental laboratories.

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