Mapping visual representations in the brain using machine learning

Abstract: My talk will explore how we use machine learning as a powerful tool to enhance our understanding of neuronal representations within the visual cortex. By employing deep neural networks as digital twins for specific cortical areas in both mice and macaques, we facilitate comprehensive in-silico experiments that precede and inform targeted in-vivo verification. This method integrates large-scale neuronal recordings with advanced AI models to systematically analyze how visual neurons encode diverse stimuli attributes, ranging from isolated object features to more complex scenarios involving internal brain states and contextual information. This approach not only deepens our understanding of the functional organization within the visual cortex but also establishes a link between biological and artificial vision systems.

Speaker Bio: Katrin Franke completed her PhD at the International Max Planck Research School for Neural & Behavioral Neurosciences at Tuebingen University, Germany. Following her PhD, she served as an Early Career Group Leader at the Bernstein Center for Computational Neuroscience in Tuebingen. Since 2022, she has been a Research Faculty member in Andreas Tolias's lab, initially at Baylor College of Medicine in Houston and now at Stanford School of Medicine.
 

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