Emmanouil Froudarakis

Dr. Froudarakis studied biology at National & Kapodistrian University of Athens and did his thesis on the mechanisms of learning and memory at the BSRC “Alexander Fleming”. He continued his studies in Neuroscience in Utrecht university in the Netherlands and Baylor college of Medicine in Houston, USA where he got his Ph.D. studying the neural representations of natural image statistics. He did hist post-doctoral studies and after which he worked as an Instructor of Neuroscience at Baylor College of Medicine between 2017 and 2019. From 2019 he is a group leader at IMBB-FORTH and his lab investigates how cortical circuits across different brain areas interact to form sensory representations that can guide behavior. His lab combines advanced imaging techniques for recording neural activity with high-throughput behavioral training and computational modeling to study how the activity of large neuronal populations across different cortical regions enables behaving animals to identify and isolate objects in different contexts. Specifically, Dr. Froudarakis group’s research focuses on understanding: 1. How neural representations of objects evolve across the cortical hierarchy and how circuits in these areas interact in order to optimize the computations necessary for different behaviors. 2. How multisensory input that carries information about both the identity and the context of the objects, affects the computations along the visual processing stream. In order to address these questions, they have successfully implemented an automatic, high-throughput, low-cost behavioral training system in which animals are trained to discriminate objects in their home cage. They have adapted this system to perform multimodal two-alterative forced choice training and currently they are working on expanding the capabilities to include match-to-sample tasks that are typically used for object discrimination in higher mammals. Additionally, they are developing a virtual environment in which animals can navigate based on the information from multiple sensory inputs, which will enable using the mouse as an animal model to study how context can affect the cortical representation of objects. Together with the behavioral training, both wide-field imaging methods and chronic recordings with miniature microscopes are used to map the re-organization of the visual circuits that occurs with visual experience. Finally, they have implemented computational methods to study the state-dependent dynamic interactions between all cortical visual areas of the mouse.