Who we are > FENS-Kavli Scholars > EMMANOUIL FROUDARAKIS (2023)

Emmanouil Froudarakis

Country of origin: Greece


Emmanouil Froudarakis obtained his B.Sc. Degree in Biology at the National & Kapodistrian University of Athens. 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. in 2015 studying the neural representations of natural image statistics. He did his post-doctoral studies and worked as an Instructor of Neuroscience at Baylor College of Medicine. From 2019 he is a group leader at IMBB-FORTH and his lab investigates how cortical circuits across different brain areas interact to form representations that can guide behavior. In 2022 E. Froudarakis was elected Assistant Professor of Neurophysiology at the Medical School, University of Crete. In 2022, he was awarded a Starting Grant from the European Research Council (ERC) to study the neural mechanisms for object recognition.


2022 - Present
University of Crete, Greece
2019 - 2022
IMBB-FORTH Heraklion, Greece
2017 - 2019
Baylor College of Medicine Houston TX, USA


Ph.D: Baylor College of Medicine Houston TX, USA


Our lab investigates how cortical circuits interact to form transformation-invariant object representations that can guide behaviour. Natural environment contains large number of objects with overlapping sensory input, and our brain is capable of using information from different sensory modalities to extract their identities with ease. To accomplish this computationally challenging task, a large part of our brain is dedicated to processing all the available information from the environment and extracting and isolating object identities. Yet, despite extensive research in the last few decades, we are still far from having a complete understanding of how the brain creates untangled object representations. If we understood how cortex achieves this extraordinary ability at the algorithmic level, this would represent a significant advance in our understanding of brain computation in general. To address this question, we combine advanced imaging techniques for recording neural activity with high-throughput behavioural training and computational modelling to study how the activity of large neuronal populations across different cortical regions enables behaving animals to identify and isolate objects in different contexts.