Combining modeling techniques at various different scales can aid us to investigate how neuronal processes - from cellular mechanisms to network interactions - act in concert to form visual perceptions.
Dr. Simone Ebert
Simone Ebert studies how inhibition shapes early visual processing in the retina. She is a PostDoc in the Department of Data Science. Before joining the Hertie Institute, she studied how dynamic inhibition can shape temporal predictions of future visual inputs in the retina. Inhibitory interneurons constitute the widest class of cells in the retina, yet little is known about their functional roles. By combining biophysical models with machine learning techniques, her work aims to advance our understanding of how the inhibitory circuitry extracts complex features from a visual scene.
Combining modeling techniques at various different scales can aid us to investigate how neuronal processes - from cellular mechanisms to network interactions - act in concert to form visual perceptions.