Inception Loops

Inception Loops

We are using highly predictive deep learning models as in silico avatars of neural populations in visual cortex and analyse their nonlinear properties. Our goal is to find interpretable and experimentally verifiable computational principles of biological vision. We do this by synthesizing new stimuli for neuroscientific experiments in silico and verifying them in vivo with our collaboration partners. We call this cycle “Inception Loops”.


Lab members are shown in this color. Preprints are shown in this color.


Jiakun Fu, Konstantin F. Willeke, Pawel A. Pierzchlewicz, Taliah Muhammad, George H. Denfield, Fabian H. Sinz, Andreas S. Tolias Hetereogenous orientation tuning across sub-regions of receptive fields of V1 neurons in mice Cell Press Sneak Preview


Katrin Franke, Konstantin F. Willeke, Kayla Ponder, Mario Galdamez, Taliah Muhammad, Saumil Patel, Emmanouil Froudarakis, Jacob Reimer, Fabian H. Sinz, Andreas S. Tolias Behavioral state tunes mouse vision to ethological features through pupil dilation biorXiv


Edgar Y. Walker, Fabian H. Sinz, Erick Cobos, Taliah Muhammad, Emmanouil Froudarakis, Paul G. Fahey, Alexander S. Ecker, Jacob Reimer, Xaq Pitkow, Andreas S. Tolias Inception loops discover what excites neurons most using deep predictive models Nature Neuroscience