I am fascinated by the similarities between artificial neural networks and biological neural networks, and I am particularly interested in how machine learning algorithms and the brain are solving the complex task of vision. To gain insight into these matters, I am using deep neural networks to model the early visual cortex of monkeys and mice, to see in which cases these models work, and in which cases they do not. Using network dissection techniques and in silico experiments, my aim is to discover functional principles of artificial neural networks that are instructive for neuroscientific experiments, and that could lead to insights into the fundamentals of our cognitive system and its implementation. I am also passionate about open science, sharing of data and methods, and the movement of using AI to empower society.
Publications
Lab members are shown in this color. Preprints are shown in this color.
2021
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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
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Shahd Safarani, Arne Nix, Konstantin Willeke, Santiago A. Cadena, Kelli Restivo, George Denfield, Andreas S. Tolias, Fabian H. Sinz
Towards robust vision by multi-task learning on monkey visual cortex
NeurIPS (accepted)
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Shahd Safarani, Arne Nix, Konstantin Willeke, Santiago A. Cadena, Kelli Restivo, George Denfield, Andreas S. Tolias, Fabian H. Sinz
Towards robust vision by multi-task learning on monkey visual cortex
ICLR 2021 Workshop: How Can Findings About The Brain Improve AI Systems?
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Konstantin-Klemens Lurz, Mohammad Bashiri, Konstantin Friedrich Willeke, Akshay Kumar Jagadish, Eric Wang, Edgar Y Walker, Santiago Cadena, Taliah Muhammad, Eric Cobos, Andreas Tolias, Alexander Ecker, Fabian Sinz
Generalization in data-driven models of primary visual cortex
ICLR (spotlight)
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