My primary interest lies in uncovering fundamental algorithms and principles of intelligence and intelligent behavior - be it biological or artificial. My current work revolves around theories of perception in the brain - specifically that the brain performs perception via probabilistic inference. I am interested in modeling and evaluating these theories on large scale neurophysiological recordings from the visual cortex using modern probabilistic machine learning methods.
Publications
Lab members are shown in this color.
2023
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Suhas Shrinivasan, Konstantin-Klemens Lurz, Kelli Restivo, George Denfield, Andreas S. Tolias, Edgar Y. Walker1, Fabian H. Sinz1
Taking the neural sampling code very seriously: A data-driven approach for assessing generative models of the visual system
NeurIPS
, equal contribution: 1
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Jiakun Fu, Suhas Shrinivasan, Kayla Ponder, Taliah Muhammad, Zhuokun Ding, Eric Wang, Zhiwei Ding, Dat T. Tran, Paul G. Fahey, Stelios Papadopoulos, Saumil Patel, Jacob Reimer, Alexander S. Ecker, Xaq Pitkow, Ralf M. Haefner, Fabian H. Sinz, Katrin Franke, Andreas S. Tolias
Pattern completion and disruption characterize contextual modulation in mouse visual cortex
bioRxiv
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Suhas Shrinivasan, Andreas S. Tolias, Edgar Y. Walker*, Fabian H. Sinz*
Fitting normative neural sampling hypothesis models to neuronal response data
COSYNE 2023
, equal contribution: *
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2022
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Arne Nix, Suhas Shrinivasan, Edgar Y. Walker, Fabian H. Sinz
Can Functional Transfer Methods Capture Simple Inductive Biases?
AISTATS 2022
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