Suhas Shrinivasan

Graduate Student

normative models
Suhas Shrinivasan

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.


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Jiakun Fu, Suhas Shrinivasan, Luca Baroni, Zhuokun Ding, Paul G. Fahey, PaweĊ‚ Pierzchlewicz, Kayla Ponder, Rachel Froebe, Lydia Ntanavara, Taliah Muhammad, Konstantin F Willeke, Eric Wang, Zhiwei Ding, Dat T. Tran, Stelios Papadopoulos, Saumil Patel, Jacob Reimer, Alexander S. Ecker, Xaq Pitkow, Jan Antolik, Fabian H. Sinz, Ralf M. Haefner, Andreas S. Tolias, Katrin Franke Pattern completion and disruption characterize contextual modulation in the visual cortex bioRxiv


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
conference paper · normative_models ·
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: *


Arne Nix, Suhas Shrinivasan, Edgar Y. Walker, Fabian H. Sinz Can Functional Transfer Methods Capture Simple Inductive Biases? AISTATS 2022