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Plos Computational Biology|March 5, 2021
Inferring phenomenological models of first passage processesCatalina Rivera, David Hofmann, Ilya NemenmanProceedings of the National Academy of Sciences of the United States of America|August 22, 2018
Chance, long tails, and inference in a non-Gaussian, Bayesian theory of vocal learning in songbirdsBaohua Zhou, David Hofmann, Itai Pinkoviezky, et al.Physical Biology|April 6, 2012
Gain control in molecular information processing: lessons from neuroscienceIlya NemenmanPhysical Review Letters|February 1, 2020
Universal Properties of Concentration Sensing in Large Ligand-Receptor NetworksVijay Singh, Ilya NemenmanPlos Computational Biology|May 8, 2020
Randomly connected networks generate emergent selectivity and predict decoding properties of large populations of neuronsAudrey Sederberg, Ilya NemenmanProceedings of the National Academy of Sciences of the United States of America|June 15, 2026
Random-with-constraints: Constructing minimal models for high-dimensional biologyIlya Nemenman, Pankaj MehtaPhysical Review. E|August 17, 2022
Statistical properties of large data sets with linear latent featuresPhilipp Fleig, Ilya NemenmanPlos Computational Biology|April 15, 2017
Simple biochemical networks allow accurate sensing of multiple ligands with a single receptorVijay Singh, Ilya NemenmanPhysical Review. E, Statistical, Nonlinear, and Soft Matter Physics|February 28, 2002
Occam factors and model independent Bayesian learning of continuous distributionsIlya Nemenman, William BialekPageof 13