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Ilya Nemenman

Showing results (1-10 of 74) with videos related to

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Neural Computation|July 5, 2005
Fluctuation-dissipation theorem and models of learningIlya Nemenman
Physical Biology|April 6, 2012
Gain control in molecular information processing: lessons from neuroscienceIlya Nemenman
Physical Review Letters|February 1, 2020
Universal Properties of Concentration Sensing in Large Ligand-Receptor NetworksVijay Singh, Ilya Nemenman
Plos Computational Biology|May 8, 2020
Randomly connected networks generate emergent selectivity and predict decoding properties of large populations of neuronsAudrey Sederberg, Ilya Nemenman
Proceedings 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 Mehta
Physical Review. E|August 17, 2022
Statistical properties of large data sets with linear latent featuresPhilipp Fleig, Ilya Nemenman
Plos Computational Biology|April 15, 2017
Simple biochemical networks allow accurate sensing of multiple ligands with a single receptorVijay Singh, Ilya Nemenman
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|February 28, 2002
Occam factors and model independent Bayesian learning of continuous distributionsIlya Nemenman, William Bialek
Physical Review Letters|November 26, 2019
Physical Limit to Concentration Sensing in a Changing EnvironmentThierry Mora, Ilya Nemenman
Plos One|May 8, 2013
Genotype to phenotype mapping and the fitness landscape of the E. coli lac promoterJakub Otwinowski, Ilya Nemenman
Pageof 8

Showing results (1-10 of 74) with videos related to

Sort By:
Pageof 8
Neural Computation|July 5, 2005
Fluctuation-dissipation theorem and models of learningIlya Nemenman
Physical Biology|April 6, 2012
Gain control in molecular information processing: lessons from neuroscienceIlya Nemenman
Physical Review Letters|February 1, 2020
Universal Properties of Concentration Sensing in Large Ligand-Receptor NetworksVijay Singh, Ilya Nemenman
Plos Computational Biology|May 8, 2020
Randomly connected networks generate emergent selectivity and predict decoding properties of large populations of neuronsAudrey Sederberg, Ilya Nemenman
Proceedings 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 Mehta
Physical Review. E|August 17, 2022
Statistical properties of large data sets with linear latent featuresPhilipp Fleig, Ilya Nemenman
Plos Computational Biology|April 15, 2017
Simple biochemical networks allow accurate sensing of multiple ligands with a single receptorVijay Singh, Ilya Nemenman
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|February 28, 2002
Occam factors and model independent Bayesian learning of continuous distributionsIlya Nemenman, William Bialek
Physical Review Letters|November 26, 2019
Physical Limit to Concentration Sensing in a Changing EnvironmentThierry Mora, Ilya Nemenman
Plos One|May 8, 2013
Genotype to phenotype mapping and the fitness landscape of the E. coli lac promoterJakub Otwinowski, Ilya Nemenman
Pageof 8