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185 Publications

2017 | Published | Journal Article | IST-REx-ID: 666 | OA
Mitosch, K., Rieckh, G., & Bollenbach, M. T. (2017). Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment. Cell Systems. Cell Press. https://doi.org/10.1016/j.cels.2017.03.001
[Published Version] View | Files available | DOI
 
2017 | Published | Journal Article | IST-REx-ID: 680 | OA
Chalk, M. J., Masset, P., Gutkin, B., & Denève, S. (2017). Sensory noise predicts divisive reshaping of receptive fields. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005582
[Published Version] View | Files available | DOI
 
2017 | Published | Journal Article | IST-REx-ID: 720 | OA
Humplik, J., & Tkačik, G. (2017). Probabilistic models for neural populations that naturally capture global coupling and criticality. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005763
[Published Version] View | Files available | DOI
 
2017 | Published | Journal Article | IST-REx-ID: 725 | OA
Harpaz, R., Tkačik, G., & Schneidman, E. (2017). Discrete modes of social information processing predict individual behavior of fish in a group. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1703817114
[Submitted Version] View | DOI | Download Submitted Version (ext.) | PubMed | Europe PMC
 
2017 | Published | Journal Article | IST-REx-ID: 730
Savin, C., & Tkačik, G. (2017). Maximum entropy models as a tool for building precise neural controls. Current Opinion in Neurobiology. Elsevier. https://doi.org/10.1016/j.conb.2017.08.001
View | DOI | WoS
 
2017 | Published | Journal Article | IST-REx-ID: 955 | OA
Friedlander, T., Prizak, R., Barton, N. H., & Tkačik, G. (2017). Evolution of new regulatory functions on biophysically realistic fitness landscapes. Nature Communications. Nature Publishing Group. https://doi.org/10.1038/s41467-017-00238-8
[Published Version] View | Files available | DOI | WoS
 
2017 | Published | Journal Article | IST-REx-ID: 2016 | OA
Martin Del Campo Sanchez, A., Cepeda Humerez, S. A., & Uhler, C. (2017). Exact goodness-of-fit testing for the Ising model. Scandinavian Journal of Statistics. Wiley-Blackwell. https://doi.org/10.1111/sjos.12251
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
2017 | Published | Journal Article | IST-REx-ID: 823 | OA
Colabrese, S., De Martino, D., Leuzzi, L., & Marinari, E. (2017). Phase transitions in integer linear problems. Journal of Statistical Mechanics: Theory and Experiment. IOP Publishing. https://doi.org/10.1088/1742-5468/aa85c3
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
2017 | Published | Journal Article | IST-REx-ID: 735
Barone, V., Lang, M., Krens, G., Pradhan, S., Shamipour, S., Sako, K., … Heisenberg, C.-P. J. (2017). An effective feedback loop between cell-cell contact duration and morphogen signaling determines cell fate. Developmental Cell. Cell Press. https://doi.org/10.1016/j.devcel.2017.09.014
View | Files available | DOI | WoS
 
2016 | Published | Conference Paper | IST-REx-ID: 948
Monk, T., Savin, C., & Lücke, J. (2016). Neurons equipped with intrinsic plasticity learn stimulus intensity statistics (Vol. 29, pp. 4285–4293). Presented at the NIPS: Neural Information Processing Systems, Barcelona, Spaine: Neural Information Processing Systems.
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2016 | Research Data Reference | IST-REx-ID: 9869
Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Error bound on an estimator of position. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628.s001
[Published Version] View | Files available | DOI
 
2016 | Research Data Reference | IST-REx-ID: 9870
Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Computation of positional information in an Ising model. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628.s002
[Published Version] View | Files available | DOI
 
2016 | Research Data Reference | IST-REx-ID: 9871
Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Computation of positional information in a discrete morphogen field. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628.s003
[Published Version] View | Files available | DOI
 
2016 | Published | Conference Paper | IST-REx-ID: 1320
Lang, M., & Sontag, E. (2016). Scale-invariant systems realize nonlinear differential operators (Vol. 2016–July). Presented at the ACC: American Control Conference, Boston, MA, USA: IEEE. https://doi.org/10.1109/ACC.2016.7526722
[Preprint] View | Files available | DOI
 
2016 | Published | Journal Article | IST-REx-ID: 1332 | OA
Chait, R. P., Palmer, A., Yelin, I., & Kishony, R. (2016). Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments. Nature Communications. Nature Publishing Group. https://doi.org/10.1038/ncomms10333
[Published Version] View | Files available | DOI
 
2016 | Published | Journal Article | IST-REx-ID: 1342 | OA
Baym, M., Lieberman, T., Kelsic, E., Chait, R. P., Gross, R., Yelin, I., & Kishony, R. (2016). Spatiotemporal microbial evolution on antibiotic landscapes. Science. American Association for the Advancement of Science. https://doi.org/10.1126/science.aag0822
[Preprint] View | DOI | Download Preprint (ext.)
 
2016 | Published | Journal Article | IST-REx-ID: 1420 | OA
Bodova, K., Tkačik, G., & Barton, N. H. (2016). A general approximation for the dynamics of quantitative traits. Genetics. Genetics Society of America. https://doi.org/10.1534/genetics.115.184127
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2016 | Published | Conference Paper | IST-REx-ID: 1082 | OA
Chalk, M. J., Marre, O., & Tkačik, G. (2016). Relevant sparse codes with variational information bottleneck (Vol. 29, pp. 1965–1973). Presented at the NIPS: Neural Information Processing Systems, Barcelona, Spain: Neural Information Processing Systems.
[Preprint] View | Files available | Download Preprint (ext.)
 
2016 | Published | Conference Paper | IST-REx-ID: 1105
Savin, C., & Tkačik, G. (2016). Estimating nonlinear neural response functions using GP priors and Kronecker methods (Vol. 29, pp. 3610–3618). Presented at the NIPS: Neural Information Processing Systems, Barcelona; Spain: Neural Information Processing Systems.
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2016 | Published | Journal Article | IST-REx-ID: 1148
Schilling, C., Bogomolov, S., Henzinger, T. A., Podelski, A., & Ruess, J. (2016). Adaptive moment closure for parameter inference of biochemical reaction networks. Biosystems. Elsevier. https://doi.org/10.1016/j.biosystems.2016.07.005
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