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Humplik, J., &; Tkadik, 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 
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Harpaz, R., Tkadik, 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 
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Prentice, J., Marre, O., Ioffe, M., Loback, A., Tkadik, G., &; Berry, M. (2017). Data from: Error-robust modes of the retinal population code. Dryad.https://doi.org/10.5061/dryad.1f1rc 
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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 
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Chalk, M. J., Masset, P., Gutkin, B., &; Denève, S. (2017). Supplementary appendix. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1005582.s001 
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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 
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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 
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arXiv: 1410.1242

Deny, S., Ferrari, U., Mace, E., Yger, P., Caplette, R., Picaud, S.,https://doi.org/10.1038/s41467-017-02159-y__Marre, O. (2017). Multiplexed computations in retinal ganglion cells of a single type. Nature Communications. Nature Publishing Group.https://doi.org/10.1038/s41467-017-02159-y 
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Levina (Martius), A., &; Priesemann, V. (2017). Subsampling scaling. Nature Communications. Nature Publishing Group.https://doi.org/10.1038/ncomms15140 
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Friedlander, T., Prizak, R., Barton, N. H., &; Tkadik, 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 
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De Martino, D. (2017). Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics. https://doi.org/10.1103/PhysRevE.95.062419__Physical Review E Statistical Nonlinear and Soft Matter Physics . American Institute of Physics.https://doi.org/10.1103/PhysRevE.95.062419 
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De Martino, D., Capuani, F., &; De Martino, A. (2017). Quantifying the entropic cost of cellular growth control. https://doi.org/10.1103/PhysRevE.96.010401__Physical Review E Statistical Nonlinear and Soft Matter Physics . American Institute of Physics.https://doi.org/10.1103/PhysRevE.96.010401 
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Zagórski, M. P., Tabata, Y., Brandenberg, N., Lutolf, M., Tkadik, G., Bollenbach, T.,https://doi.org/10.1126/science.aam5887__Kicheva, A. (2017). Decoding of position in the developing neural tube from antiparallel morphogen gradients. Science. American Association for the Advancement of Science.https://doi.org/10.1126/science.aam5887 
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PMID: 28663499

Colabrese, S., De Martino, D., Leuzzi, L., &; Marinari, E. (2017). Phase transitions in integer linear problems. https://doi.org/10.1088/1742-5468/aa85c3__Journal of Statistical Mechanics: Theory and Experiment. IOPscience.https://doi.org/10.1088/1742-5468/aa85c3 
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Savin, C., &; Tkadik, 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 
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De Martino, D. (2017). Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes. Physical Review E. American Physical Society.https://doi.org/10.1103/PhysRevE.96.060401 
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Lang, M., &; Sontag, E. (2017). Zeros of nonlinear systems with input invariances. Automatica. International Federation of Automatic Control.https://doi.org/10.1016/j.automatica.2017.03.030 
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Marre, O., Tkadik, G., Amodei, D., Schneidman, E., Bialek, W., &; Berry, M. (2017). Multi-electrode array recording from salamander retinal ganglion cells. Institute of Science and Technology Austria.https://doi.org/10.15479/AT:ISTA:61 
LibreCat: https://research-explorer.ista.ac.at/record/5562
DOI: 10.15479/AT:ISTA:61

Bergmiller, T., Andersson, A. M., Tomasek, K., Balleza, E., Kiviet, D., Hauschild, R.,https://doi.org/10.15479/AT:ISTA:53__Guet, C. C. (2017). Biased partitioning of the multi-drug efflux pump AcrAB-TolC underlies long-lived phenotypic heterogeneity. Institute of Science and Technology Austria.https://doi.org/10.15479/AT:ISTA:53 
LibreCat: https://research-explorer.ista.ac.at/record/5560
DOI: 10.15479/AT:ISTA:53