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Cepeda Humerez, S. A. (2019). Estimating information flow in single cells. Institute of Science and Technology Austria.https://doi.org/10.15479/AT:ISTA:6473 
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Prizak, R. (2019). Coevolution of transcription factors and their binding sites in sequence space. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:th6071 
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Wang, J. W. J. L., Lombardi, F., Zhang, X., Anaclet, C., &; Ivanov, P. C. (2019). Non-equilibrium critical dynamics of bursts in ;8 and ;4 rhythms as fundamental characteristic of sleep and wake micro-architecture. PLoS Computational Biology. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1007268 
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Granados, A., Pietsch, J., Cepeda Humerez, S. A., Farquhar, I., Tkadik, G., &; Swain, P. (2018). Distributed and dynamic intracellular organization of extracellular information. PNAS. National Academy of Sciences.https://doi.org/10.1073/pnas.1716659115 
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Bod’Ová, K., Mitchell, G., Harpaz, R., Schneidman, E., &; Tkadik, G. (2018). Probabilistic models of individual and collective animal behavior. PLoS One. Public Library of Science.https://doi.org/10.1371/journal.pone.0193049 
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Ferrari, U., Deny, S., Chalk, M. J., Tkadik, G., Marre, O., &; Mora, T. (2018). Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons. Physical Review E. American Physical Society.https://doi.org/10.1103/PhysRevE.98.042410 
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Botella Soler, V., Deny, S., Martius, G. S., Marre, O., &; Tkadik, G. (2018). Nonlinear decoding of a complex movie from the mammalian retina. PLoS Computational Biology. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1006057 
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Deny, S., Marre, O., Botella-Soler, V., Martius, G. S., &; Tkadik, G. (2018). Nonlinear decoding of a complex movie from the mammalian retina. Institute of Science and Technology Austria.https://doi.org/10.15479/AT:ISTA:98 
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De Martino, D., Mc, A. A., Bergmiller, T., Guet, C. C., &; Tkadik, G. (2018). Statistical mechanics for metabolic networks during steady state growth. Nature Communications. Springer Nature.https://doi.org/10.1038/s41467-018-05417-9 
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Igler, C., Lagator, M., Tkadik, G., Bollback, J. P., &; Guet, C. C. (2018). Evolutionary potential of transcription factors for gene regulatory rewiring. Nature Ecology and Evolution. Nature Publishing Group.https://doi.org/10.1038/s41559-018-0651-y 
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Igler, C., Lagator, M., Tkadik, G., Bollback, J. P., &; Guet, C. C. (2018). Data for the paper Evolutionary potential of transcription factors for gene regulatory rewiring. Institute of Science and Technology Austria.https://doi.org/10.15479/AT:ISTA:108 
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Chait, R. P., Ruess, J., Bergmiller, T., Tkadik, G., &; Guet, C. C. (2017). Shaping bacterial population behavior through computer interfaced control of individual cells. Nature Communications. Nature Publishing Group.https://doi.org/10.1038/s41467-017-01683-1 
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Der, R., &; Martius, G. S. (2017). Dynamical self consistency leads to behavioral development and emergent social interactions in robots. Presented at the ICDL EpiRob: International Conference on Development and Learning and Epigenetic Robotics , Cergy-Pontoise, France: IEEE.https://doi.org/10.1109/DEVLRN.2016.7846789 
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Der, R., &; Martius, G. S. (2017). Self organized behavior generation for musculoskeletal robots. Frontiers in Neurorobotics. Frontiers Research Foundation.https://doi.org/10.3389/fnbot.2017.00008 
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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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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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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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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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