De Martino, D., &; Masoero, D. (2016). Asymptotic analysis of noisy fitness maximization, applied to metabolism &;amp; growth. https://doi.org/10.1088/1742-5468/aa4e8f__Journal of Statistical Mechanics: Theory and Experiment. IOPscience.https://doi.org/10.1088/1742-5468/aa4e8f 
LibreCat: https://research-explorer.ista.ac.at/record/1188
DOI: 10.1088/1742-5468/aa4e8f

Hu, F., Rishishwar, L., Sivadas, A., Mitchell, G., King, J., Murphy, T.,https://doi.org/10.1128/JCM.01511-16__Wang, X. (2016). Comparative genomic analysis of Haemophilus haemolyticus and nontypeable Haemophilus influenzae and a new testing scheme for their discrimination. Journal of Clinical Microbiology. American Society for Microbiology.https://doi.org/10.1128/JCM.01511-16 
LibreCat: https://research-explorer.ista.ac.at/record/1203
DOI: 10.1128/JCM.01511-16

Martius, G. S., Hostettler, R., Knoll, A., &; Der, R. (2016). Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm (Vol. 2016–November). Presented at the IEEE RSJ International Conference on Intelligent Robots and Systems IROS , Daejeon, Korea: IEEE.https://doi.org/10.1109/IROS.2016.7759138 
LibreCat: https://research-explorer.ista.ac.at/record/1214
DOI: 10.1109/IROS.2016.7759138

Miki, G., Stoll, A., Bevirt, J., Grah, R., &; Moore, M. (2016). Fuselage boundary layer ingestion propulsion applied to a thin haul commuter aircraft for optimal efficiency (pp. 1–19). Presented at the AIAA: Aviation Technology, Integration, and Operations Conference, Washington, D.C., USA: AIAA.https://doi.org/10.2514/6.2016-3764 
LibreCat: https://research-explorer.ista.ac.at/record/1220
DOI: 10.2514/6.2016-3764

Sokolowski, T. R., Walczak, A., Bialek, W., &; Tkadik, G. (2016). Extending the dynamic range of transcription factor action by translational regulation. Physical Review E Statistical Nonlinear and Soft Matter Physics. American Institute of Physics.https://doi.org/10.1103/PhysRevE.93.022404 
LibreCat: https://research-explorer.ista.ac.at/record/1242
DOI: 10.1103/PhysRevE.93.022404

Recouvreux, P., Sokolowski, T. R., Grammoustianou, A., Tenwolde, P., &; Dogterom, M. (2016). Chimera proteins with affinity for membranes and microtubule tips polarize in the membrane of fission yeast cells. PNAS. National Academy of Sciences.https://doi.org/10.1073/pnas.1419248113 
LibreCat: https://research-explorer.ista.ac.at/record/1244
DOI: 10.1073/pnas.1419248113

Tkadik, G., &; Bialek, W. (2016). Information processing in living systems. Annual Review of Condensed Matter Physics. Annual Reviews.https://doi.org/10.1146/annurev-conmatphys-031214-014803 
LibreCat: https://research-explorer.ista.ac.at/record/1248
DOI: 10.1146/annurev-conmatphys-031214-014803

De Martino, D. (2016). The dual of the space of interactions in neural network models. International Journal of Modern Physics C. World Scientific Publishing.https://doi.org/10.1142/S0129183116500674 
LibreCat: https://research-explorer.ista.ac.at/record/1260
DOI: 10.1142/S0129183116500674
arXiv: 1505.02963

Chalk, M. J., Gutkin, B., &; Denève, S. (2016). Neural oscillations as a signature of efficient coding in the presence of synaptic delays. ELife. eLife Sciences Publications.https://doi.org/10.7554/eLife.13824 
LibreCat: https://research-explorer.ista.ac.at/record/1266
DOI: 10.7554/eLife.13824

Stone, L., Baym, M., Lieberman, T., Chait, R. P., Clardy, J., &; Kishony, R. (2016). Compounds that select against the tetracycline-resistance efflux pump. Nature Chemical Biology. Nature Publishing Group.https://doi.org/10.1038/nchembio.2176 
LibreCat: https://research-explorer.ista.ac.at/record/1290
DOI: 10.1038/nchembio.2176

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 
LibreCat: https://research-explorer.ista.ac.at/record/1320
DOI: 10.1109/ACC.2016.7526722

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 
LibreCat: https://research-explorer.ista.ac.at/record/1332
DOI: 10.1038/ncomms10333

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 
LibreCat: https://research-explorer.ista.ac.at/record/1342
DOI: 10.1126/science.aag0822

De Martino, D., Capuani, F., &; De Martino, A. (2016). Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli. Physical Biology. IOP Publishing Ltd.https://doi.org/10.1088/1478-3975/13/3/036005 
LibreCat: https://research-explorer.ista.ac.at/record/1394
DOI: 10.1088/1478-3975/13/3/036005

Bodova, K., Tkadik, 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 
LibreCat: https://research-explorer.ista.ac.at/record/1420
DOI: 10.1534/genetics.115.184127
arXiv: 1510.08344

De Martino, D. (2016). Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis. Physical Biology. IOP Publishing Ltd.https://doi.org/10.1088/1478-3975/13/1/016003 
LibreCat: https://research-explorer.ista.ac.at/record/1485
DOI: 10.1088/1478-3975/13/1/016003

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 
LibreCat: https://research-explorer.ista.ac.at/record/1148
DOI: 10.1016/j.biosystems.2016.07.005

Martius, G. S., Hostettler, R., Knoll, A., &; Der, R. (2016). Self-organized control of an tendon driven arm by differential extrinsic plasticity. In Proceedings of the Artificial Life Conference 2016 (Vol. 28, pp. 142–143). Cancun, Mexico: MIT Press.https://doi.org/10.7551/978-0-262-33936-0-ch029 
LibreCat: https://research-explorer.ista.ac.at/record/8094
DOI: 10.7551/978-0-262-33936-0-ch029

Prentice, J., Marre, O., Ioffe, M., Loback, A., Tkadik, G., &; Berry, M. (2016). Error-robust modes of the retinal population code. PLoS Computational Biology. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1005148 
LibreCat: https://research-explorer.ista.ac.at/record/1197
DOI: 10.1371/journal.pcbi.1005148

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.
LibreCat: https://research-explorer.ista.ac.at/record/948

Hillenbrand, P., Gerland, U., &; Tkadik, G. (2016). Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information. PLoS One. Public Library of Science.https://doi.org/10.1371/journal.pone.0163628 
LibreCat: https://research-explorer.ista.ac.at/record/1270
DOI: 10.1371/journal.pone.0163628

Hillenbrand, P., Gerland, U., &; Tkadik, G. (2016). Computation of positional information in an Ising model. Public Library of Science.https://doi.org/10.1371/journal.pone.0163628.s002 
LibreCat: https://research-explorer.ista.ac.at/record/9870
DOI: 10.1371/journal.pone.0163628.s002

Hillenbrand, P., Gerland, U., &; Tkadik, G. (2016). Error bound on an estimator of position. Public Library of Science.https://doi.org/10.1371/journal.pone.0163628.s001 
LibreCat: https://research-explorer.ista.ac.at/record/9869
DOI: 10.1371/journal.pone.0163628.s001

Hillenbrand, P., Gerland, U., &; Tkadik, G. (2016). Computation of positional information in a discrete morphogen field. Public Library of Science.https://doi.org/10.1371/journal.pone.0163628.s003 
LibreCat: https://research-explorer.ista.ac.at/record/9871
DOI: 10.1371/journal.pone.0163628.s003

Rieckh, G. (2016). Studying the complexities of transcriptional regulation. Institute of Science and Technology Austria.
LibreCat: https://research-explorer.ista.ac.at/record/1128

Friedlander, T., Prizak, R., Guet, C. C., Barton, N. H., &; Tkadik, G. (2016). Intrinsic limits to gene regulation by global crosstalk. Nature Communications. Nature Publishing Group.https://doi.org/10.1038/ncomms12307 
LibreCat: https://research-explorer.ista.ac.at/record/1358
DOI: 10.1038/ncomms12307

Parise, F., Lygeros, J., &; Ruess, J. (2015). Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. Frontiers in Environmental Science. Frontiers.https://doi.org/10.3389/fenvs.2015.00042 
LibreCat: https://research-explorer.ista.ac.at/record/10794
DOI: 10.3389/fenvs.2015.00042

Ruess, J. (2015). Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. Journal of Chemical Physics. American Institute of Physics.https://doi.org/10.1063/1.4937937 
LibreCat: https://research-explorer.ista.ac.at/record/1539
DOI: 10.1063/1.4937937

Ruess, J., Parise, F., Milias Argeitis, A., Khammash, M., &; Lygeros, J. (2015). Iterative experiment design guides the characterization of a light-inducible gene expression circuit. PNAS. National Academy of Sciences.https://doi.org/10.1073/pnas.1423947112 
LibreCat: https://research-explorer.ista.ac.at/record/1538
DOI: 10.1073/pnas.1423947112
PMID: 26085136

Gilson, M., Savin, C., &; Zenke, F. (2015). Editorial: Emergent neural computation from the interaction of different forms of plasticity. Frontiers in Computational Neuroscience. Frontiers Research Foundation.https://doi.org/10.3389/fncom.2015.00145 
LibreCat: https://research-explorer.ista.ac.at/record/1564
DOI: 10.3389/fncom.2015.00145

Der, R., &; Martius, G. S. (2015). Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS. National Academy of Sciences.https://doi.org/10.1073/pnas.1508400112 
LibreCat: https://research-explorer.ista.ac.at/record/1570
DOI: 10.1073/pnas.1508400112
PMID: 26504200

Bogomolov, S., Henzinger, T. A., Podelski, A., Ruess, J., &; Schilling, C. (2015). Adaptive moment closure for parameter inference of biochemical reaction networks. Presented at the CMSB: Computational Methods in Systems Biology, Nantes, France: Springer.https://doi.org/10.1007/978-3-319-23401-4_8 
LibreCat: https://research-explorer.ista.ac.at/record/1658
DOI: 10.1007/978-3-319-23401-4_8

Marre, O., Botella Soler, V., Simmons, K., Mora, T., Tkadik, G., &; Berry, M. (2015). High accuracy decoding of dynamical motion from a large retinal population. PLoS Computational Biology. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1004304 
LibreCat: https://research-explorer.ista.ac.at/record/1697
DOI: 10.1371/journal.pcbi.1004304

Tkadik, G., Mora, T., Marre, O., Amodei, D., Palmer, S., Berry Ii, M., &; Bialek, W. (2015). Thermodynamics and signatures of criticality in a network of neurons. PNAS. National Academy of Sciences.https://doi.org/10.1073/pnas.1514188112 
LibreCat: https://research-explorer.ista.ac.at/record/1701
DOI: 10.1073/pnas.1514188112
PMID: 26330611

Ruess, J., &; Lygeros, J. (2015). Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation. ACM.https://doi.org/10.1145/2688906 
LibreCat: https://research-explorer.ista.ac.at/record/1861
DOI: 10.1145/2688906

Tkadik, G., Dubuis, J., Petkova, M., &; Gregor, T. (2015). Positional information, positional error, and readout precision in morphogenesis: A mathematical framework. Genetics. Genetics Society of America.https://doi.org/10.1534/genetics.114.171850 
LibreCat: https://research-explorer.ista.ac.at/record/1885
DOI: 10.1534/genetics.114.171850

Sokolowski, T. R., &; Tkadik, G. (2015). Optimizing information flow in small genetic networks. IV. Spatial coupling. Physical Review E Statistical Nonlinear and Soft Matter Physics. American Institute of Physics.https://doi.org/10.1103/PhysRevE.91.062710 
LibreCat: https://research-explorer.ista.ac.at/record/1940
DOI: 10.1103/PhysRevE.91.062710

Friedlander, T., Mayo, A. E., Tlusty, T., &; Alon, U. (2015). Supporting information text. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1004055.s001 
LibreCat: https://research-explorer.ista.ac.at/record/9718
DOI: 10.1371/journal.pcbi.1004055.s001

Friedlander, T., Mayo, A., Tlusty, T., &; Alon, U. (2015). Evolution of bow-tie architectures in biology. PLoS Computational Biology. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1004055 
LibreCat: https://research-explorer.ista.ac.at/record/1827
DOI: 10.1371/journal.pcbi.1004055

Friedlander, T., Mayo, A. E., Tlusty, T., &; Alon, U. (2015). Evolutionary simulation code. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1004055.s002 
LibreCat: https://research-explorer.ista.ac.at/record/9773
DOI: 10.1371/journal.pcbi.1004055.s002

Tugrul, M., Paixao, T., Barton, N. H., &; Tkadik, G. (2015). Other fitness models for comparison &; for interacting TFBSs. Public Library of Science.https://doi.org/10.1371/journal.pgen.1005639.s001 
LibreCat: https://research-explorer.ista.ac.at/record/9712
DOI: 10.1371/journal.pgen.1005639.s001

Tugrul, M., Paixao, T., Barton, N. H., &; Tkadik, G. (2015). Dynamics of transcription factor binding site evolution. PLoS Genetics. Public Library of Science.https://doi.org/10.1371/journal.pgen.1005639 
LibreCat: https://research-explorer.ista.ac.at/record/1666
DOI: 10.1371/journal.pgen.1005639

Cepeda Humerez, S. A., Rieckh, G., &; Tkadik, G. (2015). Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation. Physical Review Letters. American Physical Society.https://doi.org/10.1103/PhysRevLett.115.248101 
LibreCat: https://research-explorer.ista.ac.at/record/1576
DOI: 10.1103/PhysRevLett.115.248101

Martius, G. S., &; Olbrich, E. (2015). Quantifying emergent behavior of autonomous robots. Entropy. MDPI.https://doi.org/10.3390/e17107266 
LibreCat: https://research-explorer.ista.ac.at/record/1655
DOI: 10.3390/e17107266

Savin, C., &; Denève, S. (2014). Spatio-temporal representations of uncertainty in spiking neural networks (Vol. 3, pp. 2024–2032). Presented at the NIPS: Neural Information Processing Systems, Montreal, Canada: Neural Information Processing Systems.
LibreCat: https://research-explorer.ista.ac.at/record/1708

Hermundstad, A., Briguglio, J., Conte, M., Victor, J., Balasubramanian, V., &; Tkadik, G. (2014). Variance predicts salience in central sensory processing. ELife. eLife Sciences Publications.https://doi.org/10.7554/eLife.03722 
LibreCat: https://research-explorer.ista.ac.at/record/1886
DOI: 10.7554/eLife.03722

Kollár, R., Bodova, K., Nosek, J., &; Tomáska, d. (2014). Mathematical model of alternative mechanism of telomere length maintenance. Physical Review E Statistical Nonlinear and Soft Matter Physics. American Institute of Physics.https://doi.org/10.1103/PhysRevE.89.032701 
LibreCat: https://research-explorer.ista.ac.at/record/1896
DOI: 10.1103/PhysRevE.89.032701

Ezard, T., Prizak, R., &; Hoyle, R. (2014). The fitness costs of adaptation via phenotypic plasticity and maternal effects. Functional Ecology. Wiley-Blackwell.https://doi.org/10.1111/1365-2435.12207 
LibreCat: https://research-explorer.ista.ac.at/record/1909
DOI: 10.1111/1365-2435.12207

Humplik, J., Hill, A., &; Nowak, M. (2014). Evolutionary dynamics of infectious diseases in finite populations. Journal of Theoretical Biology. Elsevier.https://doi.org/10.1016/j.jtbi.2014.06.039 
LibreCat: https://research-explorer.ista.ac.at/record/1928
DOI: 10.1016/j.jtbi.2014.06.039

Savin, C., &; Triesch, J. (2014). Emergence of task-dependent representations in working memory circuits. Frontiers in Computational Neuroscience. Frontiers Research Foundation.https://doi.org/10.3389/fncom.2014.00057 
LibreCat: https://research-explorer.ista.ac.at/record/1931
DOI: 10.3389/fncom.2014.00057

Bodova, K., Paydarfar, D., &; Forger, D. (2014). Characterizing spiking in noisy type II neurons. https://doi.org/10.1016/j.jtbi.2014.09.041__Journal of Theoretical Biology. Academic Press.https://doi.org/10.1016/j.jtbi.2014.09.041 
LibreCat: https://research-explorer.ista.ac.at/record/2028
DOI: 10.1016/j.jtbi.2014.09.041

Botella Soler, V., &; Glendinning, P. (2014). Hierarchy and polysynchrony in an adaptive network . Physical Review E Statistical Nonlinear and Soft Matter Physics. American Institute of Physics.https://doi.org/10.1103/PhysRevE.89.062809 
LibreCat: https://research-explorer.ista.ac.at/record/2183
DOI: 10.1103/PhysRevE.89.062809

Rieckh, G., &; Tkadik, G. (2014). Noise and information transmission in promoters with multiple internal states. Biophysical Journal. Biophysical Society.https://doi.org/10.1016/j.bpj.2014.01.014 
LibreCat: https://research-explorer.ista.ac.at/record/2231
DOI: 10.1016/j.bpj.2014.01.014
PMID: 24606943

Tkadik, G., Ghosh, A., Schneidman, E., &; Segev, R. (2014). Adaptation to changes in higher-order stimulus statistics in the salamander retina. PLoS One. Public Library of Science.https://doi.org/10.1371/journal.pone.0085841 
LibreCat: https://research-explorer.ista.ac.at/record/3263
DOI: 10.1371/journal.pone.0085841

Prizak, R., Ezard, T., &; Hoyle, R. (2014). Fitness consequences of maternal and grandmaternal effects. Ecology and Evolution. Wiley-Blackwell.https://doi.org/10.1002/ece3.1150 
LibreCat: https://research-explorer.ista.ac.at/record/537
DOI: 10.1002/ece3.1150

Simmons, K., Prentice, J., Tkadik, G., Homann, J., Yee, H., Palmer, S.,https://doi.org/10.5061/dryad.246qg__Balasubramanian, V. (2014). Data from: Transformation of stimulus correlations by the retina. Dryad.https://doi.org/10.5061/dryad.246qg 
LibreCat: https://research-explorer.ista.ac.at/record/9752
DOI: 10.5061/dryad.246qg

Tkadik, G., Marre, O., Amodei, D., Schneidman, E., Bialek, W., &; Berry, M. (2014). Searching for collective behavior in a large network of sensory neurons. PLoS Computational Biology. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1003408 
LibreCat: https://research-explorer.ista.ac.at/record/2257
DOI: 10.1371/journal.pcbi.1003408

Valderrama, M., Botella Soler, V., &; Le Van Quyen, M. (2013). Neuronal oscillations scale up and scale down the brain dynamics . In M. Meyer &; Z. Pesenson (Eds.), Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain. Wiley-VCH.https://doi.org/10.1002/9783527671632.ch08 
LibreCat: https://research-explorer.ista.ac.at/record/2413
DOI: 10.1002/9783527671632.ch08

Rajan, K., Marre, O., &; Tkadik, G. (2013). Learning quadratic receptive fields from neural responses to natural stimuli. Neural Computation. MIT Press .https://doi.org/10.1162/NECO_a_00463 
LibreCat: https://research-explorer.ista.ac.at/record/2818
DOI: 10.1162/NECO_a_00463
arXiv: 1209.0121

Tkadik, G., Marre, O., Mora, T., Amodei, D., Berry, M., &; Bialek, W. (2013). The simplest maximum entropy model for collective behavior in a neural network. Journal of Statistical Mechanics Theory and Experiment. IOP Publishing Ltd.https://doi.org/10.1088/1742-5468/2013/03/P03011 
LibreCat: https://research-explorer.ista.ac.at/record/2850
DOI: 10.1088/1742-5468/2013/03/P03011
arXiv: 1207.6319

Berry, M., Tkadik, G., Dubuis, J., Marre, O., &; Da Silveira, R. (2013). A simple method for estimating the entropy of neural activity. Journal of Statistical Mechanics Theory and Experiment. IOP Publishing Ltd.https://doi.org/10.1088/1742-5468/2013/03/P03015 
LibreCat: https://research-explorer.ista.ac.at/record/2851
DOI: 10.1088/1742-5468/2013/03/P03015

Granot Atedgi, E., Tkadik, G., Segev, R., &; Schneidman, E. (2013). Stimulus-dependent maximum entropy models of neural population codes. PLoS Computational Biology. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1002922 
LibreCat: https://research-explorer.ista.ac.at/record/2863
DOI: 10.1371/journal.pcbi.1002922

Botella Soler, V., Oteo, J., Ros, J., &; Glendinning, P. (2013). Lyapunov exponent and topological entropy plateaus in piecewise linear maps. Journal of Physics A: Mathematical and Theoretical. IOP Publishing Ltd.https://doi.org/10.1088/1751-8113/46/12/125101 
LibreCat: https://research-explorer.ista.ac.at/record/2861
DOI: 10.1088/1751-8113/46/12/125101

Tkadik, G., Granot Atedgi, E., Segev, R., &; Schneidman, E. (2013). Retinal metric: a stimulus distance measure derived from population neural responses. Physical Review Letters. American Physical Society.https://doi.org/10.1103/PhysRevLett.110.058104 
LibreCat: https://research-explorer.ista.ac.at/record/2913
DOI: 10.1103/PhysRevLett.110.058104

Dubuis, J., Tkadik, G., Wieschaus, E., Gregor, T., &; Bialek, W. (2013). Positional information, in bits. PNAS. National Academy of Sciences.https://doi.org/10.1073/pnas.1315642110 
LibreCat: https://research-explorer.ista.ac.at/record/3261
DOI: 10.1073/pnas.1315642110
PMID: 24089448

Wakamoto, Y., Dhar, N., Chait, R. P., Schneider, K., Signorino Gelo, F., Leibler, S., &; Mckinney, J. (2013). Dynamic persistence of antibiotic-stressed mycobacteria. Science. American Association for the Advancement of Science.https://doi.org/10.1126/science.1229858 
LibreCat: https://research-explorer.ista.ac.at/record/499
DOI: 10.1126/science.1229858

Simmons, K., Prentice, J., Tkadik, G., Homann, J., Yee, H., Palmer, S.,https://doi.org/10.1371/journal.pcbi.1003344__Balasubramanian, V. (2013). Transformation of stimulus correlations by the retina. PLoS Computational Biology. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1003344 
LibreCat: https://research-explorer.ista.ac.at/record/2277
DOI: 10.1371/journal.pcbi.1003344

Stephens, G., Mora, T., Tkadik, G., &; Bialek, W. (2013). Statistical thermodynamics of natural images. Physical Review Letters. American Physical Society.https://doi.org/10.1103/PhysRevLett.110.018701 
LibreCat: https://research-explorer.ista.ac.at/record/2914
DOI: 10.1103/PhysRevLett.110.018701
arXiv: 0806.2694

Tkadik, G., Walczak, A., &; Bialek, W. (2012). Optimizing information flow in small genetic networks. III. A self-interacting gene. https://doi.org/10.1103/PhysRevE.85.041903__Physical Review E Statistical Nonlinear and Soft Matter Physics . American Institute of Physics.https://doi.org/10.1103/PhysRevE.85.041903 
LibreCat: https://research-explorer.ista.ac.at/record/3262
DOI: 10.1103/PhysRevE.85.041903

Rieckh, G., Kreuzer, W., Waubke, H., &; Balazs, P. (2012). A 2.5D-Fourier-BEM model for vibrations in a tunnel running through layered anisotropic soil. https://doi.org/10.1016/j.enganabound.2011.12.014__Engineering Analysis with Boundary Elements. Elsevier.https://doi.org/10.1016/j.enganabound.2011.12.014 
LibreCat: https://research-explorer.ista.ac.at/record/3274
DOI: 10.1016/j.enganabound.2011.12.014

Tkadik, G., &; Walczak, A. (2011). Information transmission in genetic regulatory networks a review. Journal of Physics: Condensed Matter. IOP Publishing Ltd.https://doi.org/10.1088/0953-8984/23/15/153102 
LibreCat: https://research-explorer.ista.ac.at/record/3374
DOI: 10.1088/0953-8984/23/15/153102

Tkadik, G., Garrigan, P., Ratliff, C., Milcinski, G., Klein, J., Seyfarth, L.,https://doi.org/10.1371/journal.pone.0020409__Balasubramanian, V. (2011). Natural images from the birthplace of the human eye. PLoS One. Public Library of Science.https://doi.org/10.1371/journal.pone.0020409 
LibreCat: https://research-explorer.ista.ac.at/record/3384
DOI: 10.1371/journal.pone.0020409