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J. Humplik and G. Tkadik, “Probabilistic models for neural populations that naturally capture global coupling and criticality,” PLoS Computational Biology, vol. 13, no. 9. Public Library of Science, 2017.

R. Harpaz, G. Tkadik, and E. Schneidman, “Discrete modes of social information processing predict individual behavior of fish in a group,” PNAS, vol. 114, no. 38. National Academy of Sciences, pp. 10149–10154, 2017.

J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkadik, and M. Berry, “Data from: Error-robust modes of the retinal population code.” Dryad, 2017.

M. J. Chalk, P. Masset, B. Gutkin, and S. Denève, “Sensory noise predicts divisive reshaping of receptive fields,” PLoS Computational Biology, vol. 13, no. 6. Public Library of Science, 2017.

M. J. Chalk, P. Masset, B. Gutkin, and S. Denève, “Supplementary appendix.” Public Library of Science, 2017.

K. Mitosch, G. Rieckh, and M. T. Bollenbach, “Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment,” Cell Systems, vol. 4, no. 4. Cell Press, pp. 393–403, 2017.

A. Martin Del Campo Sanchez, S. A. Cepeda Humerez, and C. Uhler, “Exact goodness-of-fit testing for the Ising model,” Scandinavian Journal of Statistics, vol. 44, no. 2. Wiley-Blackwell, pp. 285–306, 2017.

S. Deny et al., “Multiplexed computations in retinal ganglion cells of a single type,” Nature Communications, vol. 8, no. 1. Nature Publishing Group, 2017.

A. Levina (Martius) and V. Priesemann, “Subsampling scaling,” Nature Communications, vol. 8. Nature Publishing Group, 2017.

T. Friedlander, R. Prizak, N. H. Barton, and G. Tkadik, “Evolution of new regulatory functions on biophysically realistic fitness landscapes,” Nature Communications, vol. 8, no. 1. Nature Publishing Group, 2017.

D. De Martino, “Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics,”  Physical Review E Statistical Nonlinear and Soft Matter Physics , vol. 95, no. 6. American Institute of Physics, p. 062419, 2017.

D. De Martino, F. Capuani, and A. De Martino, “Quantifying the entropic cost of cellular growth control,”  Physical Review E Statistical Nonlinear and Soft Matter Physics , vol. 96, no. 1. American Institute of Physics, 2017.

M. P. Zagórski et al., “Decoding of position in the developing neural tube from antiparallel morphogen gradients,” Science, vol. 356, no. 6345. American Association for the Advancement of Science, pp. 1379–1383, 2017.

S. Colabrese, D. De Martino, L. Leuzzi, and E. Marinari, “Phase transitions in integer linear problems,”  Journal of Statistical Mechanics: Theory and Experiment, vol. 2017, no. 9. IOPscience, 2017.

C. Savin and G. Tkadik, “Maximum entropy models as a tool for building precise neural controls,” Current Opinion in Neurobiology, vol. 46. Elsevier, pp. 120–126, 2017.

D. De Martino, “Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes,” Physical Review E, vol. 96, no. 6. American Physical Society, 2017.

M. Lang and E. Sontag, “Zeros of nonlinear systems with input invariances,” Automatica, vol. 81C. International Federation of Automatic Control, pp. 46–55, 2017.

O. Marre, G. Tkadik, D. Amodei, E. Schneidman, W. Bialek, and M. Berry, “Multi-electrode array recording from salamander retinal ganglion cells.” Institute of Science and Technology Austria, 2017.

T. Bergmiller et al., “Biased partitioning of the multi-drug efflux pump AcrAB-TolC underlies long-lived phenotypic heterogeneity.” Institute of Science and Technology Austria, 2017.