[{"oa_version":"Preprint","volume":13,"project":[{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734"}],"type":"journal_article","date_published":"2016-05-27T00:00:00Z","article_number":"036005","quality_controlled":"1","oa":1,"month":"05","publication":"Physical Biology","date_updated":"2021-01-12T06:50:23Z","publisher":"IOP Publishing Ltd.","doi":"10.1088/1478-3975/13/3/036005","publist_id":"5815","publication_status":"published","day":"27","date_created":"2018-12-11T11:51:46Z","author":[{"last_name":"De Martino","orcid":"0000-0002-5214-4706","first_name":"Daniele","id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","full_name":"De Martino, Daniele"},{"last_name":"Capuani","first_name":"Fabrizio","full_name":"Capuani, Fabrizio"},{"last_name":"De Martino","full_name":"De Martino, Andrea","first_name":"Andrea"}],"department":[{"_id":"GaTk"}],"year":"2016","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1601.03243"}],"citation":{"mla":"De Martino, Daniele, et al. “Growth against Entropy in Bacterial Metabolism: The Phenotypic Trade-off behind Empirical Growth Rate Distributions in E. Coli.” Physical Biology, vol. 13, no. 3, 036005, IOP Publishing Ltd., 2016, doi:10.1088/1478-3975/13/3/036005.","ama":"De Martino D, Capuani F, De Martino A. Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli. Physical Biology. 2016;13(3). doi:10.1088/1478-3975/13/3/036005","chicago":"De Martino, Daniele, Fabrizio Capuani, and Andrea De Martino. “Growth against Entropy in Bacterial Metabolism: The Phenotypic Trade-off behind Empirical Growth Rate Distributions in E. Coli.” Physical Biology. IOP Publishing Ltd., 2016. https://doi.org/10.1088/1478-3975/13/3/036005.","apa":"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","ista":"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. 13(3), 036005.","ieee":"D. De Martino, F. Capuani, and A. De Martino, “Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli,” Physical Biology, vol. 13, no. 3. IOP Publishing Ltd., 2016.","short":"D. De Martino, F. Capuani, A. De Martino, Physical Biology 13 (2016)."},"intvolume":" 13","ec_funded":1,"status":"public","acknowledgement":"The research leading to these results has received funding from the from the Marie\r\nCurie Action ITN NETADIS, grant agreement no. 290038.","_id":"1394","title":"Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli","abstract":[{"lang":"eng","text":"The solution space of genome-scale models of cellular metabolism provides a map between physically\r\nviable flux configurations and cellular metabolic phenotypes described, at the most basic level, by the\r\ncorresponding growth rates. By sampling the solution space of E. coliʼs metabolic network, we show\r\nthat empirical growth rate distributions recently obtained in experiments at single-cell resolution can\r\nbe explained in terms of a trade-off between the higher fitness of fast-growing phenotypes and the\r\nhigher entropy of slow-growing ones. Based on this, we propose a minimal model for the evolution of\r\na large bacterial population that captures this trade-off. The scaling relationships observed in\r\nexperiments encode, in such frameworks, for the same distance from the maximum achievable growth\r\nrate, the same degree of growth rate maximization, and/or the same rate of phenotypic change. Being\r\ngrounded on genome-scale metabolic network reconstructions, these results allow for multiple\r\nimplications and extensions in spite of the underlying conceptual simplicity."}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","issue":"3","scopus_import":1},{"arxiv":1,"date_created":"2018-12-11T11:51:55Z","day":"06","publist_id":"5787","publication_status":"published","author":[{"orcid":"0000-0002-7214-0171","last_name":"Bod'ová","full_name":"Bod'ová, Katarína","first_name":"Katarína","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Tkacik","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","full_name":"Tkacik, Gasper"},{"orcid":"0000-0002-8548-5240","last_name":"Barton","first_name":"Nicholas H","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","full_name":"Barton, Nicholas H"}],"page":"1523 - 1548","department":[{"_id":"GaTk"},{"_id":"NiBa"}],"language":[{"iso":"eng"}],"year":"2016","oa_version":"Preprint","external_id":{"arxiv":["1510.08344"]},"project":[{"grant_number":"250152","_id":"25B07788-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"Limits to selection in biology and in evolutionary computation"},{"_id":"255008E4-B435-11E9-9278-68D0E5697425","grant_number":"RGP0065/2012","name":"Information processing and computation in fish groups"}],"type":"journal_article","date_published":"2016-04-06T00:00:00Z","volume":202,"month":"04","quality_controlled":"1","oa":1,"article_processing_charge":"No","publisher":"Genetics Society of America","date_updated":"2025-05-28T11:42:47Z","doi":"10.1534/genetics.115.184127","publication":"Genetics","issue":"4","scopus_import":"1","intvolume":" 202","citation":{"ieee":"K. Bodova, G. Tkačik, and N. H. Barton, “A general approximation for the dynamics of quantitative traits,” Genetics, vol. 202, no. 4. Genetics Society of America, pp. 1523–1548, 2016.","short":"K. Bodova, G. Tkačik, N.H. Barton, Genetics 202 (2016) 1523–1548.","ista":"Bodova K, Tkačik G, Barton NH. 2016. A general approximation for the dynamics of quantitative traits. Genetics. 202(4), 1523–1548.","apa":"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","chicago":"Bodova, Katarina, Gašper Tkačik, and Nicholas H Barton. “A General Approximation for the Dynamics of Quantitative Traits.” Genetics. Genetics Society of America, 2016. https://doi.org/10.1534/genetics.115.184127.","ama":"Bodova K, Tkačik G, Barton NH. A general approximation for the dynamics of quantitative traits. Genetics. 2016;202(4):1523-1548. doi:10.1534/genetics.115.184127","mla":"Bodova, Katarina, et al. “A General Approximation for the Dynamics of Quantitative Traits.” Genetics, vol. 202, no. 4, Genetics Society of America, 2016, pp. 1523–48, doi:10.1534/genetics.115.184127."},"main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1510.08344"}],"status":"public","ec_funded":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"lang":"eng","text":"Selection, mutation, and random drift affect the dynamics of allele frequencies and consequently of quantitative traits. While the macroscopic dynamics of quantitative traits can be measured, the underlying allele frequencies are typically unobserved. Can we understand how the macroscopic observables evolve without following these microscopic processes? This problem has been studied previously by analogy with statistical mechanics: the allele frequency distribution at each time point is approximated by the stationary form, which maximizes entropy. We explore the limitations of this method when mutation is small (4Nμ < 1) so that populations are typically close to fixation, and we extend the theory in this regime to account for changes in mutation strength. We consider a single diallelic locus either under directional selection or with overdominance and then generalize to multiple unlinked biallelic loci with unequal effects. We find that the maximum-entropy approximation is remarkably accurate, even when mutation and selection change rapidly. "}],"title":"A general approximation for the dynamics of quantitative traits","_id":"1420"},{"ec_funded":1,"status":"public","main_file_link":[{"url":"https://papers.nips.cc/paper/6582-neurons-equipped-with-intrinsic-plasticity-learn-stimulus-intensity-statistics"}],"intvolume":" 29","citation":{"ista":"Monk T, Savin C, Lücke J. 2016. Neurons equipped with intrinsic plasticity learn stimulus intensity statistics. NIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 29, 4285–4293.","apa":"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.","short":"T. Monk, C. Savin, J. Lücke, in:, Neural Information Processing Systems, 2016, pp. 4285–4293.","ieee":"T. Monk, C. Savin, and J. Lücke, “Neurons equipped with intrinsic plasticity learn stimulus intensity statistics,” presented at the NIPS: Neural Information Processing Systems, Barcelona, Spaine, 2016, vol. 29, pp. 4285–4293.","ama":"Monk T, Savin C, Lücke J. Neurons equipped with intrinsic plasticity learn stimulus intensity statistics. In: Vol 29. Neural Information Processing Systems; 2016:4285-4293.","mla":"Monk, Travis, et al. Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics. Vol. 29, Neural Information Processing Systems, 2016, pp. 4285–93.","chicago":"Monk, Travis, Cristina Savin, and Jörg Lücke. “Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics,” 29:4285–93. Neural Information Processing Systems, 2016."},"_id":"948","title":"Neurons equipped with intrinsic plasticity learn stimulus intensity statistics","abstract":[{"text":"Experience constantly shapes neural circuits through a variety of plasticity mechanisms. While the functional roles of some plasticity mechanisms are well-understood, it remains unclear how changes in neural excitability contribute to learning. Here, we develop a normative interpretation of intrinsic plasticity (IP) as a key component of unsupervised learning. We introduce a novel generative mixture model that accounts for the class-specific statistics of stimulus intensities, and we derive a neural circuit that learns the input classes and their intensities. We will analytically show that inference and learning for our generative model can be achieved by a neural circuit with intensity-sensitive neurons equipped with a specific form of IP. Numerical experiments verify our analytical derivations and show robust behavior for artificial and natural stimuli. Our results link IP to non-trivial input statistics, in particular the statistics of stimulus intensities for classes to which a neuron is sensitive. More generally, our work paves the way toward new classification algorithms that are robust to intensity variations.","lang":"eng"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","acknowledgement":"DFG Cluster of Excellence EXC 1077/1 (Hearing4all) and LU 1196/5-1 (JL and TM), People Programme (Marie Curie Actions) FP7/2007-2013 grant agreement no. 291734 (CS)","scopus_import":1,"volume":29,"date_published":"2016-01-01T00:00:00Z","project":[{"grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"type":"conference","oa_version":"None","publisher":"Neural Information Processing Systems","date_updated":"2021-01-12T08:22:08Z","quality_controlled":"1","month":"01","page":"4285 - 4293","alternative_title":["Advances in Neural Information Processing Systems"],"conference":{"location":"Barcelona, Spaine","end_date":"2016-12-10","start_date":"2016-12-05","name":"NIPS: Neural Information Processing Systems"},"author":[{"first_name":"Travis","full_name":"Monk, Travis","last_name":"Monk"},{"last_name":"Savin","full_name":"Savin, Cristina","first_name":"Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Lücke","full_name":"Lücke, Jörg","first_name":"Jörg"}],"publication_status":"published","publist_id":"6469","day":"01","date_created":"2018-12-11T11:49:21Z","language":[{"iso":"eng"}],"year":"2016","department":[{"_id":"GaTk"}]},{"status":"public","date_published":"2016-09-27T00:00:00Z","type":"research_data_reference","citation":{"chicago":"Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Error Bound on an Estimator of Position.” Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628.s001.","mla":"Hillenbrand, Patrick, et al. Error Bound on an Estimator of Position. Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.s001.","ama":"Hillenbrand P, Gerland U, Tkačik G. Error bound on an estimator of position. 2016. doi:10.1371/journal.pone.0163628.s001","ieee":"P. Hillenbrand, U. Gerland, and G. Tkačik, “Error bound on an estimator of position.” Public Library of Science, 2016.","short":"P. Hillenbrand, U. Gerland, G. Tkačik, (2016).","apa":"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","ista":"Hillenbrand P, Gerland U, Tkačik G. 2016. Error bound on an estimator of position, Public Library of Science, 10.1371/journal.pone.0163628.s001."},"oa_version":"Published Version","_id":"9869","publisher":"Public Library of Science","date_updated":"2023-02-21T16:56:40Z","title":"Error bound on an estimator of position","doi":"10.1371/journal.pone.0163628.s001","abstract":[{"text":"A lower bound on the error of a positional estimator with limited positional information is derived.","lang":"eng"}],"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","article_processing_charge":"No","month":"09","author":[{"last_name":"Hillenbrand","first_name":"Patrick","full_name":"Hillenbrand, Patrick"},{"last_name":"Gerland","first_name":"Ulrich","full_name":"Gerland, Ulrich"},{"full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","last_name":"Tkačik","orcid":"0000-0002-6699-1455"}],"day":"27","date_created":"2021-08-10T08:53:48Z","year":"2016","related_material":{"record":[{"id":"1270","relation":"used_in_publication","status":"public"}]},"department":[{"_id":"GaTk"}]},{"author":[{"first_name":"Patrick","full_name":"Hillenbrand, Patrick","last_name":"Hillenbrand"},{"last_name":"Gerland","first_name":"Ulrich","full_name":"Gerland, Ulrich"},{"last_name":"Tkačik","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","full_name":"Tkačik, Gašper"}],"day":"27","date_created":"2021-08-10T09:23:45Z","year":"2016","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"1270"}]},"department":[{"_id":"GaTk"}],"status":"public","type":"research_data_reference","date_published":"2016-09-27T00:00:00Z","citation":{"ama":"Hillenbrand P, Gerland U, Tkačik G. Computation of positional information in an Ising model. 2016. doi:10.1371/journal.pone.0163628.s002","mla":"Hillenbrand, Patrick, et al. Computation of Positional Information in an Ising Model. Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.s002.","chicago":"Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Computation of Positional Information in an Ising Model.” Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628.s002.","ista":"Hillenbrand P, Gerland U, Tkačik G. 2016. Computation of positional information in an Ising model, Public Library of Science, 10.1371/journal.pone.0163628.s002.","apa":"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","short":"P. Hillenbrand, U. Gerland, G. Tkačik, (2016).","ieee":"P. Hillenbrand, U. Gerland, and G. Tkačik, “Computation of positional information in an Ising model.” Public Library of Science, 2016."},"oa_version":"Published Version","_id":"9870","publisher":"Public Library of Science","title":"Computation of positional information in an Ising model","doi":"10.1371/journal.pone.0163628.s002","date_updated":"2023-02-21T16:56:40Z","abstract":[{"text":"The effect of noise in the input field on an Ising model is approximated. Furthermore, methods to compute positional information in an Ising model by transfer matrices and Monte Carlo sampling are outlined.","lang":"eng"}],"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","article_processing_charge":"No","month":"09"},{"type":"research_data_reference","status":"public","oa_version":"Published Version","citation":{"ista":"Hillenbrand P, Gerland U, Tkačik G. 2016. Computation of positional information in a discrete morphogen field, Public Library of Science, 10.1371/journal.pone.0163628.s003.","apa":"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","short":"P. Hillenbrand, U. Gerland, G. Tkačik, (2016).","ieee":"P. Hillenbrand, U. Gerland, and G. Tkačik, “Computation of positional information in a discrete morphogen field.” Public Library of Science, 2016.","ama":"Hillenbrand P, Gerland U, Tkačik G. Computation of positional information in a discrete morphogen field. 2016. doi:10.1371/journal.pone.0163628.s003","mla":"Hillenbrand, Patrick, et al. Computation of Positional Information in a Discrete Morphogen Field. Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.s003.","chicago":"Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Computation of Positional Information in a Discrete Morphogen Field.” Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628.s003."},"abstract":[{"lang":"eng","text":"The positional information in a discrete morphogen field with Gaussian noise is computed."}],"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","article_processing_charge":"No","_id":"9871","title":"Computation of positional information in a discrete morphogen field","doi":"10.1371/journal.pone.0163628.s003","publisher":"Public Library of Science","date_updated":"2023-02-21T16:56:40Z","month":"09","author":[{"last_name":"Hillenbrand","full_name":"Hillenbrand, Patrick","first_name":"Patrick"},{"full_name":"Gerland, Ulrich","first_name":"Ulrich","last_name":"Gerland"},{"last_name":"Tkačik","orcid":"0000-0002-6699-1455","full_name":"Tkačik, Gašper","first_name":"Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"date_created":"2021-08-10T09:27:35Z","day":"27","year":"2016","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"1270"}]},"department":[{"_id":"GaTk"}]},{"department":[{"_id":"GaTk"}],"scopus_import":1,"language":[{"iso":"eng"}],"year":"2016","related_material":{"link":[{"relation":"other","url":"https://papers.nips.cc/paper/6101-relevant-sparse-codes-with-variational-information-bottleneck"}]},"date_created":"2018-12-11T11:50:03Z","publication_status":"published","publist_id":"6298","day":"01","author":[{"id":"2BAAC544-F248-11E8-B48F-1D18A9856A87","first_name":"Matthew J","full_name":"Chalk, Matthew J","orcid":"0000-0001-7782-4436","last_name":"Chalk"},{"last_name":"Marre","full_name":"Marre, Olivier","first_name":"Olivier"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik"}],"page":"1965-1973","alternative_title":["Advances in Neural Information Processing Systems"],"conference":{"start_date":"2016-12-05","name":"NIPS: Neural Information Processing Systems","location":"Barcelona, Spain","end_date":"2016-12-10"},"month":"12","quality_controlled":"1","oa":1,"abstract":[{"lang":"eng","text":"In many applications, it is desirable to extract only the relevant aspects of data. A principled way to do this is the information bottleneck (IB) method, where one seeks a code that maximises information about a relevance variable, Y, while constraining the information encoded about the original data, X. Unfortunately however, the IB method is computationally demanding when data are high-dimensional and/or non-gaussian. Here we propose an approximate variational scheme for maximising a lower bound on the IB objective, analogous to variational EM. Using this method, we derive an IB algorithm to recover features that are both relevant and sparse. Finally, we demonstrate how kernelised versions of the algorithm can be used to address a broad range of problems with non-linear relation between X and Y."}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","_id":"1082","title":"Relevant sparse codes with variational information bottleneck","publisher":"Neural Information Processing Systems","date_updated":"2021-01-12T06:48:09Z","oa_version":"Preprint","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1605.07332"}],"citation":{"chicago":"Chalk, Matthew J, Olivier Marre, and Gašper Tkačik. “Relevant Sparse Codes with Variational Information Bottleneck,” 29:1965–73. Neural Information Processing Systems, 2016.","mla":"Chalk, Matthew J., et al. Relevant Sparse Codes with Variational Information Bottleneck. Vol. 29, Neural Information Processing Systems, 2016, pp. 1965–73.","ama":"Chalk MJ, Marre O, Tkačik G. Relevant sparse codes with variational information bottleneck. In: Vol 29. Neural Information Processing Systems; 2016:1965-1973.","ieee":"M. J. Chalk, O. Marre, and G. Tkačik, “Relevant sparse codes with variational information bottleneck,” presented at the NIPS: Neural Information Processing Systems, Barcelona, Spain, 2016, vol. 29, pp. 1965–1973.","short":"M.J. Chalk, O. Marre, G. Tkačik, in:, Neural Information Processing Systems, 2016, pp. 1965–1973.","apa":"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.","ista":"Chalk MJ, Marre O, Tkačik G. 2016. Relevant sparse codes with variational information bottleneck. NIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 29, 1965–1973."},"intvolume":" 29","volume":29,"type":"conference","date_published":"2016-12-01T00:00:00Z","status":"public"},{"scopus_import":1,"status":"public","ec_funded":1,"citation":{"short":"C. Savin, G. Tkačik, in:, Neural Information Processing Systems, 2016, pp. 3610–3618.","ieee":"C. Savin and G. Tkačik, “Estimating nonlinear neural response functions using GP priors and Kronecker methods,” presented at the NIPS: Neural Information Processing Systems, Barcelona; Spain, 2016, vol. 29, pp. 3610–3618.","apa":"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.","ista":"Savin C, Tkačik G. 2016. Estimating nonlinear neural response functions using GP priors and Kronecker methods. NIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 29, 3610–3618.","chicago":"Savin, Cristina, and Gašper Tkačik. “Estimating Nonlinear Neural Response Functions Using GP Priors and Kronecker Methods,” 29:3610–18. Neural Information Processing Systems, 2016.","mla":"Savin, Cristina, and Gašper Tkačik. Estimating Nonlinear Neural Response Functions Using GP Priors and Kronecker Methods. Vol. 29, Neural Information Processing Systems, 2016, pp. 3610–18.","ama":"Savin C, Tkačik G. Estimating nonlinear neural response functions using GP priors and Kronecker methods. In: Vol 29. Neural Information Processing Systems; 2016:3610-3618."},"intvolume":" 29","main_file_link":[{"url":"http://papers.nips.cc/paper/6153-estimating-nonlinear-neural-response-functions-using-gp-priors-and-kronecker-methods"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","abstract":[{"text":"Jointly characterizing neural responses in terms of several external variables promises novel insights into circuit function, but remains computationally prohibitive in practice. Here we use gaussian process (GP) priors and exploit recent advances in fast GP inference and learning based on Kronecker methods, to efficiently estimate multidimensional nonlinear tuning functions. Our estimator require considerably less data than traditional methods and further provides principled uncertainty estimates. We apply these tools to hippocampal recordings during open field exploration and use them to characterize the joint dependence of CA1 responses on the position of the animal and several other variables, including the animal\\'s speed, direction of motion, and network oscillations.Our results provide an unprecedentedly detailed quantification of the tuning of hippocampal neurons. The model\\'s generality suggests that our approach can be used to estimate neural response properties in other brain regions.","lang":"eng"}],"title":"Estimating nonlinear neural response functions using GP priors and Kronecker methods","_id":"1105","acknowledgement":"We thank Jozsef Csicsvari for kindly sharing the CA1 data.\r\nThis work was supported by the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme(FP7/2007-2013) under REA grant agreement no. 291734.","author":[{"last_name":"Savin","full_name":"Savin, Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87","first_name":"Cristina"},{"orcid":"0000-0002-6699-1455","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","full_name":"Tkacik, Gasper"}],"conference":{"start_date":"2016-12-05","name":"NIPS: Neural Information Processing Systems","location":"Barcelona; Spain","end_date":"2016-12-10"},"alternative_title":["Advances in Neural Information Processing Systems"],"page":"3610-3618","date_created":"2018-12-11T11:50:10Z","day":"01","publication_status":"published","publist_id":"6265","year":"2016","language":[{"iso":"eng"}],"department":[{"_id":"GaTk"}],"type":"conference","project":[{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"date_published":"2016-12-01T00:00:00Z","volume":29,"oa_version":"None","publisher":"Neural Information Processing Systems","date_updated":"2021-01-12T06:48:19Z","month":"12","quality_controlled":"1"},{"alternative_title":["ISTA Thesis"],"page":"114","author":[{"first_name":"Georg","id":"34DA8BD6-F248-11E8-B48F-1D18A9856A87","full_name":"Rieckh, Georg","last_name":"Rieckh"}],"publist_id":"6232","publication_status":"published","day":"01","degree_awarded":"PhD","date_created":"2018-12-11T11:50:18Z","ddc":["570"],"year":"2016","language":[{"iso":"eng"}],"supervisor":[{"first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik"}],"department":[{"_id":"GaTk"}],"date_published":"2016-08-01T00:00:00Z","type":"dissertation","oa_version":"Published Version","date_updated":"2023-09-07T11:44:34Z","publisher":"Institute of Science and Technology Austria","article_processing_charge":"No","oa":1,"file":[{"date_created":"2019-08-13T11:46:25Z","file_id":"6815","content_type":"application/pdf","relation":"main_file","creator":"dernst","checksum":"ec453918c3bf8e6f460fd1156ef7b493","file_name":"Thesis_Georg_Rieckh_w_signature_page.pdf","access_level":"closed","file_size":2614660,"date_updated":"2019-08-13T11:46:25Z"},{"file_size":6096178,"date_updated":"2020-09-21T11:30:40Z","checksum":"51ae398166370d18fd22478b6365c4da","success":1,"file_name":"Thesis_Georg_Rieckh.pdf","access_level":"open_access","relation":"main_file","creator":"dernst","date_created":"2020-09-21T11:30:40Z","file_id":"8542","content_type":"application/pdf"}],"month":"08","publication_identifier":{"issn":["2663-337X"]},"file_date_updated":"2020-09-21T11:30:40Z","status":"public","has_accepted_license":"1","citation":{"chicago":"Rieckh, Georg. “Studying the Complexities of Transcriptional Regulation.” Institute of Science and Technology Austria, 2016.","mla":"Rieckh, Georg. Studying the Complexities of Transcriptional Regulation. Institute of Science and Technology Austria, 2016.","ama":"Rieckh G. Studying the complexities of transcriptional regulation. 2016.","ieee":"G. Rieckh, “Studying the complexities of transcriptional regulation,” Institute of Science and Technology Austria, 2016.","short":"G. Rieckh, Studying the Complexities of Transcriptional Regulation, Institute of Science and Technology Austria, 2016.","apa":"Rieckh, G. (2016). Studying the complexities of transcriptional regulation. Institute of Science and Technology Austria.","ista":"Rieckh G. 2016. Studying the complexities of transcriptional regulation. Institute of Science and Technology Austria."},"_id":"1128","title":"Studying the complexities of transcriptional regulation","abstract":[{"lang":"eng","text":"The process of gene expression is central to the modern understanding of how cellular systems\r\nfunction. In this process, a special kind of regulatory proteins, called transcription factors,\r\nare important to determine how much protein is produced from a given gene. As biological\r\ninformation is transmitted from transcription factor concentration to mRNA levels to amounts of\r\nprotein, various sources of noise arise and pose limits to the fidelity of intracellular signaling.\r\nThis thesis concerns itself with several aspects of stochastic gene expression: (i) the mathematical\r\ndescription of complex promoters responsible for the stochastic production of biomolecules,\r\n(ii) fundamental limits to information processing the cell faces due to the interference from multiple\r\nfluctuating signals, (iii) how the presence of gene expression noise influences the evolution\r\nof regulatory sequences, (iv) and tools for the experimental study of origins and consequences\r\nof cell-cell heterogeneity, including an application to bacterial stress response systems."}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1"},{"date_created":"2018-12-11T11:50:24Z","day":"01","publication_status":"published","publist_id":"6210","author":[{"first_name":"Christian","full_name":"Schilling, Christian","last_name":"Schilling"},{"orcid":"0000-0002-0686-0365","last_name":"Bogomolov","full_name":"Bogomolov, Sergiy","id":"369D9A44-F248-11E8-B48F-1D18A9856A87","first_name":"Sergiy"},{"orcid":"0000−0002−2985−7724","last_name":"Henzinger","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","first_name":"Thomas A","full_name":"Henzinger, Thomas A"},{"last_name":"Podelski","first_name":"Andreas","full_name":"Podelski, Andreas"},{"last_name":"Ruess","orcid":"0000-0003-1615-3282","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","first_name":"Jakob","full_name":"Ruess, Jakob"}],"page":"15 - 25","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"related_material":{"record":[{"id":"1658","status":"public","relation":"earlier_version"}]},"year":"2016","language":[{"iso":"eng"}],"oa_version":"None","project":[{"_id":"25EE3708-B435-11E9-9278-68D0E5697425","grant_number":"267989","call_identifier":"FP7","name":"Quantitative Reactive Modeling"},{"grant_number":"S 11407_N23","_id":"25832EC2-B435-11E9-9278-68D0E5697425","name":"Rigorous Systems Engineering","call_identifier":"FWF"},{"grant_number":"Z211","_id":"25F42A32-B435-11E9-9278-68D0E5697425","name":"The Wittgenstein Prize","call_identifier":"FWF"},{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"date_published":"2016-11-01T00:00:00Z","type":"journal_article","volume":149,"month":"11","quality_controlled":"1","publisher":"Elsevier","doi":"10.1016/j.biosystems.2016.07.005","date_updated":"2023-02-23T10:08:46Z","publication":"Biosystems","scopus_import":1,"intvolume":" 149","citation":{"ama":"Schilling C, Bogomolov S, Henzinger TA, Podelski A, Ruess J. Adaptive moment closure for parameter inference of biochemical reaction networks. Biosystems. 2016;149:15-25. doi:10.1016/j.biosystems.2016.07.005","mla":"Schilling, Christian, et al. “Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks.” Biosystems, vol. 149, Elsevier, 2016, pp. 15–25, doi:10.1016/j.biosystems.2016.07.005.","chicago":"Schilling, Christian, Sergiy Bogomolov, Thomas A Henzinger, Andreas Podelski, and Jakob Ruess. “Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks.” Biosystems. Elsevier, 2016. https://doi.org/10.1016/j.biosystems.2016.07.005.","ista":"Schilling C, Bogomolov S, Henzinger TA, Podelski A, Ruess J. 2016. Adaptive moment closure for parameter inference of biochemical reaction networks. Biosystems. 149, 15–25.","apa":"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","ieee":"C. Schilling, S. Bogomolov, T. A. Henzinger, A. Podelski, and J. Ruess, “Adaptive moment closure for parameter inference of biochemical reaction networks,” Biosystems, vol. 149. Elsevier, pp. 15–25, 2016.","short":"C. Schilling, S. Bogomolov, T.A. Henzinger, A. Podelski, J. Ruess, Biosystems 149 (2016) 15–25."},"status":"public","ec_funded":1,"acknowledgement":"This work is based on the CMSB 2015 paper “Adaptive moment closure for parameter inference of biochemical reaction networks” (Bogomolov et al., 2015). The work was partly supported by the German Research Foundation (DFG) as part of the Transregional Collaborative Research Center “Automatic Verification and Analysis of Complex Systems” (SFB/TR 14 AVACS1), by the European Research Council (ERC) under grant 267989 (QUAREM) and by the Austrian Science Fund (FWF) under grants S11402-N23 (RiSE) and Z211-N23 (Wittgenstein Award). J.R. acknowledges support from the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7/2007-2013) under REA grant agreement no. 291734.","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"lang":"eng","text":"Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes. When such models are employed to answer questions in applications, in order to ensure that the model provides a sufficiently accurate representation of the real system, it is of vital importance that the model parameters are inferred from real measured data. This, however, is often a formidable task and all of the existing methods fail in one case or the other, usually because the underlying CTMC model is high-dimensional and computationally difficult to analyze. The parameter inference methods that tend to scale best in the dimension of the CTMC are based on so-called moment closure approximations. However, there exists a large number of different moment closure approximations and it is typically hard to say a priori which of the approximations is the most suitable for the inference procedure. Here, we propose a moment-based parameter inference method that automatically chooses the most appropriate moment closure method. Accordingly, contrary to existing methods, the user is not required to be experienced in moment closure techniques. In addition to that, our method adaptively changes the approximation during the parameter inference to ensure that always the best approximation is used, even in cases where different approximations are best in different regions of the parameter space. © 2016 Elsevier Ireland Ltd"}],"title":"Adaptive moment closure for parameter inference of biochemical reaction networks","_id":"1148"},{"issue":"6","file_date_updated":"2020-07-14T12:44:37Z","scopus_import":1,"has_accepted_license":"1","intvolume":" 38","citation":{"chicago":"Lang, Moritz, and Jörg Stelling. “Modular Parameter Identification of Biomolecular Networks.” SIAM Journal on Scientific Computing. Society for Industrial and Applied Mathematics , 2016. https://doi.org/10.1137/15M103306X.","ama":"Lang M, Stelling J. Modular parameter identification of biomolecular networks. SIAM Journal on Scientific Computing. 2016;38(6):B988-B1008. doi:10.1137/15M103306X","mla":"Lang, Moritz, and Jörg Stelling. “Modular Parameter Identification of Biomolecular Networks.” SIAM Journal on Scientific Computing, vol. 38, no. 6, Society for Industrial and Applied Mathematics , 2016, pp. B988–1008, doi:10.1137/15M103306X.","short":"M. Lang, J. Stelling, SIAM Journal on Scientific Computing 38 (2016) B988–B1008.","ieee":"M. Lang and J. Stelling, “Modular parameter identification of biomolecular networks,” SIAM Journal on Scientific Computing, vol. 38, no. 6. Society for Industrial and Applied Mathematics , pp. B988–B1008, 2016.","ista":"Lang M, Stelling J. 2016. Modular parameter identification of biomolecular networks. SIAM Journal on Scientific Computing. 38(6), B988–B1008.","apa":"Lang, M., & Stelling, J. (2016). Modular parameter identification of biomolecular networks. SIAM Journal on Scientific Computing. Society for Industrial and Applied Mathematics . https://doi.org/10.1137/15M103306X"},"status":"public","_id":"1170","title":"Modular parameter identification of biomolecular networks","abstract":[{"text":"The increasing complexity of dynamic models in systems and synthetic biology poses computational challenges especially for the identification of model parameters. While modularization of the corresponding optimization problems could help reduce the “curse of dimensionality,” abundant feedback and crosstalk mechanisms prohibit a simple decomposition of most biomolecular networks into subnetworks, or modules. Drawing on ideas from network modularization and multiple-shooting optimization, we present here a modular parameter identification approach that explicitly allows for such interdependencies. Interfaces between our modules are given by the experimentally measured molecular species. This definition allows deriving good (initial) estimates for the inter-module communication directly from the experimental data. Given these estimates, the states and parameter sensitivities of different modules can be integrated independently. To achieve consistency between modules, we iteratively adjust the estimates for inter-module communication while optimizing the parameters. After convergence to an optimal parameter set---but not during earlier iterations---the intermodule communication as well as the individual modules\\' state dynamics agree with the dynamics of the nonmodularized network. Our modular parameter identification approach allows for easy parallelization; it can reduce the computational complexity for larger networks and decrease the probability to converge to suboptimal local minima. We demonstrate the algorithm\\'s performance in parameter estimation for two biomolecular networks, a synthetic genetic oscillator and a mammalian signaling pathway.","lang":"eng"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","publist_id":"6186","publication_status":"published","day":"15","date_created":"2018-12-11T11:50:31Z","page":"B988 - B1008","pubrep_id":"811","author":[{"last_name":"Lang","full_name":"Lang, Moritz","id":"29E0800A-F248-11E8-B48F-1D18A9856A87","first_name":"Moritz"},{"first_name":"Jörg","full_name":"Stelling, Jörg","last_name":"Stelling"}],"department":[{"_id":"CaGu"},{"_id":"GaTk"}],"ddc":["003","518","570","621"],"language":[{"iso":"eng"}],"year":"2016","oa_version":"Submitted Version","volume":38,"date_published":"2016-11-15T00:00:00Z","type":"journal_article","file":[{"checksum":"781bc3ffd30b2dd65b7727c5a285fc78","access_level":"local","file_name":"IST-2017-811-v1+1_modular_parameter_identification.pdf","file_size":871964,"date_updated":"2020-07-14T12:44:37Z","file_id":"5095","date_created":"2018-12-12T10:14:41Z","content_type":"application/pdf","creator":"system","relation":"main_file"}],"quality_controlled":"1","month":"11","publication":"SIAM Journal on Scientific Computing","doi":"10.1137/15M103306X","date_updated":"2021-01-12T06:48:49Z","publisher":"Society for Industrial and Applied Mathematics "},{"oa_version":"None","citation":{"mla":"Tkačik, Gašper. “Understanding Regulatory Networks Requires More than Computing a Multitude of Graph Statistics: Comment on "Drivers of Structural Features in Gene Regulatory Networks: From Biophysical Constraints to Biological Function" by O. C. Martin et Al.” Physics of Life Reviews, vol. 17, Elsevier, 2016, pp. 166–67, doi:10.1016/j.plrev.2016.06.005.","ama":"Tkačik G. Understanding regulatory networks requires more than computing a multitude of graph statistics: Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O. C. Martin et al. Physics of Life Reviews. 2016;17:166-167. doi:10.1016/j.plrev.2016.06.005","chicago":"Tkačik, Gašper. “Understanding Regulatory Networks Requires More than Computing a Multitude of Graph Statistics: Comment on "Drivers of Structural Features in Gene Regulatory Networks: From Biophysical Constraints to Biological Function" by O. C. Martin et Al.” Physics of Life Reviews. Elsevier, 2016. https://doi.org/10.1016/j.plrev.2016.06.005.","apa":"Tkačik, G. (2016). Understanding regulatory networks requires more than computing a multitude of graph statistics: Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O. C. Martin et al. Physics of Life Reviews. Elsevier. https://doi.org/10.1016/j.plrev.2016.06.005","ista":"Tkačik G. 2016. Understanding regulatory networks requires more than computing a multitude of graph statistics: Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O. C. Martin et al. Physics of Life Reviews. 17, 166–167.","short":"G. Tkačik, Physics of Life Reviews 17 (2016) 166–167.","ieee":"G. Tkačik, “Understanding regulatory networks requires more than computing a multitude of graph statistics: Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O. C. Martin et al.,” Physics of Life Reviews, vol. 17. Elsevier, pp. 166–167, 2016."},"intvolume":" 17","type":"journal_article","date_published":"2016-07-01T00:00:00Z","volume":17,"status":"public","month":"07","quality_controlled":"1","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","doi":"10.1016/j.plrev.2016.06.005","publisher":"Elsevier","date_updated":"2021-01-12T06:48:50Z","title":"Understanding regulatory networks requires more than computing a multitude of graph statistics: Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O. C. Martin et al.","_id":"1171","publication":"Physics of Life Reviews","date_created":"2018-12-11T11:50:32Z","day":"01","publication_status":"published","publist_id":"6185","author":[{"full_name":"Tkacik, Gasper","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik","orcid":"0000-0002-6699-1455"}],"page":"166 - 167","department":[{"_id":"GaTk"}],"scopus_import":1,"language":[{"iso":"eng"}],"year":"2016"},{"doi":"10.1088/1742-5468/aa4e8f","publisher":"IOPscience","date_updated":"2021-01-12T06:48:57Z","publication":" Journal of Statistical Mechanics: Theory and Experiment","oa":1,"quality_controlled":"1","article_number":"123502","month":"12","date_published":"2016-12-30T00:00:00Z","type":"journal_article","project":[{"grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"volume":2016,"oa_version":"Preprint","language":[{"iso":"eng"}],"year":"2016","department":[{"_id":"GaTk"}],"author":[{"last_name":"De Martino","orcid":"0000-0002-5214-4706","id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","first_name":"Daniele","full_name":"De Martino, Daniele"},{"last_name":"Masoero","first_name":"Davide","full_name":"Masoero, Davide"}],"day":"30","publication_status":"published","publist_id":"6165","date_created":"2018-12-11T11:50:37Z","title":"Asymptotic analysis of noisy fitness maximization, applied to metabolism & growth","_id":"1188","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","abstract":[{"text":"We consider a population dynamics model coupling cell growth to a diffusion in the space of metabolic phenotypes as it can be obtained from realistic constraints-based modelling. \r\nIn the asymptotic regime of slow\r\ndiffusion, that coincides with the relevant experimental range, the resulting\r\nnon-linear Fokker–Planck equation is solved for the steady state in the WKB\r\napproximation that maps it into the ground state of a quantum particle in an\r\nAiry potential plus a centrifugal term. We retrieve scaling laws for growth rate\r\nfluctuations and time response with respect to the distance from the maximum\r\ngrowth rate suggesting that suboptimal populations can have a faster response\r\nto perturbations.","lang":"eng"}],"acknowledgement":"D De Martino is supported by the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7/2007–2013) under REA grant agreement no. [291734]. D Masoero is supported by the FCT scholarship, number SFRH/BPD/75908/2011. D De Martino thanks the Grupo de Física Matemática of the Universidade de Lisboa for the kind hospitality. We also wish to thank Matteo Osella, Vincenzo Vitagliano and Vera Luz Masoero for useful discussions, also late at night.","status":"public","ec_funded":1,"citation":{"apa":"De Martino, D., & Masoero, D. (2016). Asymptotic analysis of noisy fitness maximization, applied to metabolism & growth. Journal of Statistical Mechanics: Theory and Experiment. IOPscience. https://doi.org/10.1088/1742-5468/aa4e8f","ista":"De Martino D, Masoero D. 2016. Asymptotic analysis of noisy fitness maximization, applied to metabolism & growth. Journal of Statistical Mechanics: Theory and Experiment. 2016(12), 123502.","ieee":"D. De Martino and D. Masoero, “Asymptotic analysis of noisy fitness maximization, applied to metabolism & growth,” Journal of Statistical Mechanics: Theory and Experiment, vol. 2016, no. 12. IOPscience, 2016.","short":"D. De Martino, D. Masoero, Journal of Statistical Mechanics: Theory and Experiment 2016 (2016).","mla":"De Martino, Daniele, and Davide Masoero. “Asymptotic Analysis of Noisy Fitness Maximization, Applied to Metabolism & Growth.” Journal of Statistical Mechanics: Theory and Experiment, vol. 2016, no. 12, 123502, IOPscience, 2016, doi:10.1088/1742-5468/aa4e8f.","ama":"De Martino D, Masoero D. Asymptotic analysis of noisy fitness maximization, applied to metabolism & growth. Journal of Statistical Mechanics: Theory and Experiment. 2016;2016(12). doi:10.1088/1742-5468/aa4e8f","chicago":"De Martino, Daniele, and Davide Masoero. “Asymptotic Analysis of Noisy Fitness Maximization, Applied to Metabolism & Growth.” Journal of Statistical Mechanics: Theory and Experiment. IOPscience, 2016. https://doi.org/10.1088/1742-5468/aa4e8f."},"intvolume":" 2016","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1606.09048"}],"scopus_import":1,"issue":"12"},{"file_date_updated":"2020-07-14T12:44:38Z","scopus_import":1,"issue":"11","acknowledgement":"JSP was supported by a C.V. Starr Fellowship from the Starr Foundation (http://www.starrfoundation.org/). GT was supported by Austrian Research Foundation (https://www.fwf.ac.at/en/) grant FWF P25651. MJB received support from National Eye Institute (https://nei.nih.gov/) grant EY 14196 and from the National Science Foundation grant 1504977. The authors thank Cristina Savin and Vicent Botella-Soler for helpful comments on the manuscript.","title":"Error-robust modes of the retinal population code","_id":"1197","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","abstract":[{"text":"Across the nervous system, certain population spiking patterns are observed far more frequently than others. A hypothesis about this structure is that these collective activity patterns function as population codewords–collective modes–carrying information distinct from that of any single cell. We investigate this phenomenon in recordings of ∼150 retinal ganglion cells, the retina’s output. We develop a novel statistical model that decomposes the population response into modes; it predicts the distribution of spiking activity in the ganglion cell population with high accuracy. We found that the modes represent localized features of the visual stimulus that are distinct from the features represented by single neurons. Modes form clusters of activity states that are readily discriminated from one another. When we repeated the same visual stimulus, we found that the same mode was robustly elicited. These results suggest that retinal ganglion cells’ collective signaling is endowed with a form of error-correcting code–a principle that may hold in brain areas beyond retina.","lang":"eng"}],"citation":{"apa":"Prentice, J., Marre, O., Ioffe, M., Loback, A., Tkačik, 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","ista":"Prentice J, Marre O, Ioffe M, Loback A, Tkačik G, Berry M. 2016. Error-robust modes of the retinal population code. PLoS Computational Biology. 12(11), e1005855.","short":"J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, M. Berry, PLoS Computational Biology 12 (2016).","ieee":"J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, and M. Berry, “Error-robust modes of the retinal population code,” PLoS Computational Biology, vol. 12, no. 11. Public Library of Science, 2016.","mla":"Prentice, Jason, et al. “Error-Robust Modes of the Retinal Population Code.” PLoS Computational Biology, vol. 12, no. 11, e1005855, Public Library of Science, 2016, doi:10.1371/journal.pcbi.1005148.","ama":"Prentice J, Marre O, Ioffe M, Loback A, Tkačik G, Berry M. Error-robust modes of the retinal population code. PLoS Computational Biology. 2016;12(11). doi:10.1371/journal.pcbi.1005148","chicago":"Prentice, Jason, Olivier Marre, Mark Ioffe, Adrianna Loback, Gašper Tkačik, and Michael Berry. “Error-Robust Modes of the Retinal Population Code.” PLoS Computational Biology. Public Library of Science, 2016. https://doi.org/10.1371/journal.pcbi.1005148."},"intvolume":" 12","has_accepted_license":"1","status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"department":[{"_id":"GaTk"}],"related_material":{"record":[{"status":"public","relation":"research_data","id":"9709"}]},"year":"2016","language":[{"iso":"eng"}],"ddc":["570"],"day":"17","publist_id":"6153","publication_status":"published","date_created":"2018-12-11T11:50:40Z","license":"https://creativecommons.org/licenses/by/4.0/","author":[{"first_name":"Jason","full_name":"Prentice, Jason","last_name":"Prentice"},{"first_name":"Olivier","full_name":"Marre, Olivier","last_name":"Marre"},{"last_name":"Ioffe","first_name":"Mark","full_name":"Ioffe, Mark"},{"full_name":"Loback, Adrianna","first_name":"Adrianna","last_name":"Loback"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","full_name":"Tkacik, Gasper","last_name":"Tkacik","orcid":"0000-0002-6699-1455"},{"full_name":"Berry, Michael","first_name":"Michael","last_name":"Berry"}],"oa":1,"quality_controlled":"1","file":[{"creator":"kschuh","relation":"main_file","date_created":"2019-01-25T10:35:00Z","file_id":"5884","content_type":"application/pdf","file_size":4492021,"date_updated":"2020-07-14T12:44:38Z","checksum":"47b08cbd4dbf32b25ba161f5f4b262cc","file_name":"2016_PLOS_Prentice.pdf","access_level":"open_access"}],"article_number":"e1005855","month":"11","date_updated":"2023-02-23T14:05:40Z","publisher":"Public Library of Science","doi":"10.1371/journal.pcbi.1005148","publication":"PLoS Computational Biology","oa_version":"Published Version","date_published":"2016-11-17T00:00:00Z","project":[{"_id":"254D1A94-B435-11E9-9278-68D0E5697425","grant_number":"P 25651-N26","name":"Sensitivity to higher-order statistics in natural scenes","call_identifier":"FWF"}],"type":"journal_article","volume":12},{"acknowledgement":"We are grateful to ABCs for providing strains and the Bacterial Meningitis Laboratory for technical support.","_id":"1203","title":"Comparative genomic analysis of Haemophilus haemolyticus and nontypeable Haemophilus influenzae and a new testing scheme for their discrimination","abstract":[{"lang":"eng","text":"Haemophilus haemolyticus has been recently discovered to have the potential to cause invasive disease. It is closely related to nontypeable Haemophilus influenzae (NT H. influenzae). NT H. influenzae and H. haemolyticus are often misidentified because none of the existing tests targeting the known phenotypes of H. haemolyticus are able to specifically identify H. haemolyticus. Through comparative genomic analysis of H. haemolyticus and NT H. influenzae, we identified genes unique to H. haemolyticus that can be used as targets for the identification of H. haemolyticus. A real-time PCR targeting purT (encoding phosphoribosylglycinamide formyltransferase 2 in the purine synthesis pathway) was developed and evaluated. The lower limit of detection was 40 genomes/PCR; the sensitivity and specificity in detecting H. haemolyticus were 98.9% and 97%, respectively. To improve the discrimination of H. haemolyticus and NT H. influenzae, a testing scheme combining two targets (H. haemolyticus purT and H. influenzae hpd, encoding protein D lipoprotein) was also evaluated and showed 96.7% sensitivity and 98.2% specificity for the identification of H. haemolyticus and 92.8% sensitivity and 100% specificity for the identification of H. influenzae, respectively. The dual-target testing scheme can be used for the diagnosis and surveillance of infection and disease caused by H. haemolyticus and NT H. influenzae."}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5121393/","open_access":"1"}],"intvolume":" 54","citation":{"chicago":"Hu, Fang, Lavanya Rishishwar, Ambily Sivadas, Gabriel Mitchell, Jordan King, Timothy Murphy, Janet Gilsdorf, Leonard Mayer, and Xin Wang. “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, 2016. https://doi.org/10.1128/JCM.01511-16.","ama":"Hu F, Rishishwar L, Sivadas A, et al. Comparative genomic analysis of Haemophilus haemolyticus and nontypeable Haemophilus influenzae and a new testing scheme for their discrimination. Journal of Clinical Microbiology. 2016;54(12):3010-3017. doi:10.1128/JCM.01511-16","mla":"Hu, Fang, et al. “Comparative Genomic Analysis of Haemophilus Haemolyticus and Nontypeable Haemophilus Influenzae and a New Testing Scheme for Their Discrimination.” Journal of Clinical Microbiology, vol. 54, no. 12, American Society for Microbiology, 2016, pp. 3010–17, doi:10.1128/JCM.01511-16.","ieee":"F. Hu et al., “Comparative genomic analysis of Haemophilus haemolyticus and nontypeable Haemophilus influenzae and a new testing scheme for their discrimination,” Journal of Clinical Microbiology, vol. 54, no. 12. American Society for Microbiology, pp. 3010–3017, 2016.","short":"F. Hu, L. Rishishwar, A. Sivadas, G. Mitchell, J. King, T. Murphy, J. Gilsdorf, L. Mayer, X. Wang, Journal of Clinical Microbiology 54 (2016) 3010–3017.","ista":"Hu F, Rishishwar L, Sivadas A, Mitchell G, King J, Murphy T, Gilsdorf J, Mayer L, 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. 54(12), 3010–3017.","apa":"Hu, F., Rishishwar, L., Sivadas, A., Mitchell, G., King, J., Murphy, T., … 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"},"status":"public","scopus_import":1,"issue":"12","quality_controlled":"1","oa":1,"month":"12","publication":"Journal of Clinical Microbiology","date_updated":"2021-01-12T06:49:04Z","doi":"10.1128/JCM.01511-16","publisher":"American Society for Microbiology","oa_version":"Submitted Version","volume":54,"date_published":"2016-12-01T00:00:00Z","type":"journal_article","department":[{"_id":"GaTk"}],"year":"2016","language":[{"iso":"eng"}],"publication_status":"published","publist_id":"6146","day":"01","date_created":"2018-12-11T11:50:41Z","page":"3010 - 3017","author":[{"first_name":"Fang","full_name":"Hu, Fang","last_name":"Hu"},{"first_name":"Lavanya","full_name":"Rishishwar, Lavanya","last_name":"Rishishwar"},{"last_name":"Sivadas","full_name":"Sivadas, Ambily","first_name":"Ambily"},{"full_name":"Mitchell, Gabriel","id":"315BCD80-F248-11E8-B48F-1D18A9856A87","first_name":"Gabriel","last_name":"Mitchell"},{"last_name":"King","first_name":"Jordan","full_name":"King, Jordan"},{"last_name":"Murphy","full_name":"Murphy, Timothy","first_name":"Timothy"},{"last_name":"Gilsdorf","first_name":"Janet","full_name":"Gilsdorf, Janet"},{"last_name":"Mayer","full_name":"Mayer, Leonard","first_name":"Leonard"},{"full_name":"Wang, Xin","first_name":"Xin","last_name":"Wang"}]},{"citation":{"short":"G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, IEEE, 2016.","ieee":"G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm,” presented at the IEEE RSJ International Conference on Intelligent Robots and Systems IROS , Daejeon, Korea, 2016, vol. 2016–November.","apa":"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","ista":"Martius GS, Hostettler R, Knoll A, Der R. 2016. Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm. IEEE RSJ International Conference on Intelligent Robots and Systems IROS vol. 2016–November, 7759138.","chicago":"Martius, Georg S, Raphael Hostettler, Alois Knoll, and Ralf Der. “Compliant Control for Soft Robots: Emergent Behavior of a Tendon Driven Anthropomorphic Arm,” Vol. 2016–November. IEEE, 2016. https://doi.org/10.1109/IROS.2016.7759138.","mla":"Martius, Georg S., et al. Compliant Control for Soft Robots: Emergent Behavior of a Tendon Driven Anthropomorphic Arm. Vol. 2016–November, 7759138, IEEE, 2016, doi:10.1109/IROS.2016.7759138.","ama":"Martius GS, Hostettler R, Knoll A, Der R. Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm. In: Vol 2016-November. IEEE; 2016. doi:10.1109/IROS.2016.7759138"},"oa_version":"None","status":"public","date_published":"2016-11-28T00:00:00Z","type":"conference","volume":"2016-November","quality_controlled":"1","acknowledgement":"RD thanks for the hospitality at the Max-Planck-Institute and for helpful discussions with Nihat Ay and Keyan Zahedi.","article_number":"7759138","month":"11","publisher":"IEEE","title":"Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm","doi":"10.1109/IROS.2016.7759138","date_updated":"2021-01-12T06:49:08Z","_id":"1214","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","abstract":[{"lang":"eng","text":"With the accelerated development of robot technologies, optimal control becomes one of the central themes of research. In traditional approaches, the controller, by its internal functionality, finds appropriate actions on the basis of the history of sensor values, guided by the goals, intentions, objectives, learning schemes, and so forth. While very successful with classical robots, these methods run into severe difficulties when applied to soft robots, a new field of robotics with large interest for human-robot interaction. We claim that a novel controller paradigm opens new perspective for this field. This paper applies a recently developed neuro controller with differential extrinsic synaptic plasticity to a muscle-tendon driven arm-shoulder system from the Myorobotics toolkit. In the experiments, we observe a vast variety of self-organized behavior patterns: when left alone, the arm realizes pseudo-random sequences of different poses. By applying physical forces, the system can be entrained into definite motion patterns like wiping a table. Most interestingly, after attaching an object, the controller gets in a functional resonance with the object's internal dynamics, starting to shake spontaneously bottles half-filled with water or sensitively driving an attached pendulum into a circular mode. When attached to the crank of a wheel the neural system independently develops to rotate it. In this way, the robot discovers affordances of objects its body is interacting with."}],"day":"28","publist_id":"6121","publication_status":"published","date_created":"2018-12-11T11:50:45Z","conference":{"location":"Daejeon, Korea","end_date":"2016-09-14","start_date":"2016-09-09","name":"IEEE RSJ International Conference on Intelligent Robots and Systems IROS "},"author":[{"full_name":"Martius, Georg S","id":"3A276B68-F248-11E8-B48F-1D18A9856A87","first_name":"Georg S","last_name":"Martius"},{"first_name":"Raphael","full_name":"Hostettler, Raphael","last_name":"Hostettler"},{"last_name":"Knoll","full_name":"Knoll, Alois","first_name":"Alois"},{"last_name":"Der","first_name":"Ralf","full_name":"Der, Ralf"}],"department":[{"_id":"ChLa"},{"_id":"GaTk"}],"year":"2016","language":[{"iso":"eng"}],"scopus_import":1},{"year":"2015","language":[{"iso":"eng"}],"department":[{"_id":"ToHe"},{"_id":"GaTk"}],"page":"8148 - 8153","author":[{"last_name":"Ruess","orcid":"0000-0003-1615-3282","full_name":"Ruess, Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","first_name":"Jakob"},{"full_name":"Parise, Francesca","first_name":"Francesca","last_name":"Parise"},{"last_name":"Milias Argeitis","full_name":"Milias Argeitis, Andreas","first_name":"Andreas"},{"last_name":"Khammash","full_name":"Khammash, Mustafa","first_name":"Mustafa"},{"first_name":"John","full_name":"Lygeros, John","last_name":"Lygeros"}],"publication_status":"published","publist_id":"5633","day":"30","date_created":"2018-12-11T11:52:36Z","publication":"PNAS","publisher":"National Academy of Sciences","date_updated":"2021-01-12T06:51:27Z","doi":"10.1073/pnas.1423947112","quality_controlled":"1","oa":1,"month":"06","volume":112,"date_published":"2015-06-30T00:00:00Z","type":"journal_article","project":[{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"external_id":{"pmid":["26085136"]},"oa_version":"Submitted Version","scopus_import":1,"issue":"26","_id":"1538","title":"Iterative experiment design guides the characterization of a light-inducible gene expression circuit","abstract":[{"text":"Systems biology rests on the idea that biological complexity can be better unraveled through the interplay of modeling and experimentation. However, the success of this approach depends critically on the informativeness of the chosen experiments, which is usually unknown a priori. Here, we propose a systematic scheme based on iterations of optimal experiment design, flow cytometry experiments, and Bayesian parameter inference to guide the discovery process in the case of stochastic biochemical reaction networks. To illustrate the benefit of our methodology, we apply it to the characterization of an engineered light-inducible gene expression circuit in yeast and compare the performance of the resulting model with models identified from nonoptimal experiments. In particular, we compare the parameter posterior distributions and the precision to which the outcome of future experiments can be predicted. Moreover, we illustrate how the identified stochastic model can be used to determine light induction patterns that make either the average amount of protein or the variability in a population of cells follow a desired profile. Our results show that optimal experiment design allows one to derive models that are accurate enough to precisely predict and regulate the protein expression in heterogeneous cell populations over extended periods of time.","lang":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","acknowledgement":"J.R., F.P., and J.L. acknowledge support from the European Commission under the Network of Excellence HYCON2 (highly-complex and networked control systems) and SystemsX.ch under the SignalX Project. J.R. acknowledges support from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013 under REA (Research Executive Agency) Grant 291734. M.K. acknowledges support from Human Frontier Science Program Grant RP0061/2011 (www.hfsp.org). ","pmid":1,"ec_funded":1,"status":"public","main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4491780/","open_access":"1"}],"intvolume":" 112","citation":{"apa":"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","ista":"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. 112(26), 8148–8153.","short":"J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, J. Lygeros, PNAS 112 (2015) 8148–8153.","ieee":"J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, and J. Lygeros, “Iterative experiment design guides the characterization of a light-inducible gene expression circuit,” PNAS, vol. 112, no. 26. National Academy of Sciences, pp. 8148–8153, 2015.","mla":"Ruess, Jakob, et al. “Iterative Experiment Design Guides the Characterization of a Light-Inducible Gene Expression Circuit.” PNAS, vol. 112, no. 26, National Academy of Sciences, 2015, pp. 8148–53, doi:10.1073/pnas.1423947112.","ama":"Ruess J, Parise F, Milias Argeitis A, Khammash M, Lygeros J. Iterative experiment design guides the characterization of a light-inducible gene expression circuit. PNAS. 2015;112(26):8148-8153. doi:10.1073/pnas.1423947112","chicago":"Ruess, Jakob, Francesca Parise, Andreas Milias Argeitis, Mustafa Khammash, and John Lygeros. “Iterative Experiment Design Guides the Characterization of a Light-Inducible Gene Expression Circuit.” PNAS. National Academy of Sciences, 2015. https://doi.org/10.1073/pnas.1423947112."}},{"scopus_import":1,"file_date_updated":"2020-07-14T12:45:01Z","issue":"24","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"lang":"eng","text":"Many stochastic models of biochemical reaction networks contain some chemical species for which the number of molecules that are present in the system can only be finite (for instance due to conservation laws), but also other species that can be present in arbitrarily large amounts. The prime example of such networks are models of gene expression, which typically contain a small and finite number of possible states for the promoter but an infinite number of possible states for the amount of mRNA and protein. One of the main approaches to analyze such models is through the use of equations for the time evolution of moments of the chemical species. Recently, a new approach based on conditional moments of the species with infinite state space given all the different possible states of the finite species has been proposed. It was argued that this approach allows one to capture more details about the full underlying probability distribution with a smaller number of equations. Here, I show that the result that less moments provide more information can only stem from an unnecessarily complicated description of the system in the classical formulation. The foundation of this argument will be the derivation of moment equations that describe the complete probability distribution over the finite state space but only low-order moments over the infinite state space. I will show that the number of equations that is needed is always less than what was previously claimed and always less than the number of conditional moment equations up to the same order. To support these arguments, a symbolic algorithm is provided that can be used to derive minimal systems of unconditional moment equations for models with partially finite state space. "}],"title":"Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space","_id":"1539","status":"public","ec_funded":1,"citation":{"chicago":"Ruess, Jakob. “Minimal Moment Equations for Stochastic Models of Biochemical Reaction Networks with Partially Finite State Space.” Journal of Chemical Physics. American Institute of Physics, 2015. https://doi.org/10.1063/1.4937937.","ama":"Ruess J. Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. Journal of Chemical Physics. 2015;143(24). doi:10.1063/1.4937937","mla":"Ruess, Jakob. “Minimal Moment Equations for Stochastic Models of Biochemical Reaction Networks with Partially Finite State Space.” Journal of Chemical Physics, vol. 143, no. 24, 244103, American Institute of Physics, 2015, doi:10.1063/1.4937937.","ieee":"J. Ruess, “Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space,” Journal of Chemical Physics, vol. 143, no. 24. American Institute of Physics, 2015.","short":"J. Ruess, Journal of Chemical Physics 143 (2015).","ista":"Ruess J. 2015. Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. Journal of Chemical Physics. 143(24), 244103.","apa":"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"},"intvolume":" 143","has_accepted_license":"1","ddc":["000"],"year":"2015","language":[{"iso":"eng"}],"department":[{"_id":"ToHe"},{"_id":"GaTk"}],"author":[{"orcid":"0000-0003-1615-3282","last_name":"Ruess","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","first_name":"Jakob","full_name":"Ruess, Jakob"}],"pubrep_id":"593","date_created":"2018-12-11T11:52:36Z","day":"22","publication_status":"published","publist_id":"5632","publisher":"American Institute of Physics","date_updated":"2021-01-12T06:51:28Z","doi":"10.1063/1.4937937","publication":"Journal of Chemical Physics","month":"12","file":[{"relation":"main_file","creator":"system","content_type":"application/pdf","date_created":"2018-12-12T10:07:43Z","file_id":"4641","date_updated":"2020-07-14T12:45:01Z","file_size":605355,"access_level":"open_access","file_name":"IST-2016-593-v1+1_Minimal_moment_equations.pdf","checksum":"838657118ae286463a2b7737319f35ce"}],"oa":1,"quality_controlled":"1","article_number":"244103","type":"journal_article","project":[{"call_identifier":"FP7","name":"Quantitative Reactive Modeling","_id":"25EE3708-B435-11E9-9278-68D0E5697425","grant_number":"267989"},{"name":"Rigorous Systems Engineering","call_identifier":"FWF","grant_number":"S 11407_N23","_id":"25832EC2-B435-11E9-9278-68D0E5697425"},{"call_identifier":"FWF","name":"The Wittgenstein Prize","_id":"25F42A32-B435-11E9-9278-68D0E5697425","grant_number":"Z211"},{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734"}],"date_published":"2015-12-22T00:00:00Z","volume":143,"oa_version":"Published Version"},{"ec_funded":1,"status":"public","has_accepted_license":"1","citation":{"ieee":"M. Gilson, C. Savin, and F. Zenke, “Editorial: Emergent neural computation from the interaction of different forms of plasticity,” Frontiers in Computational Neuroscience, vol. 9, no. 11. Frontiers Research Foundation, 2015.","short":"M. Gilson, C. Savin, F. Zenke, Frontiers in Computational Neuroscience 9 (2015).","apa":"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","ista":"Gilson M, Savin C, Zenke F. 2015. Editorial: Emergent neural computation from the interaction of different forms of plasticity. Frontiers in Computational Neuroscience. 9(11), 145.","chicago":"Gilson, Matthieu, Cristina Savin, and Friedemann Zenke. “Editorial: Emergent Neural Computation from the Interaction of Different Forms of Plasticity.” Frontiers in Computational Neuroscience. Frontiers Research Foundation, 2015. https://doi.org/10.3389/fncom.2015.00145.","mla":"Gilson, Matthieu, et al. “Editorial: Emergent Neural Computation from the Interaction of Different Forms of Plasticity.” Frontiers in Computational Neuroscience, vol. 9, no. 11, 145, Frontiers Research Foundation, 2015, doi:10.3389/fncom.2015.00145.","ama":"Gilson M, Savin C, Zenke F. Editorial: Emergent neural computation from the interaction of different forms of plasticity. Frontiers in Computational Neuroscience. 2015;9(11). doi:10.3389/fncom.2015.00145"},"intvolume":" 9","_id":"1564","title":"Editorial: Emergent neural computation from the interaction of different forms of plasticity","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","issue":"11","scopus_import":1,"file_date_updated":"2020-07-14T12:45:02Z","volume":9,"date_published":"2015-11-30T00:00:00Z","project":[{"name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734"}],"type":"journal_article","oa_version":"Published Version","publication":"Frontiers in Computational Neuroscience","date_updated":"2021-01-12T06:51:37Z","publisher":"Frontiers Research Foundation","doi":"10.3389/fncom.2015.00145","article_number":"145","oa":1,"quality_controlled":"1","file":[{"content_type":"application/pdf","file_id":"4927","date_created":"2018-12-12T10:12:09Z","creator":"system","relation":"main_file","file_name":"IST-2016-479-v1+1_fncom-09-00145.pdf","access_level":"open_access","checksum":"cea73b6d3ef1579f32da10b82f4de4fd","date_updated":"2020-07-14T12:45:02Z","file_size":187038}],"month":"11","pubrep_id":"479","author":[{"full_name":"Gilson, Matthieu","first_name":"Matthieu","last_name":"Gilson"},{"full_name":"Savin, Cristina","first_name":"Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87","last_name":"Savin"},{"last_name":"Zenke","full_name":"Zenke, Friedemann","first_name":"Friedemann"}],"publication_status":"published","publist_id":"5607","day":"30","date_created":"2018-12-11T11:52:45Z","ddc":["570"],"language":[{"iso":"eng"}],"year":"2015","department":[{"_id":"GaTk"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"}},{"issue":"45","scopus_import":1,"citation":{"ieee":"R. Der and G. S. Martius, “Novel plasticity rule can explain the development of sensorimotor intelligence,” PNAS, vol. 112, no. 45. National Academy of Sciences, pp. E6224–E6232, 2015.","short":"R. Der, G.S. Martius, PNAS 112 (2015) E6224–E6232.","ista":"Der R, Martius GS. 2015. Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS. 112(45), E6224–E6232.","apa":"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","chicago":"Der, Ralf, and Georg S Martius. “Novel Plasticity Rule Can Explain the Development of Sensorimotor Intelligence.” PNAS. National Academy of Sciences, 2015. https://doi.org/10.1073/pnas.1508400112.","ama":"Der R, Martius GS. Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS. 2015;112(45):E6224-E6232. doi:10.1073/pnas.1508400112","mla":"Der, Ralf, and Georg S. Martius. “Novel Plasticity Rule Can Explain the Development of Sensorimotor Intelligence.” PNAS, vol. 112, no. 45, National Academy of Sciences, 2015, pp. E6224–32, doi:10.1073/pnas.1508400112."},"intvolume":" 112","main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4653169/","open_access":"1"}],"status":"public","ec_funded":1,"pmid":1,"title":"Novel plasticity rule can explain the development of sensorimotor intelligence","_id":"1570","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"lang":"eng","text":"Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience. In particular, self-organized behavioral development provides more questions than answers. Are there special functional units for curiosity, motivation, and creativity? This paper argues that these features can be grounded in synaptic plasticity itself, without requiring any higher-level constructs. We propose differential extrinsic plasticity (DEP) as a new synaptic rule for self-learning systems and apply it to a number of complex robotic systems as a test case. Without specifying any purpose or goal, seemingly purposeful and adaptive rhythmic behavior is developed, displaying a certain level of sensorimotor intelligence. These surprising results require no systemspecific modifications of the DEP rule. They rather arise from the underlying mechanism of spontaneous symmetry breaking,which is due to the tight brain body environment coupling. The new synaptic rule is biologically plausible and would be an interesting target for neurobiological investigation. We also argue that this neuronal mechanism may have been a catalyst in natural evolution."}],"day":"10","publist_id":"5601","publication_status":"published","date_created":"2018-12-11T11:52:47Z","page":"E6224 - E6232","author":[{"last_name":"Der","first_name":"Ralf","full_name":"Der, Ralf"},{"first_name":"Georg S","id":"3A276B68-F248-11E8-B48F-1D18A9856A87","full_name":"Martius, Georg S","last_name":"Martius"}],"department":[{"_id":"ChLa"},{"_id":"GaTk"}],"language":[{"iso":"eng"}],"year":"2015","external_id":{"pmid":["26504200"]},"oa_version":"Submitted Version","type":"journal_article","date_published":"2015-11-10T00:00:00Z","project":[{"grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"volume":112,"quality_controlled":"1","oa":1,"month":"11","date_updated":"2021-01-12T06:51:40Z","doi":"10.1073/pnas.1508400112","publisher":"National Academy of Sciences","publication":"PNAS"}]