[{"intvolume":"        17","oa":1,"corr_author":"1","language":[{"iso":"eng"}],"acknowledged_ssus":[{"_id":"ScienComp"}],"year":"2021","acknowledgement":"Computational resources for the study were provided by the Institute of Science and Technology, Austria.\r\nKB received funding from the Scientific Grant Agency of the Slovak Republic under the Grants Nos. 1/0755/19 and 1/0521/20.","arxiv":1,"day":"01","volume":17,"publication":"PLoS Computational Biology","author":[{"first_name":"Katarína","orcid":"0000-0002-7214-0171","full_name":"Bod'ová, Katarína","last_name":"Bod'ová","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Eniko","full_name":"Szep, Eniko","id":"485BB5A4-F248-11E8-B48F-1D18A9856A87","last_name":"Szep"},{"id":"4880FE40-F248-11E8-B48F-1D18A9856A87","last_name":"Barton","full_name":"Barton, Nicholas H","first_name":"Nicholas H","orcid":"0000-0002-8548-5240"}],"article_type":"original","title":"Dynamic maximum entropy provides accurate approximation of structured population dynamics","quality_controlled":"1","file":[{"success":1,"checksum":"dcd185d4f7e0acee25edf1d6537f447e","file_id":"11383","access_level":"open_access","relation":"main_file","creator":"dernst","content_type":"application/pdf","date_updated":"2022-05-16T08:53:11Z","date_created":"2022-05-16T08:53:11Z","file_name":"2021_PLOsComBio_Bodova.pdf","file_size":2299486}],"scopus_import":"1","publication_identifier":{"issn":["1553-734X"],"eissn":["1553-7358"]},"publisher":"Public Library of Science","file_date_updated":"2022-05-16T08:53:11Z","_id":"10535","date_created":"2021-12-12T23:01:27Z","department":[{"_id":"NiBa"},{"_id":"GaTk"}],"article_processing_charge":"No","has_accepted_license":"1","article_number":"e1009661","pmid":1,"issue":"12","publication_status":"published","date_updated":"2024-10-09T21:01:16Z","fulldoi":"https://doi.org/10.1371/journal.pcbi.1009661","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"journal_article","external_id":{"arxiv":["2102.03669"],"pmid":["34851948"]},"oa_version":"Published Version","abstract":[{"text":"Realistic models of biological processes typically involve interacting components on multiple scales, driven by changing environment and inherent stochasticity. Such models are often analytically and numerically intractable. We revisit a dynamic maximum entropy method that combines a static maximum entropy with a quasi-stationary approximation. This allows us to reduce stochastic non-equilibrium dynamics expressed by the Fokker-Planck equation to a simpler low-dimensional deterministic dynamics, without the need to track microscopic details. Although the method has been previously applied to a few (rather complicated) applications in population genetics, our main goal here is to explain and to better understand how the method works. We demonstrate the usefulness of the method for two widely studied stochastic problems, highlighting its accuracy in capturing important macroscopic quantities even in rapidly changing non-stationary conditions. For the Ornstein-Uhlenbeck process, the method recovers the exact dynamics whilst for a stochastic island model with migration from other habitats, the approximation retains high macroscopic accuracy under a wide range of scenarios in a dynamic environment.","lang":"eng"}],"month":"12","doi":"10.1371/journal.pcbi.1009661","status":"public","citation":{"short":"K. Bodova, E. Szep, N.H. Barton, PLoS Computational Biology 17 (2021).","ieee":"K. Bodova, E. Szep, and N. H. Barton, “Dynamic maximum entropy provides accurate approximation of structured population dynamics,” <i>PLoS Computational Biology</i>, vol. 17, no. 12. Public Library of Science, 2021.","ista":"Bodova K, Szep E, Barton NH. 2021. Dynamic maximum entropy provides accurate approximation of structured population dynamics. PLoS Computational Biology. 17(12), e1009661.","chicago":"Bodova, Katarina, Eniko Szep, and Nicholas H Barton. “Dynamic Maximum Entropy Provides Accurate Approximation of Structured Population Dynamics.” <i>PLoS Computational Biology</i>. Public Library of Science, 2021. <a href=\"https://doi.org/10.1371/journal.pcbi.1009661\">https://doi.org/10.1371/journal.pcbi.1009661</a>.","ama":"Bodova K, Szep E, Barton NH. Dynamic maximum entropy provides accurate approximation of structured population dynamics. <i>PLoS Computational Biology</i>. 2021;17(12). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1009661\">10.1371/journal.pcbi.1009661</a>","mla":"Bodova, Katarina, et al. “Dynamic Maximum Entropy Provides Accurate Approximation of Structured Population Dynamics.” <i>PLoS Computational Biology</i>, vol. 17, no. 12, e1009661, Public Library of Science, 2021, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1009661\">10.1371/journal.pcbi.1009661</a>.","apa":"Bodova, K., Szep, E., &#38; Barton, N. H. (2021). Dynamic maximum entropy provides accurate approximation of structured population dynamics. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1009661\">https://doi.org/10.1371/journal.pcbi.1009661</a>"},"ddc":["570"],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png"},"date_published":"2021-12-01T00:00:00Z"},{"keyword":["Genetics and Molecular Biology"],"fulldoi":"https://doi.org/10.7554/elife.65993","date_updated":"2025-06-12T06:36:17Z","publication_status":"published","ec_funded":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"GaTk"},{"_id":"CaGu"}],"has_accepted_license":"1","article_processing_charge":"Yes","article_number":"e65993","pmid":1,"isi":1,"doi":"10.7554/elife.65993","project":[{"_id":"2517526A-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"The Systems Biology of Transcriptional Read-Through in Bacteria: from Synthetic Networks to Genomic Studies","grant_number":"628377"},{"grant_number":"I03901","name":"Cybergenetic circuits to test composability of gene networks","call_identifier":"FWF","_id":"268BFA92-B435-11E9-9278-68D0E5697425"}],"status":"public","ddc":["570"],"citation":{"ama":"Nagy-Staron AA, Tomasek K, Caruso Carter C, et al. Local genetic context shapes the function of a gene regulatory network. <i>eLife</i>. 2021;10. doi:<a href=\"https://doi.org/10.7554/elife.65993\">10.7554/elife.65993</a>","chicago":"Nagy-Staron, Anna A, Kathrin Tomasek, Caroline Caruso Carter, Elisabeth Sonnleitner, Bor Kavcic, Tiago Paixão, and Calin C Guet. “Local Genetic Context Shapes the Function of a Gene Regulatory Network.” <i>ELife</i>. eLife Sciences Publications, 2021. <a href=\"https://doi.org/10.7554/elife.65993\">https://doi.org/10.7554/elife.65993</a>.","ista":"Nagy-Staron AA, Tomasek K, Caruso Carter C, Sonnleitner E, Kavcic B, Paixão T, Guet CC. 2021. Local genetic context shapes the function of a gene regulatory network. eLife. 10, e65993.","ieee":"A. A. Nagy-Staron <i>et al.</i>, “Local genetic context shapes the function of a gene regulatory network,” <i>eLife</i>, vol. 10. eLife Sciences Publications, 2021.","short":"A.A. Nagy-Staron, K. Tomasek, C. Caruso Carter, E. Sonnleitner, B. Kavcic, T. Paixão, C.C. Guet, ELife 10 (2021).","mla":"Nagy-Staron, Anna A., et al. “Local Genetic Context Shapes the Function of a Gene Regulatory Network.” <i>ELife</i>, vol. 10, e65993, eLife Sciences Publications, 2021, doi:<a href=\"https://doi.org/10.7554/elife.65993\">10.7554/elife.65993</a>.","apa":"Nagy-Staron, A. A., Tomasek, K., Caruso Carter, C., Sonnleitner, E., Kavcic, B., Paixão, T., &#38; Guet, C. C. (2021). Local genetic context shapes the function of a gene regulatory network. <i>ELife</i>. eLife Sciences Publications. <a href=\"https://doi.org/10.7554/elife.65993\">https://doi.org/10.7554/elife.65993</a>"},"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png"},"date_published":"2021-03-08T00:00:00Z","external_id":{"pmid":["33683203"],"isi":["000631050900001"]},"type":"journal_article","oa_version":"Published Version","related_material":{"record":[{"relation":"research_data","status":"public","id":"8951"}]},"abstract":[{"text":"Gene expression levels are influenced by multiple coexisting molecular mechanisms. Some of these interactions such as those of transcription factors and promoters have been studied extensively. However, predicting phenotypes of gene regulatory networks (GRNs) remains a major challenge. Here, we use a well-defined synthetic GRN to study in Escherichia coli how network phenotypes depend on local genetic context, i.e. the genetic neighborhood of a transcription factor and its relative position. We show that one GRN with fixed topology can display not only quantitatively but also qualitatively different phenotypes, depending solely on the local genetic context of its components. Transcriptional read-through is the main molecular mechanism that places one transcriptional unit (TU) within two separate regulons without the need for complex regulatory sequences. We propose that relative order of individual TUs, with its potential for combinatorial complexity, plays an important role in shaping phenotypes of GRNs.","lang":"eng"}],"month":"03","day":"08","acknowledgement":"We thank J Bollback, L Hurst, M Lagator, C Nizak, O Rivoire, M Savageau, G Tkacik, and B Vicozo\r\nfor helpful discussions; A Dolinar and A Greshnova for technical assistance; T Bollenbach for supplying the strain JW0336; C Rusnac, and members of the Guet lab for comments. The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA grant agreement n˚\r\n628377 (ANS) and an Austrian Science Fund (FWF) grant n˚ I 3901-B32 (CCG).","publication":"eLife","volume":10,"intvolume":"        10","oa":1,"language":[{"iso":"eng"}],"corr_author":"1","year":"2021","date_created":"2021-03-23T10:11:46Z","_id":"9283","file_date_updated":"2021-03-23T10:12:58Z","author":[{"orcid":"0000-0002-1391-8377","first_name":"Anna A","full_name":"Nagy-Staron, Anna A","last_name":"Nagy-Staron","id":"3ABC5BA6-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0003-3768-877X","first_name":"Kathrin","full_name":"Tomasek, Kathrin","last_name":"Tomasek","id":"3AEC8556-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Caruso Carter","full_name":"Caruso Carter, Caroline","first_name":"Caroline"},{"last_name":"Sonnleitner","full_name":"Sonnleitner, Elisabeth","first_name":"Elisabeth"},{"orcid":"0000-0001-6041-254X","first_name":"Bor","full_name":"Kavcic, Bor","id":"350F91D2-F248-11E8-B48F-1D18A9856A87","last_name":"Kavcic"},{"first_name":"Tiago","last_name":"Paixão","full_name":"Paixão, Tiago"},{"orcid":"0000-0001-6220-2052","first_name":"Calin C","last_name":"Guet","full_name":"Guet, Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87"}],"article_type":"original","quality_controlled":"1","title":"Local genetic context shapes the function of a gene regulatory network","scopus_import":"1","file":[{"file_id":"9284","access_level":"open_access","creator":"bkavcic","relation":"main_file","success":1,"checksum":"3c2f44058c2dd45a5a1027f09d263f8e","content_type":"application/pdf","date_updated":"2021-03-23T10:12:58Z","date_created":"2021-03-23T10:12:58Z","file_name":"elife-65993-v2.pdf","file_size":1390469}],"publisher":"eLife Sciences Publications","publication_identifier":{"issn":["2050-084X"]}},{"type":"journal_article","external_id":{"pmid":["33857170"],"isi":["000641474900072"]},"oa_version":"Published Version","abstract":[{"text":"A central goal in systems neuroscience is to understand the functions performed by neural circuits. Previous top-down models addressed this question by comparing the behaviour of an ideal model circuit, optimised to perform a given function, with neural recordings. However, this requires guessing in advance what function is being performed, which may not be possible for many neural systems. To address this, we propose an inverse reinforcement learning (RL) framework for inferring the function performed by a neural network from data. We assume that the responses of each neuron in a network are optimised so as to drive the network towards ‘rewarded’ states, that are desirable for performing a given function. We then show how one can use inverse RL to infer the reward function optimised by the network from observing its responses. This inferred reward function can be used to predict how the neural network should adapt its dynamics to perform the same function when the external environment or network structure changes. This could lead to theoretical predictions about how neural network dynamics adapt to deal with cell death and/or varying sensory stimulus statistics.","lang":"eng"}],"month":"04","isi":1,"doi":"10.1371/journal.pone.0248940","status":"public","ddc":["570"],"citation":{"mla":"Chalk, Matthew J., et al. “Inferring the Function Performed by a Recurrent Neural Network.” <i>PLoS ONE</i>, vol. 16, no. 4, e0248940, Public Library of Science, 2021, doi:<a href=\"https://doi.org/10.1371/journal.pone.0248940\">10.1371/journal.pone.0248940</a>.","apa":"Chalk, M. J., Tkačik, G., &#38; Marre, O. (2021). Inferring the function performed by a recurrent neural network. <i>PLoS ONE</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pone.0248940\">https://doi.org/10.1371/journal.pone.0248940</a>","ieee":"M. J. Chalk, G. Tkačik, and O. Marre, “Inferring the function performed by a recurrent neural network,” <i>PLoS ONE</i>, vol. 16, no. 4. Public Library of Science, 2021.","short":"M.J. Chalk, G. Tkačik, O. Marre, PLoS ONE 16 (2021).","ista":"Chalk MJ, Tkačik G, Marre O. 2021. Inferring the function performed by a recurrent neural network. PLoS ONE. 16(4), e0248940.","ama":"Chalk MJ, Tkačik G, Marre O. Inferring the function performed by a recurrent neural network. <i>PLoS ONE</i>. 2021;16(4). doi:<a href=\"https://doi.org/10.1371/journal.pone.0248940\">10.1371/journal.pone.0248940</a>","chicago":"Chalk, Matthew J, Gašper Tkačik, and Olivier Marre. “Inferring the Function Performed by a Recurrent Neural Network.” <i>PLoS ONE</i>. Public Library of Science, 2021. <a href=\"https://doi.org/10.1371/journal.pone.0248940\">https://doi.org/10.1371/journal.pone.0248940</a>."},"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png"},"date_published":"2021-04-15T00:00:00Z","department":[{"_id":"GaTk"}],"has_accepted_license":"1","article_processing_charge":"No","article_number":"e0248940","pmid":1,"fulldoi":"https://doi.org/10.1371/journal.pone.0248940","date_updated":"2026-04-02T13:58:56Z","issue":"4","publication_status":"published","user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","author":[{"first_name":"Matthew J","orcid":"0000-0001-7782-4436","id":"2BAAC544-F248-11E8-B48F-1D18A9856A87","full_name":"Chalk, Matthew J","last_name":"Chalk"},{"last_name":"Tkačik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","orcid":"0000-0002-6699-1455","first_name":"Gašper"},{"first_name":"Olivier","last_name":"Marre","full_name":"Marre, Olivier"}],"article_type":"original","quality_controlled":"1","title":"Inferring the function performed by a recurrent neural network","scopus_import":"1","file":[{"file_size":2768282,"date_created":"2021-05-04T13:22:19Z","file_name":"2021_pone_Chalk.pdf","date_updated":"2021-05-04T13:22:19Z","content_type":"application/pdf","relation":"main_file","creator":"kschuh","access_level":"open_access","file_id":"9371","checksum":"c52da133850307d2031f552d998f00e8","success":1}],"publisher":"Public Library of Science","publication_identifier":{"eissn":["1932-6203"]},"_id":"9362","date_created":"2021-05-02T22:01:28Z","file_date_updated":"2021-05-04T13:22:19Z","intvolume":"        16","oa":1,"language":[{"iso":"eng"}],"year":"2021","day":"15","acknowledgement":"The authors would like to thank Ulisse Ferrari for useful discussions and feedback.","volume":16,"publication":"PLoS ONE"},{"acknowledgement":"We thank D. Kastner and T. Münch for generously providing figures from their work. We also thank V. Jayaraman, M. Noorman, T. Ma, and K. Krishnamurthy for useful discussions and feedback on the manuscript. W.F.M. was funded by the European Union’s Horizon 2020 Research and Innovation Programme under Marie Skłodowska-Curie Grant Agreement No. 754411. A.M.H. was supported by the Howard Hughes Medical Institute.","day":"20","publication":"Nature Neuroscience","volume":24,"oa":1,"intvolume":"        24","year":"2021","language":[{"iso":"eng"}],"date_created":"2021-05-30T22:01:24Z","_id":"9439","page":"998-1009","title":"Efficient and adaptive sensory codes","quality_controlled":"1","article_type":"original","author":[{"first_name":"Wiktor F","id":"358A453A-F248-11E8-B48F-1D18A9856A87","last_name":"Mlynarski","full_name":"Mlynarski, Wiktor F"},{"last_name":"Hermundstad","full_name":"Hermundstad, Ann M.","first_name":"Ann M."}],"publication_identifier":{"issn":["1097-6256"],"eissn":["1546-1726"]},"publisher":"Springer Nature","scopus_import":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","ec_funded":1,"publication_status":"published","date_updated":"2025-06-12T06:41:38Z","fulldoi":"https://doi.org/10.1038/s41593-021-00846-0","article_processing_charge":"No","department":[{"_id":"GaTk"}],"pmid":1,"status":"public","project":[{"grant_number":"754411","name":"ISTplus - Postdoctoral Fellowships","_id":"260C2330-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.1101/669200 "}],"doi":"10.1038/s41593-021-00846-0","isi":1,"date_published":"2021-05-20T00:00:00Z","citation":{"apa":"Mlynarski, W. F., &#38; Hermundstad, A. M. (2021). Efficient and adaptive sensory codes. <i>Nature Neuroscience</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41593-021-00846-0\">https://doi.org/10.1038/s41593-021-00846-0</a>","mla":"Mlynarski, Wiktor F., and Ann M. Hermundstad. “Efficient and Adaptive Sensory Codes.” <i>Nature Neuroscience</i>, vol. 24, Springer Nature, 2021, pp. 998–1009, doi:<a href=\"https://doi.org/10.1038/s41593-021-00846-0\">10.1038/s41593-021-00846-0</a>.","ama":"Mlynarski WF, Hermundstad AM. Efficient and adaptive sensory codes. <i>Nature Neuroscience</i>. 2021;24:998-1009. doi:<a href=\"https://doi.org/10.1038/s41593-021-00846-0\">10.1038/s41593-021-00846-0</a>","chicago":"Mlynarski, Wiktor F, and Ann M. Hermundstad. “Efficient and Adaptive Sensory Codes.” <i>Nature Neuroscience</i>. Springer Nature, 2021. <a href=\"https://doi.org/10.1038/s41593-021-00846-0\">https://doi.org/10.1038/s41593-021-00846-0</a>.","ista":"Mlynarski WF, Hermundstad AM. 2021. Efficient and adaptive sensory codes. Nature Neuroscience. 24, 998–1009.","ieee":"W. F. Mlynarski and A. M. Hermundstad, “Efficient and adaptive sensory codes,” <i>Nature Neuroscience</i>, vol. 24. Springer Nature, pp. 998–1009, 2021.","short":"W.F. Mlynarski, A.M. Hermundstad, Nature Neuroscience 24 (2021) 998–1009."},"oa_version":"Preprint","type":"journal_article","external_id":{"pmid":["34017131"],"isi":["000652577300003"]},"month":"05","abstract":[{"lang":"eng","text":"The ability to adapt to changes in stimulus statistics is a hallmark of sensory systems. Here, we developed a theoretical framework that can account for the dynamics of adaptation from an information processing perspective. We use this framework to optimize and analyze adaptive sensory codes, and we show that codes optimized for stationary environments can suffer from prolonged periods of poor performance when the environment changes. To mitigate the adversarial effects of these environmental changes, sensory systems must navigate tradeoffs between the ability to accurately encode incoming stimuli and the ability to rapidly detect and adapt to changes in the distribution of these stimuli. We derive families of codes that balance these objectives, and we demonstrate their close match to experimentally observed neural dynamics during mean and variance adaptation. Our results provide a unifying perspective on adaptation across a range of sensory systems, environments, and sensory tasks."}]},{"abstract":[{"text":"Attachment of adhesive molecules on cell culture surfaces to restrict cell adhesion to defined areas and shapes has been vital for the progress of in vitro research. In currently existing patterning methods, a combination of pattern properties such as stability, precision, specificity, high-throughput outcome, and spatiotemporal control is highly desirable but challenging to achieve. Here, we introduce a versatile and high-throughput covalent photoimmobilization technique, comprising a light-dose-dependent patterning step and a subsequent functionalization of the pattern via click chemistry. This two-step process is feasible on arbitrary surfaces and allows for generation of sustainable patterns and gradients. The method is validated in different biological systems by patterning adhesive ligands on cell-repellent surfaces, thereby constraining the growth and migration of cells to the designated areas. We then implement a sequential photopatterning approach by adding a second switchable patterning step, allowing for spatiotemporal control over two distinct surface patterns. As a proof of concept, we reconstruct the dynamics of the tip/stalk cell switch during angiogenesis. Our results show that the spatiotemporal control provided by our “sequential photopatterning” system is essential for mimicking dynamic biological processes and that our innovative approach has great potential for further applications in cell science.","lang":"eng"}],"month":"08","type":"journal_article","external_id":{"isi":["000683741400026"],"pmid":["34283577"]},"oa_version":"Published Version","ddc":["620","570"],"citation":{"ieee":"T. Zisis <i>et al.</i>, “Sequential and switchable patterning for studying cellular processes under spatiotemporal control,” <i>ACS Applied Materials and Interfaces</i>, vol. 13, no. 30. American Chemical Society, pp. 35545–35560, 2021.","short":"T. Zisis, J. Schwarz, M. Balles, M. Kretschmer, M. Nemethova, R.P. Chait, R. Hauschild, J. Lange, C.C. Guet, M.K. Sixt, S. Zahler, ACS Applied Materials and Interfaces 13 (2021) 35545–35560.","ama":"Zisis T, Schwarz J, Balles M, et al. Sequential and switchable patterning for studying cellular processes under spatiotemporal control. <i>ACS Applied Materials and Interfaces</i>. 2021;13(30):35545–35560. doi:<a href=\"https://doi.org/10.1021/acsami.1c09850\">10.1021/acsami.1c09850</a>","ista":"Zisis T, Schwarz J, Balles M, Kretschmer M, Nemethova M, Chait RP, Hauschild R, Lange J, Guet CC, Sixt MK, Zahler S. 2021. Sequential and switchable patterning for studying cellular processes under spatiotemporal control. ACS Applied Materials and Interfaces. 13(30), 35545–35560.","chicago":"Zisis, Themistoklis, Jan Schwarz, Miriam Balles, Maibritt Kretschmer, Maria Nemethova, Remy P Chait, Robert Hauschild, et al. “Sequential and Switchable Patterning for Studying Cellular Processes under Spatiotemporal Control.” <i>ACS Applied Materials and Interfaces</i>. American Chemical Society, 2021. <a href=\"https://doi.org/10.1021/acsami.1c09850\">https://doi.org/10.1021/acsami.1c09850</a>.","apa":"Zisis, T., Schwarz, J., Balles, M., Kretschmer, M., Nemethova, M., Chait, R. P., … Zahler, S. (2021). Sequential and switchable patterning for studying cellular processes under spatiotemporal control. <i>ACS Applied Materials and Interfaces</i>. American Chemical Society. <a href=\"https://doi.org/10.1021/acsami.1c09850\">https://doi.org/10.1021/acsami.1c09850</a>","mla":"Zisis, Themistoklis, et al. “Sequential and Switchable Patterning for Studying Cellular Processes under Spatiotemporal Control.” <i>ACS Applied Materials and Interfaces</i>, vol. 13, no. 30, American Chemical Society, 2021, pp. 35545–35560, doi:<a href=\"https://doi.org/10.1021/acsami.1c09850\">10.1021/acsami.1c09850</a>."},"tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)","image":"/images/cc_by_nc_nd.png"},"date_published":"2021-08-04T00:00:00Z","isi":1,"doi":"10.1021/acsami.1c09850","status":"public","project":[{"_id":"25FE9508-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","grant_number":"724373","name":"Cellular Navigation Along Spatial Gradients"}],"pmid":1,"department":[{"_id":"MiSi"},{"_id":"GaTk"},{"_id":"Bio"},{"_id":"CaGu"}],"has_accepted_license":"1","article_processing_charge":"Yes (in subscription journal)","date_updated":"2025-07-10T12:02:02Z","fulldoi":"https://doi.org/10.1021/acsami.1c09850","publication_status":"published","issue":"30","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","ec_funded":1,"scopus_import":"1","file":[{"file_size":7123293,"file_name":"2021_ACSAppliedMaterialsAndInterfaces_Zisis.pdf","date_updated":"2021-08-09T09:44:03Z","date_created":"2021-08-09T09:44:03Z","content_type":"application/pdf","creator":"asandaue","relation":"main_file","access_level":"open_access","file_id":"9833","checksum":"b043a91d9f9200e467b970b692687ed3","success":1}],"publisher":"American Chemical Society","publication_identifier":{"eissn":["1944-8252"],"issn":["1944-8244"]},"article_type":"original","author":[{"first_name":"Themistoklis","last_name":"Zisis","full_name":"Zisis, Themistoklis"},{"full_name":"Schwarz, Jan","last_name":"Schwarz","id":"346C1EC6-F248-11E8-B48F-1D18A9856A87","first_name":"Jan"},{"first_name":"Miriam","full_name":"Balles, Miriam","last_name":"Balles"},{"first_name":"Maibritt","full_name":"Kretschmer, Maibritt","last_name":"Kretschmer"},{"first_name":"Maria","full_name":"Nemethova, Maria","last_name":"Nemethova","id":"34E27F1C-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Chait","id":"3464AE84-F248-11E8-B48F-1D18A9856A87","full_name":"Chait, Remy P","orcid":"0000-0003-0876-3187","first_name":"Remy P"},{"orcid":"0000-0001-9843-3522","first_name":"Robert","last_name":"Hauschild","full_name":"Hauschild, Robert","id":"4E01D6B4-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Janina","full_name":"Lange, Janina","last_name":"Lange"},{"orcid":"0000-0001-6220-2052","first_name":"Calin C","full_name":"Guet, Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","last_name":"Guet"},{"orcid":"0000-0002-4561-241X","first_name":"Michael K","last_name":"Sixt","id":"41E9FBEA-F248-11E8-B48F-1D18A9856A87","full_name":"Sixt, Michael K"},{"first_name":"Stefan","full_name":"Zahler, Stefan","last_name":"Zahler"}],"quality_controlled":"1","title":"Sequential and switchable patterning for studying cellular processes under spatiotemporal control","_id":"9822","date_created":"2021-08-08T22:01:28Z","page":"35545–35560","file_date_updated":"2021-08-09T09:44:03Z","language":[{"iso":"eng"}],"corr_author":"1","year":"2021","intvolume":"        13","oa":1,"publication":"ACS Applied Materials and Interfaces","volume":13,"day":"04","acknowledgement":"We would like to thank Charlott Leu for the production of our chromium wafers, Louise Ritter for her contribution of the IF stainings in Figure 4, Shokoufeh Teymouri for her help with the Bioinert coated slides, and finally Prof. Dr. Joachim Rädler for his valuable scientific guidance."},{"publication":"Neurocomputing","volume":461,"day":"13","acknowledgement":"LdA would like to acknowledge the financial support from MIUR-PRIN2017 WZFTZP and VALERE:VAnviteLli pEr la RicErca 2019. FL acknowledges support from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 754411. HJH would like to thank the Agencies CAPES and FUNCAP for financial support.","language":[{"iso":"eng"}],"year":"2021","intvolume":"       461","oa":1,"_id":"7463","date_created":"2020-02-06T16:09:14Z","page":"657-666","scopus_import":"1","publisher":"Elsevier","publication_identifier":{"issn":["0925-2312"],"eissn":["1872-8286"]},"author":[{"first_name":"Fabrizio","orcid":"0000-0003-2623-5249","last_name":"Lombardi","full_name":"Lombardi, Fabrizio","id":"A057D288-3E88-11E9-986D-0CF4E5697425"},{"last_name":"Shriki","full_name":"Shriki, Oren","first_name":"Oren"},{"first_name":"Hans J","full_name":"Herrmann, Hans J","last_name":"Herrmann"},{"last_name":"de Arcangelis","full_name":"de Arcangelis, Lucilla","first_name":"Lucilla"}],"article_type":"original","quality_controlled":"1","title":"Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches","date_updated":"2025-04-14T07:44:02Z","fulldoi":"https://doi.org/10.1016/j.neucom.2020.05.126","publication_status":"published","ec_funded":1,"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","department":[{"_id":"GaTk"}],"article_processing_charge":"No","citation":{"mla":"Lombardi, Fabrizio, et al. “Long-Range Temporal Correlations in the Broadband Resting State Activity of the Human Brain Revealed by Neuronal Avalanches.” <i>Neurocomputing</i>, vol. 461, Elsevier, 2021, pp. 657–66, doi:<a href=\"https://doi.org/10.1016/j.neucom.2020.05.126\">10.1016/j.neucom.2020.05.126</a>.","apa":"Lombardi, F., Shriki, O., Herrmann, H. J., &#38; de Arcangelis, L. (2021). Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches. <i>Neurocomputing</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.neucom.2020.05.126\">https://doi.org/10.1016/j.neucom.2020.05.126</a>","ama":"Lombardi F, Shriki O, Herrmann HJ, de Arcangelis L. Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches. <i>Neurocomputing</i>. 2021;461:657-666. doi:<a href=\"https://doi.org/10.1016/j.neucom.2020.05.126\">10.1016/j.neucom.2020.05.126</a>","ista":"Lombardi F, Shriki O, Herrmann HJ, de Arcangelis L. 2021. Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches. Neurocomputing. 461, 657–666.","chicago":"Lombardi, Fabrizio, Oren Shriki, Hans J Herrmann, and Lucilla de Arcangelis. “Long-Range Temporal Correlations in the Broadband Resting State Activity of the Human Brain Revealed by Neuronal Avalanches.” <i>Neurocomputing</i>. Elsevier, 2021. <a href=\"https://doi.org/10.1016/j.neucom.2020.05.126\">https://doi.org/10.1016/j.neucom.2020.05.126</a>.","ieee":"F. Lombardi, O. Shriki, H. J. Herrmann, and L. de Arcangelis, “Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches,” <i>Neurocomputing</i>, vol. 461. Elsevier, pp. 657–666, 2021.","short":"F. Lombardi, O. Shriki, H.J. Herrmann, L. de Arcangelis, Neurocomputing 461 (2021) 657–666."},"date_published":"2021-05-13T00:00:00Z","isi":1,"doi":"10.1016/j.neucom.2020.05.126","main_file_link":[{"url":"https://doi.org/10.1101/2020.02.03.930966","open_access":"1"}],"status":"public","project":[{"name":"ISTplus - Postdoctoral Fellowships","grant_number":"754411","call_identifier":"H2020","_id":"260C2330-B435-11E9-9278-68D0E5697425"}],"abstract":[{"text":"Resting-state brain activity is characterized by the presence of neuronal avalanches showing absence of characteristic size. Such evidence has been interpreted in the context of criticality and associated with the normal functioning of the brain. A distinctive attribute of systems at criticality is the presence of long-range correlations. Thus, to verify the hypothesis that the brain operates close to a critical point and consequently assess deviations from criticality for diagnostic purposes, it is of primary importance to robustly and reliably characterize correlations in resting-state brain activity. Recent works focused on the analysis of narrow-band electroencephalography (EEG) and magnetoencephalography (MEG) signal amplitude envelope, showing evidence of long-range temporal correlations (LRTC) in neural oscillations. However, brain activity is a broadband phenomenon, and a significant piece of information useful to precisely discriminate between normal (critical) and pathological behavior (non-critical), may be encoded in the broadband spatio-temporal cortical dynamics. Here we propose to characterize the temporal correlations in the broadband brain activity through the lens of neuronal avalanches. To this end, we consider resting-state EEG and long-term MEG recordings, extract the corresponding neuronal avalanche sequences, and study their temporal correlations. We demonstrate that the broadband resting-state brain activity consistently exhibits long-range power-law correlations in both EEG and MEG recordings, with similar values of the scaling exponents. Importantly, although we observe that the avalanche size distribution depends on scale parameters, scaling exponents characterizing long-range correlations are quite robust. In particular, they are independent of the temporal binning (scale of analysis), indicating that our analysis captures intrinsic characteristics of the underlying dynamics. Because neuronal avalanches constitute a fundamental feature of neural systems with universal characteristics, the proposed approach may serve as a general, systems- and experiment-independent procedure to infer the existence of underlying long-range correlations in extended neural systems, and identify pathological behaviors in the complex spatio-temporal interplay of cortical rhythms.","lang":"eng"}],"month":"05","external_id":{"isi":["000704086300015"]},"type":"journal_article","oa_version":"Preprint"},{"type":"journal_article","external_id":{"pmid":["33411759"],"isi":["000608045000010"]},"oa_version":"Published Version","related_material":{"record":[{"status":"public","id":"8930","relation":"research_data"},{"relation":"earlier_version","status":"public","id":"7673"}]},"abstract":[{"text":"Phenomenological relations such as Ohm’s or Fourier’s law have a venerable history in physics but are still scarce in biology. This situation restrains predictive theory. Here, we build on bacterial “growth laws,” which capture physiological feedback between translation and cell growth, to construct a minimal biophysical model for the combined action of ribosome-targeting antibiotics. Our model predicts drug interactions like antagonism or synergy solely from responses to individual drugs. We provide analytical results for limiting cases, which agree well with numerical results. We systematically refine the model by including direct physical interactions of different antibiotics on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions that were derived using entropy maximization. We further refine the model to include the effects of antibiotics that mimic starvation and the presence of resistance genes. We describe the impact of a starvation-mimicking antibiotic on drug interactions analytically and verify it experimentally. Our extended model suggests a change in the type of drug interaction that depends on the strength of resistance, which challenges established rescaling paradigms. We experimentally show that the presence of unregulated resistance genes can lead to altered drug interaction, which agrees with the prediction of the model. While minimal, the model is readily adaptable and opens the door to predicting interactions of second and higher-order in a broad range of biological systems.","lang":"eng"}],"month":"01","doi":"10.1371/journal.pcbi.1008529","isi":1,"status":"public","project":[{"_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"P27201-B22","name":"Revealing the mechanisms underlying drug interactions"},{"_id":"254E9036-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","name":"Biophysics of information processing in gene regulation","grant_number":"P28844-B27"}],"ddc":["570"],"citation":{"mla":"Kavcic, Bor, et al. “Minimal Biophysical Model of Combined Antibiotic Action.” <i>PLOS Computational Biology</i>, vol. 17, e1008529, Public Library of Science, 2021, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1008529\">10.1371/journal.pcbi.1008529</a>.","apa":"Kavcic, B., Tkačik, G., &#38; Bollenbach, M. T. (2021). Minimal biophysical model of combined antibiotic action. <i>PLOS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1008529\">https://doi.org/10.1371/journal.pcbi.1008529</a>","ista":"Kavcic B, Tkačik G, Bollenbach MT. 2021. Minimal biophysical model of combined antibiotic action. PLOS Computational Biology. 17, e1008529.","ama":"Kavcic B, Tkačik G, Bollenbach MT. Minimal biophysical model of combined antibiotic action. <i>PLOS Computational Biology</i>. 2021;17. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1008529\">10.1371/journal.pcbi.1008529</a>","chicago":"Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “Minimal Biophysical Model of Combined Antibiotic Action.” <i>PLOS Computational Biology</i>. Public Library of Science, 2021. <a href=\"https://doi.org/10.1371/journal.pcbi.1008529\">https://doi.org/10.1371/journal.pcbi.1008529</a>.","ieee":"B. Kavcic, G. Tkačik, and M. T. Bollenbach, “Minimal biophysical model of combined antibiotic action,” <i>PLOS Computational Biology</i>, vol. 17. Public Library of Science, 2021.","short":"B. Kavcic, G. Tkačik, M.T. Bollenbach, PLOS Computational Biology 17 (2021)."},"date_published":"2021-01-07T00:00:00Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png"},"department":[{"_id":"GaTk"}],"article_processing_charge":"Yes","has_accepted_license":"1","article_number":"e1008529","pmid":1,"keyword":["Modelling and Simulation","Genetics","Molecular Biology","Antibiotics","Drug interactions"],"publication_status":"published","fulldoi":"https://doi.org/10.1371/journal.pcbi.1008529","date_updated":"2026-07-06T12:44:35Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"first_name":"Bor","orcid":"0000-0001-6041-254X","full_name":"Kavcic, Bor","id":"350F91D2-F248-11E8-B48F-1D18A9856A87","last_name":"Kavcic"},{"first_name":"Gašper","orcid":"0000-0002-6699-1455","full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkačik"},{"full_name":"Bollenbach, Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","last_name":"Bollenbach","first_name":"Tobias","orcid":"0000-0003-4398-476X"}],"article_type":"original","title":"Minimal biophysical model of combined antibiotic action","quality_controlled":"1","file":[{"file_size":3690053,"date_created":"2021-02-04T12:30:48Z","file_name":"2021_PlosComBio_Kavcic.pdf","date_updated":"2021-02-04T12:30:48Z","content_type":"application/pdf","creator":"dernst","relation":"main_file","access_level":"open_access","file_id":"9092","checksum":"e29f2b42651bef8e034781de8781ffac","success":1}],"scopus_import":"1","publication_identifier":{"issn":["1553-7358"]},"publisher":"Public Library of Science","file_date_updated":"2021-02-04T12:30:48Z","_id":"8997","date_created":"2021-01-08T07:16:18Z","intvolume":"        17","oa":1,"language":[{"iso":"eng"}],"year":"2021","acknowledgement":"This work was supported in part by Tum stipend of Knafelj foundation (to B.K.), Austrian Science Fund (FWF) standalone grants P 27201-B22 (to T.B.) and P 28844(to G.T.), HFSP program Grant RGP0042/2013 (to T.B.), German Research Foundation (DFG) individual grant BO 3502/2-1 (to T.B.), and German Research Foundation (DFG) Collaborative Research Centre (SFB) 1310 (to T.B.). ","day":"07","publication":"PLOS Computational Biology","volume":17},{"department":[{"_id":"GaTk"}],"article_processing_charge":"No","article_number":"2108.06686","publication_status":"submitted","fulldoi":"https://doi.org/10.48550/ARXIV.2108.06686","date_updated":"2026-07-29T06:53:41Z","ec_funded":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","external_id":{"arxiv":["2108.06686"]},"type":"preprint","oa_version":"Preprint","related_material":{"record":[{"relation":"later_version","status":"public","id":"12762"}]},"abstract":[{"lang":"eng","text":"Brain dynamics display collective phenomena as diverse as neuronal oscillations and avalanches. Oscillations are rhythmic, with fluctuations occurring at a characteristic scale, whereas avalanches are scale-free cascades of neural activity. Here we show that such antithetic features can coexist in a very generic class of adaptive neural networks. In the most simple yet fully microscopic model from this class we make direct contact with human brain resting-state activity recordings via tractable inference of the model's two essential parameters. The inferred model quantitatively captures the dynamics over a broad range of scales, from single sensor fluctuations, collective behaviors of nearly-synchronous extreme events on multiple sensors, to neuronal avalanches unfolding over multiple sensors across multiple time-bins. Importantly, the inferred parameters correlate with model-independent signatures of \"closeness to criticality\", suggesting that the coexistence of scale-specific (neural oscillations) and scale-free (neuronal avalanches) dynamics in brain activity occurs close to a non-equilibrium critical point at the onset of self-sustained oscillations."}],"month":"08","doi":"10.48550/ARXIV.2108.06686","OA_type":"green","project":[{"_id":"260C2330-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","grant_number":"754411","name":"ISTplus - Postdoctoral Fellowships"},{"name":"Efficient coding with biophysical realism","grant_number":"P34015","_id":"626c45b5-2b32-11ec-9570-e509828c1ba6"}],"status":"public","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2108.06686"}],"ddc":["570"],"citation":{"short":"F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, D. De Martino, (n.d.).","ieee":"F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, and D. De Martino, “Quantifying the coexistence of neuronal oscillations and avalanches.” arXiv.","ama":"Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Quantifying the coexistence of neuronal oscillations and avalanches. doi:<a href=\"https://doi.org/10.48550/ARXIV.2108.06686\">10.48550/ARXIV.2108.06686</a>","ista":"Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Quantifying the coexistence of neuronal oscillations and avalanches. 2108.06686.","chicago":"Lombardi, Fabrizio, Selver Pepic, Oren Shriki, Gašper Tkačik, and Daniele De Martino. “Quantifying the Coexistence of Neuronal Oscillations and Avalanches.” arXiv, n.d. <a href=\"https://doi.org/10.48550/ARXIV.2108.06686\">https://doi.org/10.48550/ARXIV.2108.06686</a>.","apa":"Lombardi, F., Pepic, S., Shriki, O., Tkačik, G., &#38; De Martino, D. (n.d.). Quantifying the coexistence of neuronal oscillations and avalanches. arXiv. <a href=\"https://doi.org/10.48550/ARXIV.2108.06686\">https://doi.org/10.48550/ARXIV.2108.06686</a>","mla":"Lombardi, Fabrizio, et al. <i>Quantifying the Coexistence of Neuronal Oscillations and Avalanches</i>. 2108.06686, arXiv, doi:<a href=\"https://doi.org/10.48550/ARXIV.2108.06686\">10.48550/ARXIV.2108.06686</a>."},"date_published":"2021-08-17T00:00:00Z","oa":1,"OA_place":"repository","language":[{"iso":"eng"}],"year":"2021","acknowledgement":"FL acknowledges support from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 754411. GT\r\nacknowledges the support of the Austrian Science Fund (FWF) under Stand-Alone Grant\r\nNo. P34015.","arxiv":1,"day":"17","author":[{"orcid":"0000-0003-2623-5249","first_name":"Fabrizio","id":"A057D288-3E88-11E9-986D-0CF4E5697425","full_name":"Lombardi, Fabrizio","last_name":"Lombardi"},{"first_name":"Selver","id":"F93245C4-C3CA-11E9-B4F0-C6F4E5697425","full_name":"Pepic, Selver","last_name":"Pepic"},{"first_name":"Oren","last_name":"Shriki","full_name":"Shriki, Oren"},{"last_name":"Tkačik","full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","first_name":"Gašper"},{"last_name":"De Martino","full_name":"De Martino, Daniele","first_name":"Daniele"}],"title":"Quantifying the coexistence of neuronal oscillations and avalanches","publisher":"arXiv","date_created":"2022-03-21T11:41:28Z","_id":"10912"},{"_id":"10579","date_created":"2021-12-28T06:52:09Z","author":[{"last_name":"Kavcic","id":"350F91D2-F248-11E8-B48F-1D18A9856A87","full_name":"Kavcic, Bor","first_name":"Bor","orcid":"0000-0001-6041-254X"},{"first_name":"Gašper","orcid":"0000-0002-6699-1455","last_name":"Tkačik","full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"title":"Token-driven totally asymmetric simple exclusion process","publication":"arXiv","day":"27","arxiv":1,"acknowledgement":"B.K. thanks Stefano Elefante, Simon Rella, and Michal Hledík for their help with the usage of the cluster. B.K. additionally thanks Călin Guet and his group for help and advice. We thank M. Hennessey-Wesen for constructive comments on the manuscript. We thank Ankita Gupta (Indian Institute of Technology) for spotting a typographical error in Eq. (49) in the preprint version of this paper.","language":[{"iso":"eng"}],"corr_author":"1","year":"2021","oa":1,"citation":{"mla":"Kavcic, Bor, and Gašper Tkačik. “Token-Driven Totally Asymmetric Simple Exclusion Process.” <i>ArXiv</i>, 2112.13558, doi:<a href=\"https://doi.org/10.48550/arXiv.2112.13558\">10.48550/arXiv.2112.13558</a>.","apa":"Kavcic, B., &#38; Tkačik, G. (n.d.). Token-driven totally asymmetric simple exclusion process. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2112.13558\">https://doi.org/10.48550/arXiv.2112.13558</a>","short":"B. Kavcic, G. Tkačik, ArXiv (n.d.).","ieee":"B. Kavcic and G. Tkačik, “Token-driven totally asymmetric simple exclusion process,” <i>arXiv</i>. .","ista":"Kavcic B, Tkačik G. Token-driven totally asymmetric simple exclusion process. arXiv, 2112.13558.","chicago":"Kavcic, Bor, and Gašper Tkačik. “Token-Driven Totally Asymmetric Simple Exclusion Process.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2112.13558\">https://doi.org/10.48550/arXiv.2112.13558</a>.","ama":"Kavcic B, Tkačik G. Token-driven totally asymmetric simple exclusion process. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2112.13558\">10.48550/arXiv.2112.13558</a>"},"ddc":["530"],"tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)","image":"/images/cc_by_nc_nd.png"},"date_published":"2021-12-27T00:00:00Z","doi":"10.48550/arXiv.2112.13558","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2112.13558"}],"status":"public","related_material":{"record":[{"relation":"later_version","status":"public","id":"19785"}]},"abstract":[{"text":"We consider a totally asymmetric simple exclusion process (TASEP) consisting of particles on a lattice that require binding by a \"token\" to move. Using a combination of theory and simulations, we address the following questions: (i) How token binding kinetics affects the current-density relation; (ii) How the current-density relation depends on the scarcity of tokens; (iii) How tokens propagate the effects of the locally-imposed disorder (such a slow site) over the entire lattice; (iv) How a shared pool of tokens couples concurrent TASEPs running on multiple lattices; (v) How our results translate to TASEPs with open boundaries that exchange particles with the reservoir. Since real particle motion (including in systems that inspired the standard TASEP model, e.g., protein synthesis or movement of molecular motors) is often catalyzed, regulated, actuated, or otherwise mediated, the token-driven TASEP dynamics analyzed in this paper should allow for a better understanding of real systems and enable a closer match between TASEP theory and experimental observations.","lang":"eng"}],"month":"12","external_id":{"arxiv":["2112.13558"]},"type":"preprint","oa_version":"Preprint","fulldoi":"https://doi.org/10.48550/arXiv.2112.13558","date_updated":"2026-08-04T08:34:23Z","publication_status":"submitted","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_number":"2112.13558","department":[{"_id":"GaTk"}],"has_accepted_license":"1","article_processing_charge":"No"},{"type":"journal_article","external_id":{"isi":["000682123900002"],"arxiv":["2102.04832"]},"oa_version":"Preprint","abstract":[{"text":"Amplitude demodulation is a classical operation used in signal processing. For a long time, its effective applications in practice have been limited to narrowband signals. In this work, we generalize amplitude demodulation to wideband signals. We pose demodulation as a recovery problem of an oversampled corrupted signal and introduce special iterative schemes belonging to the family of alternating projection algorithms to solve it. Sensibly chosen structural assumptions on the demodulation outputs allow us to reveal the high inferential accuracy of the method over a rich set of relevant signals. This new approach surpasses current state-of-the-art demodulation techniques apt to wideband signals in computational efficiency by up to many orders of magnitude with no sacrifice in quality. Such performance opens the door for applications of the amplitude demodulation procedure in new contexts. In particular, the new method makes online and large-scale offline data processing feasible, including the calculation of modulator-carrier pairs in higher dimensions and poor sampling conditions, independent of the signal bandwidth. We illustrate the utility and specifics of applications of the new method in practice by using natural speech and synthetic signals.","lang":"eng"}],"month":"06","isi":1,"doi":"10.1109/TSP.2021.3087899","main_file_link":[{"url":"https://arxiv.org/abs/2102.04832","open_access":"1"}],"status":"public","citation":{"short":"M. Gabrielaitis, IEEE Transactions on Signal Processing 69 (2021) 4039–4054.","ieee":"M. Gabrielaitis, “Fast and accurate amplitude demodulation of wideband signals,” <i>IEEE Transactions on Signal Processing</i>, vol. 69. IEEE, pp. 4039–4054, 2021.","ama":"Gabrielaitis M. Fast and accurate amplitude demodulation of wideband signals. <i>IEEE Transactions on Signal Processing</i>. 2021;69:4039-4054. doi:<a href=\"https://doi.org/10.1109/TSP.2021.3087899\">10.1109/TSP.2021.3087899</a>","ista":"Gabrielaitis M. 2021. Fast and accurate amplitude demodulation of wideband signals. IEEE Transactions on Signal Processing. 69, 4039–4054.","chicago":"Gabrielaitis, Mantas. “Fast and Accurate Amplitude Demodulation of Wideband Signals.” <i>IEEE Transactions on Signal Processing</i>. IEEE, 2021. <a href=\"https://doi.org/10.1109/TSP.2021.3087899\">https://doi.org/10.1109/TSP.2021.3087899</a>.","apa":"Gabrielaitis, M. (2021). Fast and accurate amplitude demodulation of wideband signals. <i>IEEE Transactions on Signal Processing</i>. IEEE. <a href=\"https://doi.org/10.1109/TSP.2021.3087899\">https://doi.org/10.1109/TSP.2021.3087899</a>","mla":"Gabrielaitis, Mantas. “Fast and Accurate Amplitude Demodulation of Wideband Signals.” <i>IEEE Transactions on Signal Processing</i>, vol. 69, IEEE, 2021, pp. 4039–54, doi:<a href=\"https://doi.org/10.1109/TSP.2021.3087899\">10.1109/TSP.2021.3087899</a>."},"date_published":"2021-06-09T00:00:00Z","department":[{"_id":"GaTk"}],"article_processing_charge":"No","date_updated":"2026-08-12T06:42:41Z","fulldoi":"https://doi.org/10.1109/TSP.2021.3087899","publication_status":"published","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_type":"original","author":[{"id":"4D5B0CBC-F248-11E8-B48F-1D18A9856A87","full_name":"Gabrielaitis, Mantas","last_name":"Gabrielaitis","orcid":"0000-0002-7758-2016","first_name":"Mantas"}],"quality_controlled":"1","title":"Fast and accurate amplitude demodulation of wideband signals","scopus_import":"1","publisher":"IEEE","publication_identifier":{"issn":["1053-587X"],"eissn":["1941-0476"]},"page":"4039 - 4054","_id":"9828","date_created":"2021-08-08T22:01:31Z","intvolume":"        69","oa":1,"language":[{"iso":"eng"}],"corr_author":"1","year":"2021","arxiv":1,"day":"09","acknowledgement":"The author thanks his colleagues K. Huszár and G. Tkačik for valuable discussions and comments on the manuscript.","publication":"IEEE Transactions on Signal Processing","volume":69},{"intvolume":"       148","oa":1,"language":[{"iso":"eng"}],"year":"2021","day":"01","acknowledgement":"This work was supported in part by the National Science Foundation, through the Center for the Physics of Biological Function (PHY-1734030), by the National Institutes of Health (R01GM097275) and by the Fonds zur Förderung der wissenschaftlichen Forschung (FWF P28844). Deposited in PMC for release after 12 months.","volume":148,"publication":"Development","author":[{"first_name":"Gašper","orcid":"0000-0002-6699-1455","last_name":"Tkačik","full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Thomas","last_name":"Gregor","full_name":"Gregor, Thomas"}],"article_type":"original","quality_controlled":"1","title":"The many bits of positional information","scopus_import":"1","publisher":"Company of Biologists","publication_identifier":{"eissn":["1477-9129"]},"_id":"9226","date_created":"2021-03-07T23:01:25Z","department":[{"_id":"GaTk"}],"article_processing_charge":"No","article_number":"dev176065","pmid":1,"date_updated":"2026-08-12T09:57:53Z","fulldoi":"https://doi.org/10.1242/dev.176065","issue":"2","publication_status":"published","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"journal_article","external_id":{"isi":["000613906000007"],"pmid":["33526425"]},"oa_version":"Published Version","abstract":[{"text":"Half a century after Lewis Wolpert's seminal conceptual advance on how cellular fates distribute in space, we provide a brief historical perspective on how the concept of positional information emerged and influenced the field of developmental biology and beyond. We focus on a modern interpretation of this concept in terms of information theory, largely centered on its application to cell specification in the early Drosophila embryo. We argue that a true physical variable (position) is encoded in local concentrations of patterning molecules, that this mapping is stochastic, and that the processes by which positions and corresponding cell fates are determined based on these concentrations need to take such stochasticity into account. With this approach, we shift the focus from biological mechanisms, molecules, genes and pathways to quantitative systems-level questions: where does positional information reside, how it is transformed and accessed during development, and what fundamental limits it is subject to?","lang":"eng"}],"month":"02","isi":1,"doi":"10.1242/dev.176065","main_file_link":[{"url":"https://doi.org/10.1242/dev.176065","open_access":"1"}],"project":[{"grant_number":"P28844-B27","name":"Biophysics of information processing in gene regulation","call_identifier":"FWF","_id":"254E9036-B435-11E9-9278-68D0E5697425"}],"status":"public","citation":{"chicago":"Tkačik, Gašper, and Thomas Gregor. “The Many Bits of Positional Information.” <i>Development</i>. Company of Biologists, 2021. <a href=\"https://doi.org/10.1242/dev.176065\">https://doi.org/10.1242/dev.176065</a>.","ama":"Tkačik G, Gregor T. The many bits of positional information. <i>Development</i>. 2021;148(2). doi:<a href=\"https://doi.org/10.1242/dev.176065\">10.1242/dev.176065</a>","ista":"Tkačik G, Gregor T. 2021. The many bits of positional information. Development. 148(2), dev176065.","ieee":"G. Tkačik and T. Gregor, “The many bits of positional information,” <i>Development</i>, vol. 148, no. 2. Company of Biologists, 2021.","short":"G. Tkačik, T. Gregor, Development 148 (2021).","apa":"Tkačik, G., &#38; Gregor, T. (2021). The many bits of positional information. <i>Development</i>. Company of Biologists. <a href=\"https://doi.org/10.1242/dev.176065\">https://doi.org/10.1242/dev.176065</a>","mla":"Tkačik, Gašper, and Thomas Gregor. “The Many Bits of Positional Information.” <i>Development</i>, vol. 148, no. 2, dev176065, Company of Biologists, 2021, doi:<a href=\"https://doi.org/10.1242/dev.176065\">10.1242/dev.176065</a>."},"ddc":["570"],"date_published":"2021-02-01T00:00:00Z"},{"day":"07","acknowledgement":"The authors thank Dario Ringach for providing the V1 receptive fields and Olivier Marre for providing the retinal receptive fields. W.M. was funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 754411. M.H. was funded in part by Human Frontiers Science grant no. HFSP RGP0032/2018.","publication":"Neuron","volume":109,"oa":1,"intvolume":"       109","year":"2021","language":[{"iso":"eng"}],"corr_author":"1","page":"1227-1241.e5","_id":"7553","date_created":"2020-02-28T11:00:12Z","quality_controlled":"1","title":"Statistical analysis and optimality of neural systems","author":[{"first_name":"Wiktor F","id":"358A453A-F248-11E8-B48F-1D18A9856A87","last_name":"Mlynarski","full_name":"Mlynarski, Wiktor F"},{"first_name":"Michal","full_name":"Hledik, Michal","last_name":"Hledik","id":"4171253A-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Thomas R","orcid":"0000-0002-1287-3779","full_name":"Sokolowski, Thomas R","id":"3E999752-F248-11E8-B48F-1D18A9856A87","last_name":"Sokolowski"},{"full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkačik","orcid":"0000-0002-6699-1455","first_name":"Gašper"}],"publisher":"Cell Press","scopus_import":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","ec_funded":1,"fulldoi":"https://doi.org/10.1016/j.neuron.2021.01.020","date_updated":"2026-10-02T11:40:30Z","publication_status":"published","issue":"7","article_processing_charge":"No","department":[{"_id":"GaTk"}],"pmid":1,"main_file_link":[{"open_access":"1","url":"https://doi.org/10.1101/848374"}],"project":[{"grant_number":"754411","name":"ISTplus - Postdoctoral Fellowships","_id":"260C2330-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"}],"status":"public","doi":"10.1016/j.neuron.2021.01.020","isi":1,"date_published":"2021-04-07T00:00:00Z","citation":{"ieee":"W. F. Mlynarski, M. Hledik, T. R. Sokolowski, and G. Tkačik, “Statistical analysis and optimality of neural systems,” <i>Neuron</i>, vol. 109, no. 7. Cell Press, p. 1227–1241.e5, 2021.","short":"W.F. Mlynarski, M. Hledik, T.R. Sokolowski, G. Tkačik, Neuron 109 (2021) 1227–1241.e5.","chicago":"Mlynarski, Wiktor F, Michal Hledik, Thomas R Sokolowski, and Gašper Tkačik. “Statistical Analysis and Optimality of Neural Systems.” <i>Neuron</i>. Cell Press, 2021. <a href=\"https://doi.org/10.1016/j.neuron.2021.01.020\">https://doi.org/10.1016/j.neuron.2021.01.020</a>.","ama":"Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. Statistical analysis and optimality of neural systems. <i>Neuron</i>. 2021;109(7):1227-1241.e5. doi:<a href=\"https://doi.org/10.1016/j.neuron.2021.01.020\">10.1016/j.neuron.2021.01.020</a>","ista":"Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. 2021. Statistical analysis and optimality of neural systems. Neuron. 109(7), 1227–1241.e5.","apa":"Mlynarski, W. F., Hledik, M., Sokolowski, T. R., &#38; Tkačik, G. (2021). Statistical analysis and optimality of neural systems. <i>Neuron</i>. Cell Press. <a href=\"https://doi.org/10.1016/j.neuron.2021.01.020\">https://doi.org/10.1016/j.neuron.2021.01.020</a>","mla":"Mlynarski, Wiktor F., et al. “Statistical Analysis and Optimality of Neural Systems.” <i>Neuron</i>, vol. 109, no. 7, Cell Press, 2021, p. 1227–1241.e5, doi:<a href=\"https://doi.org/10.1016/j.neuron.2021.01.020\">10.1016/j.neuron.2021.01.020</a>."},"oa_version":"Preprint","external_id":{"pmid":["33592180"],"isi":["000637809600006"]},"type":"journal_article","month":"04","abstract":[{"text":"Normative theories and statistical inference provide complementary approaches for the study of biological systems. A normative theory postulates that organisms have adapted to efficiently solve essential tasks, and proceeds to mathematically work out testable consequences of such optimality; parameters that maximize the hypothesized organismal function can be derived ab initio, without reference to experimental data. In contrast, statistical inference focuses on efficient utilization of data to learn model parameters, without reference to any a priori notion of biological function, utility, or fitness. Traditionally, these two approaches were developed independently and applied separately. Here we unify them in a coherent Bayesian framework that embeds a normative theory into a family of maximum-entropy “optimization priors.” This family defines a smooth interpolation between a data-rich inference regime (characteristic of “bottom-up” statistical models), and a data-limited ab inito prediction regime (characteristic of “top-down” normative theory). We demonstrate the applicability of our framework using data from the visual cortex, and argue that the flexibility it affords is essential to address a number of fundamental challenges relating to inference and prediction in complex, high-dimensional biological problems.","lang":"eng"}],"related_material":{"link":[{"description":"News on IST Homepage","relation":"press_release","url":"https://ist.ac.at/en/news/can-evolution-be-predicted/"}],"record":[{"relation":"dissertation_contains","id":"15020","status":"public"}]}},{"abstract":[{"text":"Although much is known about how single neurons in the hippocampus represent an animal’s position, how cell-cell interactions contribute to spatial coding remains poorly understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured cell-to-cell interactions whose statistics depend on familiar vs. novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the signal-to-noise ratio of their spatial inputs. Moreover, the topology of the interactions facilitates linear decodability, making the information easy to read out by downstream circuits. These findings suggest that the efficient coding hypothesis is not applicable only to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain.","lang":"eng"}],"related_material":{"record":[{"relation":"dissertation_contains","id":"11932","status":"public"},{"status":"public","id":"14656","relation":"later_version"}]},"month":"09","author":[{"orcid":"0000-0001-8849-6570","first_name":"Michele","id":"30BD0376-F248-11E8-B48F-1D18A9856A87","full_name":"Nardin, Michele","last_name":"Nardin"},{"id":"3FA14672-F248-11E8-B48F-1D18A9856A87","full_name":"Csicsvari, Jozsef L","last_name":"Csicsvari","first_name":"Jozsef L","orcid":"0000-0002-5193-4036"},{"first_name":"Gašper","orcid":"0000-0002-6699-1455","full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkačik"},{"last_name":"Savin","id":"3933349E-F248-11E8-B48F-1D18A9856A87","full_name":"Savin, Cristina","first_name":"Cristina"}],"type":"preprint","title":"The structure of hippocampal CA1 interactions optimizes spatial coding across experience","oa_version":"Preprint","das_tickbox":"1","date_created":"2021-10-04T06:23:34Z","_id":"10077","citation":{"ieee":"M. Nardin, J. L. Csicsvari, G. Tkačik, and C. Savin, “The structure of hippocampal CA1 interactions optimizes spatial coding across experience,” <i>bioRxiv</i>. .","short":"M. Nardin, J.L. Csicsvari, G. Tkačik, C. Savin, BioRxiv (n.d.).","ista":"Nardin M, Csicsvari JL, Tkačik G, Savin C. The structure of hippocampal CA1 interactions optimizes spatial coding across experience. bioRxiv, <a href=\"https://doi.org/10.1101/2021.09.28.460602\">10.1101/2021.09.28.460602</a>.","ama":"Nardin M, Csicsvari JL, Tkačik G, Savin C. The structure of hippocampal CA1 interactions optimizes spatial coding across experience. <i>bioRxiv</i>. doi:<a href=\"https://doi.org/10.1101/2021.09.28.460602\">10.1101/2021.09.28.460602</a>","chicago":"Nardin, Michele, Jozsef L Csicsvari, Gašper Tkačik, and Cristina Savin. “The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across Experience.” <i>BioRxiv</i>, n.d. <a href=\"https://doi.org/10.1101/2021.09.28.460602\">https://doi.org/10.1101/2021.09.28.460602</a>.","apa":"Nardin, M., Csicsvari, J. L., Tkačik, G., &#38; Savin, C. (n.d.). The structure of hippocampal CA1 interactions optimizes spatial coding across experience. <i>bioRxiv</i>. <a href=\"https://doi.org/10.1101/2021.09.28.460602\">https://doi.org/10.1101/2021.09.28.460602</a>","mla":"Nardin, Michele, et al. “The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across Experience.” <i>BioRxiv</i>, doi:<a href=\"https://doi.org/10.1101/2021.09.28.460602\">10.1101/2021.09.28.460602</a>."},"tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)","image":"/images/cc_by_nc_nd.png"},"date_published":"2021-09-29T00:00:00Z","doi":"10.1101/2021.09.28.460602","main_file_link":[{"url":"https://www.biorxiv.org/content/10.1101/2021.09.28.460602","open_access":"1"}],"project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734"},{"name":"International IST Doctoral Program","grant_number":"665385","call_identifier":"H2020","_id":"2564DBCA-B435-11E9-9278-68D0E5697425"},{"call_identifier":"FP7","_id":"257A4776-B435-11E9-9278-68D0E5697425","name":"Memory-related information processing in neuronal circuits of the hippocampus and entorhinal cortex","grant_number":"281511"},{"name":"Efficient coding with biophysical realism","grant_number":"P34015","_id":"626c45b5-2b32-11ec-9570-e509828c1ba6"}],"status":"public","language":[{"iso":"eng"}],"year":"2021","department":[{"_id":"GradSch"},{"_id":"JoCs"},{"_id":"GaTk"}],"oa":1,"article_processing_charge":"No","fulldoi":"https://doi.org/10.1101/2021.09.28.460602","publication":"bioRxiv","date_updated":"2026-10-24T22:30:50Z","publication_status":"draft","ec_funded":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","day":"29","acknowledgement":"We thank Peter Baracskay, Karola Kaefer and Hugo Malagon-Vina for the acquisition of the data. We thank Federico Stella for comments on an earlier version of the manuscript. MN was supported by European Union Horizon 2020 grant 665385, JC was supported by European Research Council consolidator grant 281511, GT was supported by the Austrian Science Fund (FWF) grant P34015, CS was supported by an IST fellow grant, National Institute of Mental Health Award 1R01MH125571-01, by the National Science Foundation under NSF Award No. 1922658 and a Google faculty award."},{"type":"journal_article","external_id":{"isi":["000526725200019"],"pmid":["32097416"]},"oa_version":"Published Version","abstract":[{"text":"Genes differ in the frequency at which they are expressed and in the form of regulation used to control their activity. In particular, positive or negative regulation can lead to activation of a gene in response to an external signal. Previous works proposed that the form of regulation of a gene correlates with its frequency of usage: positive regulation when the gene is frequently expressed and negative regulation when infrequently expressed. Such network design means that, in the absence of their regulators, the genes are found in their least required activity state, hence regulatory intervention is often necessary. Due to the multitude of genes and regulators, spurious binding and unbinding events, called “crosstalk”, could occur. To determine how the form of regulation affects the global crosstalk in the network, we used a mathematical model that includes multiple regulators and multiple target genes. We found that crosstalk depends non-monotonically on the availability of regulators. Our analysis showed that excess use of regulation entailed by the formerly suggested network design caused high crosstalk levels in a large part of the parameter space. We therefore considered the opposite ‘idle’ design, where the default unregulated state of genes is their frequently required activity state. We found, that ‘idle’ design minimized the use of regulation and thus minimized crosstalk. In addition, we estimated global crosstalk of S. cerevisiae using transcription factors binding data. We demonstrated that even partial network data could suffice to estimate its global crosstalk, suggesting its applicability to additional organisms. We found that S. cerevisiae estimated crosstalk is lower than that of a random network, suggesting that natural selection reduces crosstalk. In summary, our study highlights a new type of protein production cost which is typically overlooked: that of regulatory interference caused by the presence of excess regulators in the cell. It demonstrates the importance of whole-network descriptions, which could show effects missed by single-gene models.","lang":"eng"}],"related_material":{"record":[{"relation":"research_data","status":"deleted","id":"9716"},{"id":"9776","status":"public","relation":"research_data"},{"relation":"research_data","status":"public","id":"9779"},{"relation":"research_data","id":"9777","status":"public"},{"status":"public","id":"8155","relation":"dissertation_contains"}]},"month":"02","doi":"10.1371/journal.pcbi.1007642","isi":1,"status":"public","ddc":["000","570"],"citation":{"apa":"Grah, R., &#38; Friedlander, T. (2020). The relation between crosstalk and gene regulation form revisited. <i>PLOS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1007642\">https://doi.org/10.1371/journal.pcbi.1007642</a>","mla":"Grah, Rok, and Tamar Friedlander. “The Relation between Crosstalk and Gene Regulation Form Revisited.” <i>PLOS Computational Biology</i>, vol. 16, no. 2, e1007642, Public Library of Science, 2020, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007642\">10.1371/journal.pcbi.1007642</a>.","ieee":"R. Grah and T. Friedlander, “The relation between crosstalk and gene regulation form revisited,” <i>PLOS Computational Biology</i>, vol. 16, no. 2. Public Library of Science, 2020.","short":"R. Grah, T. Friedlander, PLOS Computational Biology 16 (2020).","ama":"Grah R, Friedlander T. The relation between crosstalk and gene regulation form revisited. <i>PLOS Computational Biology</i>. 2020;16(2). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007642\">10.1371/journal.pcbi.1007642</a>","ista":"Grah R, Friedlander T. 2020. The relation between crosstalk and gene regulation form revisited. PLOS Computational Biology. 16(2), e1007642.","chicago":"Grah, Rok, and Tamar Friedlander. “The Relation between Crosstalk and Gene Regulation Form Revisited.” <i>PLOS Computational Biology</i>. Public Library of Science, 2020. <a href=\"https://doi.org/10.1371/journal.pcbi.1007642\">https://doi.org/10.1371/journal.pcbi.1007642</a>."},"date_published":"2020-02-25T00:00:00Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png"},"department":[{"_id":"CaGu"},{"_id":"GaTk"}],"article_processing_charge":"No","has_accepted_license":"1","article_number":"e1007642","pmid":1,"issue":"2","publication_status":"published","date_updated":"2026-04-08T07:25:08Z","fulldoi":"https://doi.org/10.1371/journal.pcbi.1007642","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"first_name":"Rok","orcid":"0000-0003-2539-3560","last_name":"Grah","id":"483E70DE-F248-11E8-B48F-1D18A9856A87","full_name":"Grah, Rok"},{"first_name":"Tamar","last_name":"Friedlander","full_name":"Friedlander, Tamar"}],"article_type":"original","title":"The relation between crosstalk and gene regulation form revisited","quality_controlled":"1","file":[{"checksum":"5239dd134dc6e1c71fe7b3ce2953da37","creator":"dernst","relation":"main_file","file_id":"7579","access_level":"open_access","file_size":2209325,"content_type":"application/pdf","date_created":"2020-03-09T15:12:21Z","file_name":"2020_PlosCompBio_Grah.pdf","date_updated":"2020-07-14T12:48:00Z"}],"scopus_import":"1","publication_identifier":{"issn":["1553-7358"]},"publisher":"Public Library of Science","file_date_updated":"2020-07-14T12:48:00Z","_id":"7569","date_created":"2020-03-06T07:39:38Z","intvolume":"        16","oa":1,"language":[{"iso":"eng"}],"year":"2020","day":"25","publication":"PLOS Computational Biology","volume":16},{"type":"journal_article","external_id":{"pmid":["32231528"],"isi":["000525543200001"]},"oa_version":"Published Version","abstract":[{"text":"We propose that correlations among neurons are generically strong enough to organize neural activity patterns into a discrete set of clusters, which can each be viewed as a population codeword. Our reasoning starts with the analysis of retinal ganglion cell data using maximum entropy models, showing that the population is robustly in a frustrated, marginally sub-critical, or glassy, state. This leads to an argument that neural populations in many other brain areas might share this structure. Next, we use latent variable models to show that this glassy state possesses well-defined clusters of neural activity. Clusters have three appealing properties: (i) clusters exhibit error correction, i.e., they are reproducibly elicited by the same stimulus despite variability at the level of constituent neurons; (ii) clusters encode qualitatively different visual features than their constituent neurons; and (iii) clusters can be learned by downstream neural circuits in an unsupervised fashion. We hypothesize that these properties give rise to a “learnable” neural code which the cortical hierarchy uses to extract increasingly complex features without supervision or reinforcement.","lang":"eng"}],"month":"03","isi":1,"doi":"10.3389/fncom.2020.00020","status":"public","citation":{"apa":"Berry, M. J., &#38; Tkačik, G. (2020). Clustering of neural activity: A design principle for population codes. <i>Frontiers in Computational Neuroscience</i>. Frontiers. <a href=\"https://doi.org/10.3389/fncom.2020.00020\">https://doi.org/10.3389/fncom.2020.00020</a>","mla":"Berry, Michael J., and Gašper Tkačik. “Clustering of Neural Activity: A Design Principle for Population Codes.” <i>Frontiers in Computational Neuroscience</i>, vol. 14, 20, Frontiers, 2020, doi:<a href=\"https://doi.org/10.3389/fncom.2020.00020\">10.3389/fncom.2020.00020</a>.","ama":"Berry MJ, Tkačik G. Clustering of neural activity: A design principle for population codes. <i>Frontiers in Computational Neuroscience</i>. 2020;14. doi:<a href=\"https://doi.org/10.3389/fncom.2020.00020\">10.3389/fncom.2020.00020</a>","chicago":"Berry, Michael J., and Gašper Tkačik. “Clustering of Neural Activity: A Design Principle for Population Codes.” <i>Frontiers in Computational Neuroscience</i>. Frontiers, 2020. <a href=\"https://doi.org/10.3389/fncom.2020.00020\">https://doi.org/10.3389/fncom.2020.00020</a>.","ista":"Berry MJ, Tkačik G. 2020. Clustering of neural activity: A design principle for population codes. Frontiers in Computational Neuroscience. 14, 20.","short":"M.J. Berry, G. Tkačik, Frontiers in Computational Neuroscience 14 (2020).","ieee":"M. J. Berry and G. Tkačik, “Clustering of neural activity: A design principle for population codes,” <i>Frontiers in Computational Neuroscience</i>, vol. 14. Frontiers, 2020."},"ddc":["570"],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png"},"date_published":"2020-03-13T00:00:00Z","department":[{"_id":"GaTk"}],"article_processing_charge":"No","has_accepted_license":"1","article_number":"20","pmid":1,"publication_status":"published","fulldoi":"https://doi.org/10.3389/fncom.2020.00020","date_updated":"2026-04-16T08:28:50Z","user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","article_type":"original","author":[{"full_name":"Berry, Michael J.","last_name":"Berry","first_name":"Michael J."},{"full_name":"Tkačik, Gašper","last_name":"Tkačik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","orcid":"0000-0002-6699-1455"}],"title":"Clustering of neural activity: A design principle for population codes","quality_controlled":"1","file":[{"date_created":"2020-04-14T12:20:39Z","date_updated":"2020-07-14T12:48:01Z","file_name":"2020_Frontiers_Berry.pdf","content_type":"application/pdf","file_size":4082937,"checksum":"2b1da23823eae9cedbb42d701945b61e","access_level":"open_access","file_id":"7659","relation":"main_file","creator":"dernst"}],"scopus_import":"1","publication_identifier":{"eissn":["1662-5188"]},"publisher":"Frontiers","file_date_updated":"2020-07-14T12:48:01Z","_id":"7656","date_created":"2020-04-12T22:00:40Z","intvolume":"        14","oa":1,"language":[{"iso":"eng"}],"year":"2020","day":"13","publication":"Frontiers in Computational Neuroscience","volume":14},{"day":"02","volume":40,"publication":"Journal of Neuroscience","oa":1,"intvolume":"        40","year":"2020","language":[{"iso":"eng"}],"file_date_updated":"2020-07-22T11:44:48Z","_id":"8084","page":"171-190","date_created":"2020-07-05T15:24:51Z","title":"Critical dynamics and coupling in bursts of cortical rhythms indicate non-homeostatic mechanism for sleep-stage transitions and dual role of VLPO neurons in both sleep and wake","quality_controlled":"1","article_type":"original","author":[{"last_name":"Lombardi","id":"A057D288-3E88-11E9-986D-0CF4E5697425","full_name":"Lombardi, Fabrizio","orcid":"0000-0003-2623-5249","first_name":"Fabrizio"},{"full_name":"Gómez-Extremera, Manuel","last_name":"Gómez-Extremera","first_name":"Manuel"},{"last_name":"Bernaola-Galván","full_name":"Bernaola-Galván, Pedro","first_name":"Pedro"},{"last_name":"Vetrivelan","full_name":"Vetrivelan, Ramalingam","first_name":"Ramalingam"},{"first_name":"Clifford B.","last_name":"Saper","full_name":"Saper, Clifford B."},{"first_name":"Thomas E.","last_name":"Scammell","full_name":"Scammell, Thomas E."},{"last_name":"Ivanov","full_name":"Ivanov, Plamen Ch.","first_name":"Plamen Ch."}],"publication_identifier":{"issn":["0270-6474"],"eissn":["1529-2401"]},"publisher":"Society for Neuroscience","file":[{"file_size":6646046,"date_updated":"2020-07-22T11:44:48Z","file_name":"2020_JournNeuroscience_Lombardi.pdf","date_created":"2020-07-22T11:44:48Z","content_type":"application/pdf","success":1,"creator":"dernst","relation":"main_file","access_level":"open_access","file_id":"8150"}],"scopus_import":"1","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","ec_funded":1,"issue":"1","publication_status":"published","fulldoi":"https://doi.org/10.1523/jneurosci.1278-19.2019","date_updated":"2025-04-14T07:44:04Z","article_processing_charge":"No","has_accepted_license":"1","department":[{"_id":"GaTk"}],"pmid":1,"project":[{"name":"ISTplus - Postdoctoral Fellowships","grant_number":"754411","_id":"260C2330-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"}],"status":"public","isi":1,"doi":"10.1523/jneurosci.1278-19.2019","date_published":"2020-01-02T00:00:00Z","ddc":["570"],"citation":{"mla":"Lombardi, Fabrizio, et al. “Critical Dynamics and Coupling in Bursts of Cortical Rhythms Indicate Non-Homeostatic Mechanism for Sleep-Stage Transitions and Dual Role of VLPO Neurons in Both Sleep and Wake.” <i>Journal of Neuroscience</i>, vol. 40, no. 1, Society for Neuroscience, 2020, pp. 171–90, doi:<a href=\"https://doi.org/10.1523/jneurosci.1278-19.2019\">10.1523/jneurosci.1278-19.2019</a>.","apa":"Lombardi, F., Gómez-Extremera, M., Bernaola-Galván, P., Vetrivelan, R., Saper, C. B., Scammell, T. E., &#38; Ivanov, P. C. (2020). Critical dynamics and coupling in bursts of cortical rhythms indicate non-homeostatic mechanism for sleep-stage transitions and dual role of VLPO neurons in both sleep and wake. <i>Journal of Neuroscience</i>. Society for Neuroscience. <a href=\"https://doi.org/10.1523/jneurosci.1278-19.2019\">https://doi.org/10.1523/jneurosci.1278-19.2019</a>","chicago":"Lombardi, Fabrizio, Manuel Gómez-Extremera, Pedro Bernaola-Galván, Ramalingam Vetrivelan, Clifford B. Saper, Thomas E. Scammell, and Plamen Ch. Ivanov. “Critical Dynamics and Coupling in Bursts of Cortical Rhythms Indicate Non-Homeostatic Mechanism for Sleep-Stage Transitions and Dual Role of VLPO Neurons in Both Sleep and Wake.” <i>Journal of Neuroscience</i>. Society for Neuroscience, 2020. <a href=\"https://doi.org/10.1523/jneurosci.1278-19.2019\">https://doi.org/10.1523/jneurosci.1278-19.2019</a>.","ista":"Lombardi F, Gómez-Extremera M, Bernaola-Galván P, Vetrivelan R, Saper CB, Scammell TE, Ivanov PC. 2020. Critical dynamics and coupling in bursts of cortical rhythms indicate non-homeostatic mechanism for sleep-stage transitions and dual role of VLPO neurons in both sleep and wake. Journal of Neuroscience. 40(1), 171–190.","ama":"Lombardi F, Gómez-Extremera M, Bernaola-Galván P, et al. Critical dynamics and coupling in bursts of cortical rhythms indicate non-homeostatic mechanism for sleep-stage transitions and dual role of VLPO neurons in both sleep and wake. <i>Journal of Neuroscience</i>. 2020;40(1):171-190. doi:<a href=\"https://doi.org/10.1523/jneurosci.1278-19.2019\">10.1523/jneurosci.1278-19.2019</a>","short":"F. Lombardi, M. Gómez-Extremera, P. Bernaola-Galván, R. Vetrivelan, C.B. Saper, T.E. Scammell, P.C. Ivanov, Journal of Neuroscience 40 (2020) 171–190.","ieee":"F. Lombardi <i>et al.</i>, “Critical dynamics and coupling in bursts of cortical rhythms indicate non-homeostatic mechanism for sleep-stage transitions and dual role of VLPO neurons in both sleep and wake,” <i>Journal of Neuroscience</i>, vol. 40, no. 1. Society for Neuroscience, pp. 171–190, 2020."},"oa_version":"Published Version","type":"journal_article","external_id":{"pmid":["31694962"],"isi":["000505167600016"]},"month":"01","abstract":[{"lang":"eng","text":"Origin and functions of intermittent transitions among sleep stages, including brief awakenings and arousals, constitute a challenge to the current homeostatic framework for sleep regulation, focusing on factors modulating sleep over large time scales. Here we propose that the complex micro-architecture characterizing sleep on scales of seconds and minutes results from intrinsic non-equilibrium critical dynamics. We investigate θ- and δ-wave dynamics in control rats and in rats where the sleep-promoting ventrolateral preoptic nucleus (VLPO) is lesioned (male Sprague-Dawley rats). We demonstrate that bursts in θ and δ cortical rhythms exhibit complex temporal organization, with long-range correlations and robust duality of power-law (θ-bursts, active phase) and exponential-like (δ-bursts, quiescent phase) duration distributions, features typical of non-equilibrium systems self-organizing at criticality. We show that such non-equilibrium behavior relates to anti-correlated coupling between θ- and δ-bursts, persists across a range of time scales, and is independent of the dominant physiologic state; indications of a basic principle in sleep regulation. Further, we find that VLPO lesions lead to a modulation of cortical dynamics resulting in altered dynamical parameters of θ- and δ-bursts and significant reduction in θ–δ coupling. Our empirical findings and model simulations demonstrate that θ–δ coupling is essential for the emerging non-equilibrium critical dynamics observed across the sleep–wake cycle, and indicate that VLPO neurons may have dual role for both sleep and arousal/brief wake activation. The uncovered critical behavior in sleep- and wake-related cortical rhythms indicates a mechanism essential for the micro-architecture of spontaneous sleep-stage and arousal transitions within a novel, non-homeostatic paradigm of sleep regulation."}]},{"month":"07","publisher":"Institute of Science and Technology Austria","abstract":[{"lang":"eng","text":"Antibiotics that interfere with translation, when combined, interact in diverse and difficult-to-predict ways. Here, we explain these interactions by \"translation bottlenecks\": points in the translation cycle where antibiotics block ribosomal progression. To elucidate the underlying mechanisms of drug interactions between translation inhibitors, we generate translation bottlenecks genetically using inducible control of translation factors that regulate well-defined translation cycle steps. These perturbations accurately mimic antibiotic action and drug interactions, supporting that the interplay of different translation bottlenecks causes these interactions. We further show that growth laws, combined with drug uptake and binding kinetics, enable the direct prediction of a large fraction of observed interactions, yet fail to predict suppression. However, varying two translation bottlenecks simultaneously supports that dense traffic of ribosomes and competition for translation factors account for the previously unexplained suppression. These results highlight the importance of \"continuous epistasis\" in bacterial physiology."}],"file":[{"file_size":255770756,"file_name":"natComm_2020_scripts.zip","date_updated":"2020-07-14T12:48:09Z","date_created":"2020-07-06T20:38:27Z","content_type":"application/zip","checksum":"5c321dbbb6d4b3c85da786fd3ebbdc98","creator":"bkavcic","relation":"main_file","access_level":"open_access","file_id":"8098"}],"oa_version":"Published Version","title":"Analysis scripts and research data for the paper \"Mechanisms of drug interactions between translation-inhibiting antibiotics\"","author":[{"last_name":"Kavcic","id":"350F91D2-F248-11E8-B48F-1D18A9856A87","full_name":"Kavcic, Bor","first_name":"Bor","orcid":"0000-0001-6041-254X"}],"type":"research_data","date_published":"2020-07-15T00:00:00Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png"},"_id":"8097","date_created":"2020-07-06T20:40:19Z","file_date_updated":"2020-07-14T12:48:09Z","citation":{"chicago":"Kavcic, Bor. “Analysis Scripts and Research Data for the Paper ‘Mechanisms of Drug Interactions between Translation-Inhibiting Antibiotics.’” Institute of Science and Technology Austria, 2020. <a href=\"https://doi.org/10.15479/AT:ISTA:8097\">https://doi.org/10.15479/AT:ISTA:8097</a>.","ama":"Kavcic B. Analysis scripts and research data for the paper “Mechanisms of drug interactions between translation-inhibiting antibiotics.” 2020. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8097\">10.15479/AT:ISTA:8097</a>","ista":"Kavcic B. 2020. Analysis scripts and research data for the paper ‘Mechanisms of drug interactions between translation-inhibiting antibiotics’, Institute of Science and Technology Austria, <a href=\"https://doi.org/10.15479/AT:ISTA:8097\">10.15479/AT:ISTA:8097</a>.","short":"B. Kavcic, (2020).","ieee":"B. Kavcic, “Analysis scripts and research data for the paper ‘Mechanisms of drug interactions between translation-inhibiting antibiotics.’” Institute of Science and Technology Austria, 2020.","mla":"Kavcic, Bor. <i>Analysis Scripts and Research Data for the Paper “Mechanisms of Drug Interactions between Translation-Inhibiting Antibiotics.”</i> Institute of Science and Technology Austria, 2020, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8097\">10.15479/AT:ISTA:8097</a>.","apa":"Kavcic, B. (2020). Analysis scripts and research data for the paper “Mechanisms of drug interactions between translation-inhibiting antibiotics.” Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:8097\">https://doi.org/10.15479/AT:ISTA:8097</a>"},"status":"public","doi":"10.15479/AT:ISTA:8097","acknowledged_ssus":[{"_id":"LifeSc"}],"year":"2020","oa":1,"has_accepted_license":"1","contributor":[{"last_name":"Tkačik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","contributor_type":"research_group","first_name":"Gašper"},{"contributor_type":"research_group","first_name":"Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","last_name":"Bollenbach"}],"article_processing_charge":"No","department":[{"_id":"GaTk"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2024-02-21T12:40:51Z","fulldoi":"https://doi.org/10.15479/AT:ISTA:8097","day":"15","keyword":["Escherichia coli","antibiotic combinations","translation","growth laws","drug interactions","bacterial physiology","translation inhibitors"]},{"day":"11","volume":230,"publication":"EPJ Web of Conferences","oa":1,"intvolume":"       230","year":"2020","corr_author":"1","language":[{"iso":"eng"}],"file_date_updated":"2020-07-22T06:17:11Z","date_created":"2020-07-12T16:20:33Z","_id":"8105","title":"Power-law correlations and coupling of active and quiet states underlie a class of complex systems with self-organization at criticality","quality_controlled":"1","author":[{"orcid":"0000-0003-2623-5249","first_name":"Fabrizio","full_name":"Lombardi, Fabrizio","last_name":"Lombardi","id":"A057D288-3E88-11E9-986D-0CF4E5697425"},{"first_name":"Jilin W.J.L.","full_name":"Wang, Jilin W.J.L.","last_name":"Wang"},{"first_name":"Xiyun","full_name":"Zhang, Xiyun","last_name":"Zhang"},{"full_name":"Ivanov, Plamen Ch","last_name":"Ivanov","first_name":"Plamen Ch"}],"article_type":"original","publication_identifier":{"issn":["2100-014X"]},"publisher":"EDP Sciences","file":[{"file_size":2197543,"file_name":"2020_EPJWebConf_Lombardi.pdf","date_created":"2020-07-22T06:17:11Z","date_updated":"2020-07-22T06:17:11Z","content_type":"application/pdf","relation":"main_file","creator":"dernst","access_level":"open_access","file_id":"8144","success":1}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published","date_updated":"2025-06-12T07:19:19Z","fulldoi":"https://doi.org/10.1051/epjconf/202023000005","article_processing_charge":"No","has_accepted_license":"1","department":[{"_id":"GaTk"}],"pmid":1,"article_number":"00005","status":"public","doi":"10.1051/epjconf/202023000005","date_published":"2020-03-11T00:00:00Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png"},"citation":{"ieee":"F. Lombardi, J. W. J. L. Wang, X. Zhang, and P. C. Ivanov, “Power-law correlations and coupling of active and quiet states underlie a class of complex systems with self-organization at criticality,” <i>EPJ Web of Conferences</i>, vol. 230. EDP Sciences, 2020.","short":"F. Lombardi, J.W.J.L. Wang, X. Zhang, P.C. Ivanov, EPJ Web of Conferences 230 (2020).","ista":"Lombardi F, Wang JWJL, Zhang X, Ivanov PC. 2020. Power-law correlations and coupling of active and quiet states underlie a class of complex systems with self-organization at criticality. EPJ Web of Conferences. 230, 00005.","ama":"Lombardi F, Wang JWJL, Zhang X, Ivanov PC. Power-law correlations and coupling of active and quiet states underlie a class of complex systems with self-organization at criticality. <i>EPJ Web of Conferences</i>. 2020;230. doi:<a href=\"https://doi.org/10.1051/epjconf/202023000005\">10.1051/epjconf/202023000005</a>","chicago":"Lombardi, Fabrizio, Jilin W.J.L. Wang, Xiyun Zhang, and Plamen Ch Ivanov. “Power-Law Correlations and Coupling of Active and Quiet States Underlie a Class of Complex Systems with Self-Organization at Criticality.” <i>EPJ Web of Conferences</i>. EDP Sciences, 2020. <a href=\"https://doi.org/10.1051/epjconf/202023000005\">https://doi.org/10.1051/epjconf/202023000005</a>.","mla":"Lombardi, Fabrizio, et al. “Power-Law Correlations and Coupling of Active and Quiet States Underlie a Class of Complex Systems with Self-Organization at Criticality.” <i>EPJ Web of Conferences</i>, vol. 230, 00005, EDP Sciences, 2020, doi:<a href=\"https://doi.org/10.1051/epjconf/202023000005\">10.1051/epjconf/202023000005</a>.","apa":"Lombardi, F., Wang, J. W. J. L., Zhang, X., &#38; Ivanov, P. C. (2020). Power-law correlations and coupling of active and quiet states underlie a class of complex systems with self-organization at criticality. <i>EPJ Web of Conferences</i>. EDP Sciences. <a href=\"https://doi.org/10.1051/epjconf/202023000005\">https://doi.org/10.1051/epjconf/202023000005</a>"},"ddc":["530"],"oa_version":"Published Version","type":"journal_article","external_id":{"pmid":["32655977"]},"month":"03","abstract":[{"text":"Physical and biological systems often exhibit intermittent dynamics with bursts or avalanches (active states) characterized by power-law size and duration distributions. These emergent features are typical of systems at the critical point of continuous phase transitions, and have led to the hypothesis that such systems may self-organize at criticality, i.e. without any fine tuning of parameters. Since the introduction of the Bak-Tang-Wiesenfeld (BTW) model, the paradigm of self-organized criticality (SOC) has been very fruitful for the analysis of emergent collective behaviors in a number of systems, including the brain. Although considerable effort has been devoted in identifying and modeling scaling features of burst and avalanche statistics, dynamical aspects related to the temporal organization of bursts remain often poorly understood or controversial. Of crucial importance to understand the mechanisms responsible for emergent behaviors is the relationship between active and quiet periods, and the nature of the correlations. Here we investigate the dynamics of active (θ-bursts) and quiet states (δ-bursts) in brain activity during the sleep-wake cycle. We show the duality of power-law (θ, active phase) and exponential-like (δ, quiescent phase) duration distributions, typical of SOC, jointly emerge with power-law temporal correlations and anti-correlated coupling between active and quiet states. Importantly, we demonstrate that such temporal organization shares important similarities with earthquake dynamics, and propose that specific power-law correlations and coupling between active and quiet states are distinctive characteristics of a class of systems with self-organization at criticality.","lang":"eng"}]},{"abstract":[{"text":"The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is the ability to estimate the surprise or likelihood of incoming high-dimensional neural activity patterns. Despite progress in statistical modeling of neural responses and deep learning, current approaches either do not scale to large neural populations or cannot be implemented using biologically realistic mechanisms. Inspired by the sparse and random connectivity of real neuronal circuits, we present a model for neural codes that accurately estimates the likelihood of individual spiking patterns and has a straightforward, scalable, efficient, learnable, and realistic neural implementation. This model’s performance on simultaneously recorded spiking activity of >100 neurons in the monkey visual and prefrontal cortices is comparable with or better than that of state-of-the-art models. Importantly, the model can be learned using a small number of samples and using a local learning rule that utilizes noise intrinsic to neural circuits. Slower, structural changes in random connectivity, consistent with rewiring and pruning processes, further improve the efficiency and sparseness of the resulting neural representations. Our results merge insights from neuroanatomy, machine learning, and theoretical neuroscience to suggest random sparse connectivity as a key design principle for neuronal computation.","lang":"eng"}],"month":"10","type":"journal_article","external_id":{"isi":["000579045200012"],"pmid":["32948691"]},"oa_version":"Published Version","ddc":["570"],"citation":{"ieee":"O. Maoz, G. Tkačik, M. S. Esteki, R. Kiani, and E. Schneidman, “Learning probabilistic neural representations with randomly connected circuits,” <i>Proceedings of the National Academy of Sciences of the United States of America</i>, vol. 117, no. 40. National Academy of Sciences, pp. 25066–25073, 2020.","short":"O. Maoz, G. Tkačik, M.S. Esteki, R. Kiani, E. Schneidman, Proceedings of the National Academy of Sciences of the United States of America 117 (2020) 25066–25073.","chicago":"Maoz, Ori, Gašper Tkačik, Mohamad Saleh Esteki, Roozbeh Kiani, and Elad Schneidman. “Learning Probabilistic Neural Representations with Randomly Connected Circuits.” <i>Proceedings of the National Academy of Sciences of the United States of America</i>. National Academy of Sciences, 2020. <a href=\"https://doi.org/10.1073/pnas.1912804117\">https://doi.org/10.1073/pnas.1912804117</a>.","ista":"Maoz O, Tkačik G, Esteki MS, Kiani R, Schneidman E. 2020. Learning probabilistic neural representations with randomly connected circuits. Proceedings of the National Academy of Sciences of the United States of America. 117(40), 25066–25073.","ama":"Maoz O, Tkačik G, Esteki MS, Kiani R, Schneidman E. Learning probabilistic neural representations with randomly connected circuits. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. 2020;117(40):25066-25073. doi:<a href=\"https://doi.org/10.1073/pnas.1912804117\">10.1073/pnas.1912804117</a>","mla":"Maoz, Ori, et al. “Learning Probabilistic Neural Representations with Randomly Connected Circuits.” <i>Proceedings of the National Academy of Sciences of the United States of America</i>, vol. 117, no. 40, National Academy of Sciences, 2020, pp. 25066–73, doi:<a href=\"https://doi.org/10.1073/pnas.1912804117\">10.1073/pnas.1912804117</a>.","apa":"Maoz, O., Tkačik, G., Esteki, M. S., Kiani, R., &#38; Schneidman, E. (2020). Learning probabilistic neural representations with randomly connected circuits. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1912804117\">https://doi.org/10.1073/pnas.1912804117</a>"},"tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)","image":"/images/cc_by_nc_nd.png"},"date_published":"2020-10-06T00:00:00Z","isi":1,"doi":"10.1073/pnas.1912804117","status":"public","pmid":1,"department":[{"_id":"GaTk"}],"has_accepted_license":"1","article_processing_charge":"No","fulldoi":"https://doi.org/10.1073/pnas.1912804117","date_updated":"2025-07-10T11:57:16Z","publication_status":"published","issue":"40","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","scopus_import":"1","file":[{"success":1,"checksum":"c6a24fdecf3f28faf447078e7a274a88","file_id":"8713","access_level":"open_access","relation":"main_file","creator":"cziletti","content_type":"application/pdf","date_created":"2020-10-27T14:57:50Z","date_updated":"2020-10-27T14:57:50Z","file_name":"2020_PNAS_Maoz.pdf","file_size":1755359}],"publisher":"National Academy of Sciences","publication_identifier":{"eissn":["1091-6490"],"issn":["0027-8424"]},"author":[{"full_name":"Maoz, Ori","last_name":"Maoz","first_name":"Ori"},{"full_name":"Tkačik, Gašper","last_name":"Tkačik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","first_name":"Gašper"},{"last_name":"Esteki","full_name":"Esteki, Mohamad Saleh","first_name":"Mohamad Saleh"},{"first_name":"Roozbeh","full_name":"Kiani, Roozbeh","last_name":"Kiani"},{"last_name":"Schneidman","full_name":"Schneidman, Elad","first_name":"Elad"}],"article_type":"original","quality_controlled":"1","title":"Learning probabilistic neural representations with randomly connected circuits","_id":"8698","date_created":"2020-10-25T23:01:16Z","page":"25066-25073","file_date_updated":"2020-10-27T14:57:50Z","language":[{"iso":"eng"}],"year":"2020","intvolume":"       117","oa":1,"publication":"Proceedings of the National Academy of Sciences of the United States of America","volume":117,"day":"06","acknowledgement":"We thank Udi Karpas, Roy Harpaz, Tal Tamir, Adam Haber, and Amir Bar for discussions and suggestions; and especially Oren Forkosh and Walter Senn for invaluable discussions of the learning rule. This work was supported by European Research Council Grant 311238 (to E.S.) and Israel Science Foundation Grant 1629/12 (to E.S.); as well as research support from Martin Kushner Schnur and Mr. and Mrs. Lawrence Feis (E.S.); National Institute of Mental Health Grant R01MH109180 (to R.K.); a Pew Scholarship in Biomedical Sciences (to R.K.); Simons Collaboration on the Global Brain Grant 542997 (to R.K. and E.S.); and a CRCNS (Collaborative Research in Computational Neuroscience) grant (to R.K. and E.S.)."},{"author":[{"id":"350F91D2-F248-11E8-B48F-1D18A9856A87","last_name":"Kavcic","full_name":"Kavcic, Bor","first_name":"Bor","orcid":"0000-0001-6041-254X"}],"type":"research_data","title":"Analysis scripts and research data for the paper \"Minimal biophysical model of combined antibiotic action\"","oa_version":"Published Version","abstract":[{"lang":"eng","text":"Phenomenological relations such as Ohm’s or Fourier’s law have a venerable history in physics but are still scarce in biology. This situation restrains predictive theory. Here, we build on bacterial “growth laws,” which capture physiological feedback between translation and cell growth, to construct a minimal biophysical model for the combined action of ribosome-targeting antibiotics. Our model predicts drug interactions like antagonism or synergy solely from responses to individual drugs. We provide analytical results for limiting cases, which agree well with numerical results. We systematically refine the model by including direct physical interactions of different antibiotics on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions that were derived using entropy maximization. We further refine the model to include the effects of antibiotics that mimic starvation and the presence of resistance genes. We describe the impact of a starvation-mimicking antibiotic on drug interactions analytically and verify it experimentally. Our extended model suggests a change in the type of drug interaction that depends on the strength of resistance, which challenges established rescaling paradigms. We experimentally show that the presence of unregulated resistance genes can lead to altered drug interaction, which agrees with the prediction of the model. While minimal, the model is readily adaptable and opens the door to predicting interactions of second and higher-order in a broad range of biological systems."}],"related_material":{"record":[{"status":"public","id":"8997","relation":"used_in_publication"}]},"file":[{"checksum":"60a818edeffaa7da1ebf5f8fbea9ba18","success":1,"access_level":"open_access","file_id":"8932","relation":"main_file","creator":"bkavcic","date_created":"2020-12-09T15:00:19Z","date_updated":"2020-12-09T15:00:19Z","file_name":"PLoSCompBiol2020_datarep.zip","content_type":"application/zip","file_size":315494370}],"month":"12","publisher":"Institute of Science and Technology Austria","doi":"10.15479/AT:ISTA:8930","status":"public","_id":"8930","date_created":"2020-12-09T15:04:02Z","citation":{"mla":"Kavcic, Bor. <i>Analysis Scripts and Research Data for the Paper “Minimal Biophysical Model of Combined Antibiotic Action.”</i> Institute of Science and Technology Austria, 2020, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8930\">10.15479/AT:ISTA:8930</a>.","apa":"Kavcic, B. (2020). Analysis scripts and research data for the paper “Minimal biophysical model of combined antibiotic action.” Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:8930\">https://doi.org/10.15479/AT:ISTA:8930</a>","ama":"Kavcic B. Analysis scripts and research data for the paper “Minimal biophysical model of combined antibiotic action.” 2020. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8930\">10.15479/AT:ISTA:8930</a>","ista":"Kavcic B. 2020. Analysis scripts and research data for the paper ‘Minimal biophysical model of combined antibiotic action’, Institute of Science and Technology Austria, <a href=\"https://doi.org/10.15479/AT:ISTA:8930\">10.15479/AT:ISTA:8930</a>.","chicago":"Kavcic, Bor. “Analysis Scripts and Research Data for the Paper ‘Minimal Biophysical Model of Combined Antibiotic Action.’” Institute of Science and Technology Austria, 2020. <a href=\"https://doi.org/10.15479/AT:ISTA:8930\">https://doi.org/10.15479/AT:ISTA:8930</a>.","short":"B. Kavcic, (2020).","ieee":"B. Kavcic, “Analysis scripts and research data for the paper ‘Minimal biophysical model of combined antibiotic action.’” Institute of Science and Technology Austria, 2020."},"ddc":["570"],"file_date_updated":"2020-12-09T15:00:19Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png"},"date_published":"2020-12-10T00:00:00Z","department":[{"_id":"GaTk"}],"oa":1,"has_accepted_license":"1","article_processing_charge":"No","contributor":[{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkačik","first_name":"Gašper","contributor_type":"supervisor","orcid":"0000-0002-6699-1455"},{"first_name":"Tobias","contributor_type":"supervisor","last_name":"Bollenbach","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87"}],"corr_author":"1","year":"2020","day":"10","keyword":["Escherichia coli","antibiotic combinations","translation","growth laws","drug interactions","bacterial physiology","translation inhibitors"],"fulldoi":"https://doi.org/10.15479/AT:ISTA:8930","date_updated":"2025-06-12T06:33:18Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"}]