[{"type":"journal_article","acknowledgement":"We would like to thank Chaitanya Chintaluri, Nicoleta Condruz and Douglas Feitosa Tomé for insightful discussions.","author":[{"last_name":"Confavreux","id":"C7610134-B532-11EA-BD9F-F5753DDC885E","full_name":"Confavreux, Basile J","first_name":"Basile J"},{"first_name":"Everton J.","last_name":"Agnes","full_name":"Agnes, Everton J."},{"first_name":"Friedemann","full_name":"Zenke, Friedemann","last_name":"Zenke"},{"first_name":"Henning","full_name":"Sprekeler, Henning","last_name":"Sprekeler"},{"full_name":"Vogels, Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","last_name":"Vogels","first_name":"Tim P","orcid":"0000-0003-3295-6181"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","file_date_updated":"2025-05-05T11:17:49Z","issue":"4","corr_author":"1","OA_place":"publisher","_id":"19640","DOAJ_listed":"1","oa":1,"related_material":{"link":[{"url":"https://github.com/VogelsLab/SpikES","relation":"software"}]},"pmid":1,"intvolume":"        21","file":[{"file_size":9771636,"relation":"main_file","success":1,"access_level":"open_access","checksum":"6437a1aab52813ab7e310e3b4fb36e3b","date_created":"2025-05-05T11:17:49Z","file_id":"19654","creator":"dernst","content_type":"application/pdf","file_name":"2025_PLoSCompBio_Confavreux.pdf","date_updated":"2025-05-05T11:17:49Z"}],"ddc":["570"],"has_accepted_license":"1","language":[{"iso":"eng"}],"publication":"PLoS Computational Biology","date_created":"2025-05-04T22:02:31Z","scopus_import":"1","status":"public","title":"Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks","isi":1,"article_number":"e1012910","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"license":"https://creativecommons.org/licenses/by/4.0/","month":"04","department":[{"_id":"TiVo"}],"date_updated":"2025-09-30T12:22:33Z","publication_status":"published","doi":"10.1371/journal.pcbi.1012910","article_processing_charge":"Yes","publication_identifier":{"issn":["1553-734X"],"eissn":["1553-7358"]},"publisher":"Public Library of Science","citation":{"short":"B.J. Confavreux, E.J. Agnes, F. Zenke, H. Sprekeler, T.P. Vogels, PLoS Computational Biology 21 (2025).","apa":"Confavreux, B. J., Agnes, E. J., Zenke, F., Sprekeler, H., &#38; Vogels, T. P. (2025). Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1012910\">https://doi.org/10.1371/journal.pcbi.1012910</a>","ama":"Confavreux BJ, Agnes EJ, Zenke F, Sprekeler H, Vogels TP. Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks. <i>PLoS Computational Biology</i>. 2025;21(4). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1012910\">10.1371/journal.pcbi.1012910</a>","chicago":"Confavreux, Basile J, Everton J. Agnes, Friedemann Zenke, Henning Sprekeler, and Tim P Vogels. “Balancing Complexity, Performance and Plausibility to Meta Learn Plasticity Rules in Recurrent Spiking Networks.” <i>PLoS Computational Biology</i>. Public Library of Science, 2025. <a href=\"https://doi.org/10.1371/journal.pcbi.1012910\">https://doi.org/10.1371/journal.pcbi.1012910</a>.","ista":"Confavreux BJ, Agnes EJ, Zenke F, Sprekeler H, Vogels TP. 2025. Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks. PLoS Computational Biology. 21(4), e1012910.","ieee":"B. J. Confavreux, E. J. Agnes, F. Zenke, H. Sprekeler, and T. P. Vogels, “Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks,” <i>PLoS Computational Biology</i>, vol. 21, no. 4. Public Library of Science, 2025.","mla":"Confavreux, Basile J., et al. “Balancing Complexity, Performance and Plausibility to Meta Learn Plasticity Rules in Recurrent Spiking Networks.” <i>PLoS Computational Biology</i>, vol. 21, no. 4, e1012910, Public Library of Science, 2025, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1012910\">10.1371/journal.pcbi.1012910</a>."},"year":"2025","date_published":"2025-04-24T00:00:00Z","OA_type":"gold","abstract":[{"lang":"eng","text":"Synaptic plasticity is a key player in the brain’s life-long learning abilities. However, due to experimental limitations, the mechanistic link between synaptic plasticity rules and the network-level computations they enable remain opaque. Here we use evolutionary strategies (ES) to meta learn local co-active plasticity rules in large recurrent spiking networks with excitatory (E) and inhibitory (I) neurons, using parameterizations of increasing complexity. We discover rules that robustly stabilize network dynamics for all four synapse types acting in isolation (E-to-E, E-to-I, I-to-E and I-to-I). More complex functions such as familiarity detection can also be included in the search constraints. However, our meta learning strategy begins to fail for co-active rules of increasing complexity, as it is challenging to devise loss functions that effectively constrain network dynamics to plausible solutions a priori. Moreover, in line with previous work, we can find multiple degenerate solutions with identical network behaviour. As a local optimization strategy, ES provides one solution at a time and makes exploration of this degeneracy cumbersome. Regardless, we can glean the interdependecies of various plasticity parameters by considering the covariance matrix learned alongside the optimal rule with ES. Our work provides a proof of principle for the success of machine-learning-guided discovery of plasticity rules in large spiking networks, and points at the necessity of more elaborate search strategies going forward."}],"article_type":"original","quality_controlled":"1","external_id":{"isi":["001474257000002"],"pmid":["40273284 "]},"oa_version":"Published Version","day":"24","volume":21},{"quality_controlled":"1","external_id":{"pmid":["38484020"],"isi":["001190689800001"]},"volume":20,"oa_version":"Published Version","day":"14","OA_type":"gold","article_type":"original","abstract":[{"text":"Interpretation of extracellular recordings can be challenging due to the long range of electric field. This challenge can be mitigated by estimating the current source density (CSD). Here we introduce kCSD-python, an open Python package implementing Kernel Current Source Density (kCSD) method and related tools to facilitate CSD analysis of experimental data and the interpretation of results. We show how to counter the limitations imposed by noise and assumptions in the method itself. kCSD-python allows CSD estimation for an arbitrary distribution of electrodes in 1D, 2D, and 3D, assuming distributions of sources in tissue, a slice, or in a single cell, and includes a range of diagnostic aids. We demonstrate its features in a Jupyter Notebook tutorial which illustrates a typical analytical workflow and main functionalities useful in validating analysis results.","lang":"eng"}],"publisher":"Public Library of Science","publication_identifier":{"eissn":["1553-7358"],"issn":["1553-734X"]},"article_processing_charge":"Yes","doi":"10.1371/journal.pcbi.1011941","publication_status":"published","date_published":"2024-03-14T00:00:00Z","year":"2024","citation":{"ista":"Chintaluri C, Bejtka M, Sredniawa W, Czerwinski M, Dzik JM, Jedrzejewska-Szmek J, Wojciki DK. 2024. kCSD-python, reliable current source density estimation with quality control. PLoS Computational Biology. 20(3), e1011941.","ieee":"C. Chintaluri <i>et al.</i>, “kCSD-python, reliable current source density estimation with quality control,” <i>PLoS Computational Biology</i>, vol. 20, no. 3. Public Library of Science, 2024.","mla":"Chintaluri, Chaitanya, et al. “KCSD-Python, Reliable Current Source Density Estimation with Quality Control.” <i>PLoS Computational Biology</i>, vol. 20, no. 3, e1011941, Public Library of Science, 2024, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1011941\">10.1371/journal.pcbi.1011941</a>.","short":"C. Chintaluri, M. Bejtka, W. Sredniawa, M. Czerwinski, J.M. Dzik, J. Jedrzejewska-Szmek, D.K. Wojciki, PLoS Computational Biology 20 (2024).","apa":"Chintaluri, C., Bejtka, M., Sredniawa, W., Czerwinski, M., Dzik, J. M., Jedrzejewska-Szmek, J., &#38; Wojciki, D. K. (2024). kCSD-python, reliable current source density estimation with quality control. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1011941\">https://doi.org/10.1371/journal.pcbi.1011941</a>","ama":"Chintaluri C, Bejtka M, Sredniawa W, et al. kCSD-python, reliable current source density estimation with quality control. <i>PLoS Computational Biology</i>. 2024;20(3). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1011941\">10.1371/journal.pcbi.1011941</a>","chicago":"Chintaluri, Chaitanya, Marta Bejtka, Wladyslaw Sredniawa, Michal Czerwinski, Jakub M. Dzik, Joanna Jedrzejewska-Szmek, and Daniel K. Wojciki. “KCSD-Python, Reliable Current Source Density Estimation with Quality Control.” <i>PLoS Computational Biology</i>. Public Library of Science, 2024. <a href=\"https://doi.org/10.1371/journal.pcbi.1011941\">https://doi.org/10.1371/journal.pcbi.1011941</a>."},"isi":1,"article_number":"e1011941","title":"kCSD-python, reliable current source density estimation with quality control","date_updated":"2025-09-04T13:08:54Z","department":[{"_id":"TiVo"}],"month":"03","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"publication":"PLoS Computational Biology","language":[{"iso":"eng"}],"has_accepted_license":"1","date_created":"2024-03-24T23:00:59Z","scopus_import":"1","status":"public","intvolume":"        20","pmid":1,"ddc":["000","570"],"file":[{"file_name":"2024_PLoSCompBio_Chintaluri.pdf","date_updated":"2025-06-25T05:47:36Z","content_type":"application/pdf","creator":"dernst","file_id":"19897","date_created":"2025-06-25T05:47:36Z","checksum":"c09718d0d09614642d877d0716ce32e8","success":1,"access_level":"open_access","file_size":2540277,"relation":"main_file"}],"DOAJ_listed":"1","_id":"15169","OA_place":"publisher","oa":1,"related_material":{"link":[{"relation":"software","url":"https://github.com/Neuroinflab/kCSD-python"}]},"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","acknowledgement":"The Python implementation of kCSD was started by Grzegorz Parka during Google Summer of Code project through the International Neuroinformatics Coordinating Facility. Jan Mąka implemented the first Python version of skCSD class. This work was supported by the Polish National Science Centre (2013/08/W/NZ4/00691 to DKW; 2015/17/B/ST7/04123 to DKW). ","author":[{"first_name":"Chaitanya","last_name":"Chintaluri","id":"E4EDB536-3485-11EA-98D2-20AF3DDC885E","full_name":"Chintaluri, Chaitanya"},{"first_name":"Marta","last_name":"Bejtka","full_name":"Bejtka, Marta"},{"last_name":"Sredniawa","full_name":"Sredniawa, Wladyslaw","first_name":"Wladyslaw"},{"first_name":"Michal","last_name":"Czerwinski","full_name":"Czerwinski, Michal"},{"full_name":"Dzik, Jakub M.","last_name":"Dzik","first_name":"Jakub M."},{"last_name":"Jedrzejewska-Szmek","full_name":"Jedrzejewska-Szmek, Joanna","first_name":"Joanna"},{"last_name":"Wojciki","full_name":"Wojciki, Daniel K.","first_name":"Daniel K."}],"type":"journal_article","corr_author":"1","issue":"3","file_date_updated":"2025-06-25T05:47:36Z"},{"APC_amount":"3149,96 EUR","date_created":"2024-04-07T22:00:55Z","status":"public","scopus_import":"1","has_accepted_license":"1","language":[{"iso":"eng"}],"publication":"PLoS Computational Biology","file":[{"date_updated":"2024-08-20T10:52:28Z","file_name":"2024_PloSComBio_Svoboda.pdf","content_type":"application/pdf","creator":"dernst","file_id":"17450","date_created":"2024-08-20T10:52:28Z","checksum":"a511cf369d9172beb123fe73f291b5cc","success":1,"access_level":"open_access","relation":"main_file","file_size":1425292}],"ddc":["000"],"intvolume":"        20","related_material":{"record":[{"status":"public","id":"20138","relation":"dissertation_contains"}]},"oa":1,"OA_place":"publisher","_id":"15297","arxiv":1,"DOAJ_listed":"1","file_date_updated":"2024-08-20T10:52:28Z","issue":"3","corr_author":"1","type":"journal_article","author":[{"orcid":"0000-0002-1419-3267","first_name":"Jakub","last_name":"Svoboda","id":"130759D2-D7DD-11E9-87D2-DE0DE6697425","full_name":"Svoboda, Jakub"},{"last_name":"Joshi","id":"f97aac0e-f57c-11ee-93d0-a5a82d8df168","full_name":"Joshi, Soham Shrikant","first_name":"Soham Shrikant"},{"first_name":"Josef","orcid":"0000-0002-1097-9684","id":"3F24CCC8-F248-11E8-B48F-1D18A9856A87","full_name":"Tkadlec, Josef","last_name":"Tkadlec"},{"first_name":"Krishnendu","orcid":"0000-0002-4561-241X","last_name":"Chatterjee","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","full_name":"Chatterjee, Krishnendu"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","acknowledgement":"We thank Gavin Rees for helpful discussions. J.S., S.J., and K.C were supported by\r\nEuropean Research Council (ERC) CoG 863818 (ForM-SMArt). J.T was supported by Center for Foundations of Modern Computer Science (Charles University project UNCE/SCI/004) and by the project PRIMUS/24/SCI/012 from Charles University. ","volume":20,"day":"29","oa_version":"Published Version","ec_funded":1,"project":[{"grant_number":"863818","call_identifier":"H2020","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","name":"Formal Methods for Stochastic Models: Algorithms and Applications"}],"external_id":{"isi":["001194482400002"],"arxiv":["2401.14914"]},"quality_controlled":"1","abstract":[{"lang":"eng","text":"Populations evolve by accumulating advantageous mutations. Every population has some spatial structure that can be modeled by an underlying network. The network then influences the probability that new advantageous mutations fixate. Amplifiers of selection are networks that increase the fixation probability of advantageous mutants, as compared to the unstructured fully-connected network. Whether or not a network is an amplifier depends on the choice of the random process that governs the evolutionary dynamics. Two popular choices are Moran process with Birth-death updating and Moran process with death-Birth updating. Interestingly, while some networks are amplifiers under Birth-death updating and other networks are amplifiers under death-Birth updating, so far no spatial structures have been found that function as an amplifier under both types of updating simultaneously. In this work, we identify networks that act as amplifiers of selection under both versions of the Moran process. The amplifiers are robust, modular, and increase fixation probability for any mutant fitness advantage in a range r ∈ (1, 1.2). To complement this positive result, we also prove that for certain quantities closely related to fixation probability, it is impossible to improve them simultaneously for both versions of the Moran process. Together, our results highlight how the two versions of the Moran process differ and what they have in common."}],"article_type":"original","OA_type":"gold","citation":{"ama":"Svoboda J, Joshi SS, Tkadlec J, Chatterjee K. Amplifiers of selection for the Moran process with both Birth-death and death-Birth updating. <i>PLoS Computational Biology</i>. 2024;20(3). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1012008\">10.1371/journal.pcbi.1012008</a>","chicago":"Svoboda, Jakub, Soham Shrikant Joshi, Josef Tkadlec, and Krishnendu Chatterjee. “Amplifiers of Selection for the Moran Process with Both Birth-Death and Death-Birth Updating.” <i>PLoS Computational Biology</i>. Public Library of Science, 2024. <a href=\"https://doi.org/10.1371/journal.pcbi.1012008\">https://doi.org/10.1371/journal.pcbi.1012008</a>.","short":"J. Svoboda, S.S. Joshi, J. Tkadlec, K. Chatterjee, PLoS Computational Biology 20 (2024).","apa":"Svoboda, J., Joshi, S. S., Tkadlec, J., &#38; Chatterjee, K. (2024). Amplifiers of selection for the Moran process with both Birth-death and death-Birth updating. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1012008\">https://doi.org/10.1371/journal.pcbi.1012008</a>","ieee":"J. Svoboda, S. S. Joshi, J. Tkadlec, and K. Chatterjee, “Amplifiers of selection for the Moran process with both Birth-death and death-Birth updating,” <i>PLoS Computational Biology</i>, vol. 20, no. 3. Public Library of Science, 2024.","mla":"Svoboda, Jakub, et al. “Amplifiers of Selection for the Moran Process with Both Birth-Death and Death-Birth Updating.” <i>PLoS Computational Biology</i>, vol. 20, no. 3, e1012008, Public Library of Science, 2024, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1012008\">10.1371/journal.pcbi.1012008</a>.","ista":"Svoboda J, Joshi SS, Tkadlec J, Chatterjee K. 2024. Amplifiers of selection for the Moran process with both Birth-death and death-Birth updating. PLoS Computational Biology. 20(3), e1012008."},"date_published":"2024-03-29T00:00:00Z","year":"2024","publication_status":"published","doi":"10.1371/journal.pcbi.1012008","publication_identifier":{"issn":["1553-734X"],"eissn":["1553-7358"]},"article_processing_charge":"Yes","publisher":"Public Library of Science","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"month":"03","department":[{"_id":"KrCh"}],"date_updated":"2026-04-07T11:49:11Z","title":"Amplifiers of selection for the Moran process with both Birth-death and death-Birth updating","isi":1,"article_number":"e1012008"},{"author":[{"first_name":"Richard D.J.G.","last_name":"Ho","full_name":"Ho, Richard D.J.G."},{"first_name":"Kasumi","orcid":"0000-0001-6060-4795","last_name":"Kishi","full_name":"Kishi, Kasumi","id":"3065DFC4-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Maciej","full_name":"Majka, Maciej","last_name":"Majka"},{"last_name":"Kicheva","id":"3959A2A0-F248-11E8-B48F-1D18A9856A87","full_name":"Kicheva, Anna","first_name":"Anna","orcid":"0000-0003-4509-4998"},{"orcid":"0000-0001-7896-7762","first_name":"Marcin P","last_name":"Zagórski","full_name":"Zagórski, Marcin P","id":"343DA0DC-F248-11E8-B48F-1D18A9856A87"}],"acknowledgement":"We thank Martina Greunz-Schindler for technical support, and Thomas Minchington and James Briscoe for comments on the manuscript.\r\nRDJGH, MM and MZ were supported by a grant from the Priority Research Area DigiWorld\r\nunder the Strategic Programme Excellence Initiative at Jagiellonian University. The research\r\nwas supported by the Polish National Agency for Academic Exchange, PN/PPO/2018/1/00011/U/00001 which paid the salary of MM and MZ up to Feb 2023. The research received support from National Science Center, Poland, 2021/42/E/NZ2/00188 which paid salary of MZ. Work in the AK labis supported by ISTA to KK and AK, the European\r\nResearch Council under Horizon Europe: grant 101044579 to AK, and Austrian Science Fund\r\n(FWF): Grant DOI 10.55776/F78 to AK. The salaries of AK and KK were paid by ISTA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","type":"journal_article","corr_author":"1","file_date_updated":"2024-10-29T11:59:09Z","DOAJ_listed":"1","_id":"18481","OA_place":"publisher","related_material":{"record":[{"id":"20393","status":"public","relation":"dissertation_contains"}]},"oa":1,"intvolume":"        20","pmid":1,"ddc":["570"],"file":[{"file_size":3732443,"relation":"main_file","success":1,"access_level":"open_access","checksum":"42fa714459943cb3961b40fab8fd82c8","date_created":"2024-10-29T11:59:09Z","creator":"dernst","file_id":"18487","content_type":"application/pdf","file_name":"2024_PloSComBio_Ho.pdf","date_updated":"2024-10-29T11:59:09Z"}],"publication":"PLoS Computational Biology","language":[{"iso":"eng"}],"has_accepted_license":"1","date_created":"2024-10-27T23:01:45Z","status":"public","scopus_import":"1","APC_amount":"3197,23 EUR","isi":1,"article_number":"e1012508","title":"Dynamics of morphogen source formation in a growing tissue","date_updated":"2026-04-07T12:31:58Z","department":[{"_id":"AnKi"}],"month":"10","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"article_processing_charge":"No","publication_identifier":{"issn":["1553-734X"],"eissn":["1553-7358"]},"publisher":"Public Library of Science","doi":"10.1371/journal.pcbi.1012508","publication_status":"published","year":"2024","date_published":"2024-10-14T00:00:00Z","citation":{"short":"R.D.J.G. Ho, K. Kishi, M. Majka, A. Kicheva, M.P. Zagórski, PLoS Computational Biology 20 (2024).","apa":"Ho, R. D. J. G., Kishi, K., Majka, M., Kicheva, A., &#38; Zagórski, M. P. (2024). Dynamics of morphogen source formation in a growing tissue. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1012508\">https://doi.org/10.1371/journal.pcbi.1012508</a>","ama":"Ho RDJG, Kishi K, Majka M, Kicheva A, Zagórski MP. Dynamics of morphogen source formation in a growing tissue. <i>PLoS Computational Biology</i>. 2024;20. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1012508\">10.1371/journal.pcbi.1012508</a>","chicago":"Ho, Richard D.J.G., Kasumi Kishi, Maciej Majka, Anna Kicheva, and Marcin P Zagórski. “Dynamics of Morphogen Source Formation in a Growing Tissue.” <i>PLoS Computational Biology</i>. Public Library of Science, 2024. <a href=\"https://doi.org/10.1371/journal.pcbi.1012508\">https://doi.org/10.1371/journal.pcbi.1012508</a>.","ista":"Ho RDJG, Kishi K, Majka M, Kicheva A, Zagórski MP. 2024. Dynamics of morphogen source formation in a growing tissue. PLoS Computational Biology. 20, e1012508.","ieee":"R. D. J. G. Ho, K. Kishi, M. Majka, A. Kicheva, and M. P. Zagórski, “Dynamics of morphogen source formation in a growing tissue,” <i>PLoS Computational Biology</i>, vol. 20. Public Library of Science, 2024.","mla":"Ho, Richard D. J. G., et al. “Dynamics of Morphogen Source Formation in a Growing Tissue.” <i>PLoS Computational Biology</i>, vol. 20, e1012508, Public Library of Science, 2024, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1012508\">10.1371/journal.pcbi.1012508</a>."},"OA_type":"gold","article_type":"original","abstract":[{"text":"A tight regulation of morphogen production is key for morphogen gradient formation and thereby for reproducible and organised organ development. Although many genetic interactions involved in the establishment of morphogen production domains are known, the biophysical mechanisms of morphogen source formation are poorly understood. Here we addressed this by focusing on the morphogen Sonic hedgehog (Shh) in the vertebrate neural tube. Shh is produced by the adjacently located notochord and by the floor plate of the neural tube. Using a data-constrained computational screen, we identified different possible mechanisms by which floor plate formation can occur, only one of which is consistent with experimental data. In this mechanism, the floor plate is established rapidly in response to Shh from the notochord and the dynamics of regulatory interactions within the neural tube. In this process, uniform activators and Shh-dependent repressors are key for establishing the floor plate size. Subsequently, the floor plate becomes insensitive to Shh and increases in size due to tissue growth, leading to scaling of the floor plate with neural tube size. In turn, this results in scaling of the Shh amplitude with tissue growth. Thus, this mechanism ensures a separation of time scales in floor plate formation, so that the floor plate domain becomes growth-dependent after an initial rapid establishment phase. Our study raises the possibility that the time scale separation between specification and growth might be a common strategy for scaling the morphogen gradient amplitude in growing organs. The model that we developed provides a new opportunity for quantitative studies of morphogen source formation in growing tissues.","lang":"eng"}],"quality_controlled":"1","external_id":{"isi":["001331700300003"],"pmid":["39401260"]},"project":[{"grant_number":"101044579","_id":"bd7e737f-d553-11ed-ba76-d69ffb5ee3aa","name":"Mechanisms of tissue size regulation in spinal cord development"},{"grant_number":"F7802","name":"Stem Cell Modulation in Neural Development and Regeneration/ P02-Morphogen control of growth and pattern in the spinal cord","_id":"059DF620-7A3F-11EA-A408-12923DDC885E"}],"volume":20,"day":"14","oa_version":"Published Version"},{"year":"2022","date_published":"2022-03-18T00:00:00Z","citation":{"ista":"Davidović A, Chait RP, Batt G, Ruess J. 2022. Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level. PLoS Computational Biology. 18(3), e1009950.","mla":"Davidović, Anđela, et al. “Parameter Inference for Stochastic Biochemical Models from Perturbation Experiments Parallelised at the Single Cell Level.” <i>PLoS Computational Biology</i>, vol. 18, no. 3, e1009950, Public Library of Science, 2022, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1009950\">10.1371/journal.pcbi.1009950</a>.","ieee":"A. Davidović, R. P. Chait, G. Batt, and J. Ruess, “Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level,” <i>PLoS Computational Biology</i>, vol. 18, no. 3. Public Library of Science, 2022.","apa":"Davidović, A., Chait, R. P., Batt, G., &#38; Ruess, J. (2022). Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1009950\">https://doi.org/10.1371/journal.pcbi.1009950</a>","short":"A. Davidović, R.P. Chait, G. Batt, J. Ruess, PLoS Computational Biology 18 (2022).","chicago":"Davidović, Anđela, Remy P Chait, Gregory Batt, and Jakob Ruess. “Parameter Inference for Stochastic Biochemical Models from Perturbation Experiments Parallelised at the Single Cell Level.” <i>PLoS Computational Biology</i>. Public Library of Science, 2022. <a href=\"https://doi.org/10.1371/journal.pcbi.1009950\">https://doi.org/10.1371/journal.pcbi.1009950</a>.","ama":"Davidović A, Chait RP, Batt G, Ruess J. Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level. <i>PLoS Computational Biology</i>. 2022;18(3). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1009950\">10.1371/journal.pcbi.1009950</a>"},"publication_status":"published","publisher":"Public Library of Science","article_processing_charge":"No","publication_identifier":{"eissn":["1553-7358"],"issn":["1553-734X"]},"doi":"10.1371/journal.pcbi.1009950","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"date_updated":"2025-09-09T14:29:53Z","department":[{"_id":"CaGu"}],"month":"03","title":"Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level","article_number":"e1009950","isi":1,"oa_version":"Published Version","day":"18","volume":18,"external_id":{"pmid":["35303737"],"isi":["001044208400004"]},"quality_controlled":"1","abstract":[{"lang":"eng","text":"Understanding and characterising biochemical processes inside single cells requires experimental platforms that allow one to perturb and observe the dynamics of such processes as well as computational methods to build and parameterise models from the collected data. Recent progress with experimental platforms and optogenetics has made it possible to expose each cell in an experiment to an individualised input and automatically record cellular responses over days with fine time resolution. However, methods to infer parameters of stochastic kinetic models from single-cell longitudinal data have generally been developed under the assumption that experimental data is sparse and that responses of cells to at most a few different input perturbations can be observed. Here, we investigate and compare different approaches for calculating parameter likelihoods of single-cell longitudinal data based on approximations of the chemical master equation (CME) with a particular focus on coupling the linear noise approximation (LNA) or moment closure methods to a Kalman filter. We show that, as long as cells are measured sufficiently frequently, coupling the LNA to a Kalman filter allows one to accurately approximate likelihoods and to infer model parameters from data even in cases where the LNA provides poor approximations of the CME. Furthermore, the computational cost of filtering-based iterative likelihood evaluation scales advantageously in the number of measurement times and different input perturbations and is thus ideally suited for data obtained from modern experimental platforms. To demonstrate the practical usefulness of these results, we perform an experiment in which single cells, equipped with an optogenetic gene expression system, are exposed to various different light-input sequences and measured at several hundred time points and use parameter inference based on iterative likelihood evaluation to parameterise a stochastic model of the system."}],"article_type":"original","oa":1,"related_material":{"link":[{"relation":"software","url":"https://gitlab.pasteur.fr/adavidov/inferencelnakf"}]},"_id":"10939","file_date_updated":"2022-04-04T10:14:39Z","issue":"3","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","author":[{"full_name":"Davidović, Anđela","last_name":"Davidović","first_name":"Anđela"},{"id":"3464AE84-F248-11E8-B48F-1D18A9856A87","full_name":"Chait, Remy P","last_name":"Chait","orcid":"0000-0003-0876-3187","first_name":"Remy P"},{"first_name":"Gregory","last_name":"Batt","full_name":"Batt, Gregory"},{"full_name":"Ruess, Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","last_name":"Ruess","orcid":"0000-0003-1615-3282","first_name":"Jakob"}],"acknowledgement":"We thank Virgile Andreani for useful discussions about the model and parameter inference. We thank Johan Paulsson and Jeffrey J Tabor for kind gifts of plasmids. R was supported by the ANR grant CyberCircuits (ANR-18-CE91-0002). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","type":"journal_article","status":"public","date_created":"2022-04-03T22:01:42Z","scopus_import":"1","has_accepted_license":"1","publication":"PLoS Computational Biology","language":[{"iso":"eng"}],"file":[{"file_name":"2022_PLoSCompBio_Davidovic.pdf","date_updated":"2022-04-04T10:14:39Z","content_type":"application/pdf","creator":"dernst","file_id":"10947","date_created":"2022-04-04T10:14:39Z","checksum":"458ef542761fb714ced214f240daf6b2","success":1,"access_level":"open_access","file_size":2958642,"relation":"main_file"}],"ddc":["570","000"],"pmid":1,"intvolume":"        18"},{"quality_controlled":"1","external_id":{"arxiv":["2102.03669"],"pmid":["34851948"]},"oa_version":"Published Version","volume":17,"day":"01","abstract":[{"lang":"eng","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."}],"article_type":"original","publication_status":"published","publisher":"Public Library of Science","publication_identifier":{"eissn":["1553-7358"],"issn":["1553-734X"]},"article_processing_charge":"No","doi":"10.1371/journal.pcbi.1009661","year":"2021","date_published":"2021-12-01T00:00:00Z","citation":{"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>","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>.","short":"K. Bodova, E. Szep, N.H. Barton, PLoS Computational Biology 17 (2021).","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>","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.","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>.","ista":"Bodova K, Szep E, Barton NH. 2021. Dynamic maximum entropy provides accurate approximation of structured population dynamics. PLoS Computational Biology. 17(12), e1009661."},"title":"Dynamic maximum entropy provides accurate approximation of structured population dynamics","article_number":"e1009661","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"date_updated":"2024-10-09T21:01:16Z","month":"12","department":[{"_id":"NiBa"},{"_id":"GaTk"}],"acknowledged_ssus":[{"_id":"ScienComp"}],"has_accepted_license":"1","publication":"PLoS Computational Biology","language":[{"iso":"eng"}],"date_created":"2021-12-12T23:01:27Z","status":"public","scopus_import":"1","pmid":1,"intvolume":"        17","file":[{"file_id":"11383","creator":"dernst","date_updated":"2022-05-16T08:53:11Z","file_name":"2021_PLOsComBio_Bodova.pdf","content_type":"application/pdf","success":1,"access_level":"open_access","relation":"main_file","file_size":2299486,"date_created":"2022-05-16T08:53:11Z","checksum":"dcd185d4f7e0acee25edf1d6537f447e"}],"ddc":["570"],"_id":"10535","arxiv":1,"oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"first_name":"Katarína","orcid":"0000-0002-7214-0171","last_name":"Bod'ová","full_name":"Bod'ová, Katarína","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Eniko","last_name":"Szep","id":"485BB5A4-F248-11E8-B48F-1D18A9856A87","full_name":"Szep, Eniko"},{"full_name":"Barton, Nicholas H","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","last_name":"Barton","first_name":"Nicholas H","orcid":"0000-0002-8548-5240"}],"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.","type":"journal_article","file_date_updated":"2022-05-16T08:53:11Z","corr_author":"1","issue":"12"},{"article_number":"e1008523","isi":1,"title":"Mistakes can stabilise the dynamics of rock-paper-scissors games","month":"04","department":[{"_id":"KrCh"}],"date_updated":"2025-06-12T06:40:39Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"doi":"10.1371/journal.pcbi.1008523","publication_identifier":{"issn":["1553-734X"],"eissn":["1553-7358"]},"publisher":"Public Library of Science","article_processing_charge":"No","publication_status":"published","citation":{"chicago":"Kleshnina, Maria, Sabrina S. Streipert, Jerzy A. Filar, and Krishnendu Chatterjee. “Mistakes Can Stabilise the Dynamics of Rock-Paper-Scissors Games.” <i>PLoS Computational Biology</i>. Public Library of Science, 2021. <a href=\"https://doi.org/10.1371/journal.pcbi.1008523\">https://doi.org/10.1371/journal.pcbi.1008523</a>.","ama":"Kleshnina M, Streipert SS, Filar JA, Chatterjee K. Mistakes can stabilise the dynamics of rock-paper-scissors games. <i>PLoS Computational Biology</i>. 2021;17(4). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1008523\">10.1371/journal.pcbi.1008523</a>","apa":"Kleshnina, M., Streipert, S. S., Filar, J. A., &#38; Chatterjee, K. (2021). Mistakes can stabilise the dynamics of rock-paper-scissors games. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1008523\">https://doi.org/10.1371/journal.pcbi.1008523</a>","short":"M. Kleshnina, S.S. Streipert, J.A. Filar, K. Chatterjee, PLoS Computational Biology 17 (2021).","mla":"Kleshnina, Maria, et al. “Mistakes Can Stabilise the Dynamics of Rock-Paper-Scissors Games.” <i>PLoS Computational Biology</i>, vol. 17, no. 4, e1008523, Public Library of Science, 2021, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1008523\">10.1371/journal.pcbi.1008523</a>.","ieee":"M. Kleshnina, S. S. Streipert, J. A. Filar, and K. Chatterjee, “Mistakes can stabilise the dynamics of rock-paper-scissors games,” <i>PLoS Computational Biology</i>, vol. 17, no. 4. Public Library of Science, 2021.","ista":"Kleshnina M, Streipert SS, Filar JA, Chatterjee K. 2021. Mistakes can stabilise the dynamics of rock-paper-scissors games. PLoS Computational Biology. 17(4), e1008523."},"year":"2021","date_published":"2021-04-01T00:00:00Z","article_type":"original","abstract":[{"lang":"eng","text":"A game of rock-paper-scissors is an interesting example of an interaction where none of the pure strategies strictly dominates all others, leading to a cyclic pattern. In this work, we consider an unstable version of rock-paper-scissors dynamics and allow individuals to make behavioural mistakes during the strategy execution. We show that such an assumption can break a cyclic relationship leading to a stable equilibrium emerging with only one strategy surviving. We consider two cases: completely random mistakes when individuals have no bias towards any strategy and a general form of mistakes. Then, we determine conditions for a strategy to dominate all other strategies. However, given that individuals who adopt a dominating strategy are still prone to behavioural mistakes in the observed behaviour, we may still observe extinct strategies. That is, behavioural mistakes in strategy execution stabilise evolutionary dynamics leading to an evolutionary stable and, potentially, mixed co-existence equilibrium."}],"project":[{"grant_number":"754411","name":"ISTplus - Postdoctoral Fellowships","_id":"260C2330-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"},{"grant_number":"863818","call_identifier":"H2020","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","name":"Formal Methods for Stochastic Models: Algorithms and Applications"}],"external_id":{"pmid":["33844680"],"isi":["000639711200001"]},"quality_controlled":"1","ec_funded":1,"day":"01","volume":17,"oa_version":"Published Version","type":"journal_article","acknowledgement":"Authors would like to thank Christian Hilbe and Martin Nowak for their inspiring and very helpful feedback on the manuscript.","author":[{"last_name":"Kleshnina","full_name":"Kleshnina, Maria","id":"4E21749C-F248-11E8-B48F-1D18A9856A87","first_name":"Maria"},{"full_name":"Streipert, Sabrina S.","last_name":"Streipert","first_name":"Sabrina S."},{"full_name":"Filar, Jerzy A.","last_name":"Filar","first_name":"Jerzy A."},{"last_name":"Chatterjee","full_name":"Chatterjee, Krishnendu","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","first_name":"Krishnendu","orcid":"0000-0002-4561-241X"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","issue":"4","file_date_updated":"2021-05-11T13:50:06Z","_id":"9381","oa":1,"intvolume":"        17","pmid":1,"ddc":["000"],"file":[{"date_created":"2021-05-11T13:50:06Z","checksum":"a94ebe0c4116f5047eaa6029e54d2dac","success":1,"access_level":"open_access","relation":"main_file","file_size":1323820,"file_name":"2021_pcbi_Kleshnina.pdf","date_updated":"2021-05-11T13:50:06Z","content_type":"application/pdf","creator":"kschuh","file_id":"9385"}],"language":[{"iso":"eng"}],"publication":"PLoS Computational Biology","has_accepted_license":"1","date_created":"2021-05-09T22:01:38Z","scopus_import":"1","status":"public"},{"type":"journal_article","author":[{"full_name":"Bartlett, Michael John","last_name":"Bartlett","first_name":"Michael John"},{"id":"49DA7910-F248-11E8-B48F-1D18A9856A87","full_name":"Arslan, Feyza N","last_name":"Arslan","first_name":"Feyza N","orcid":"0000-0001-5809-9566"},{"first_name":"Adriana","last_name":"Bankston","full_name":"Bankston, Adriana"},{"last_name":"Sarabipour","full_name":"Sarabipour, Sarvenaz","first_name":"Sarvenaz"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","acknowledgement":"The authors thank Inez Lam of Johns Hopkins University for valuable comments on an earlier version of the manuscript. We also thank the facilitators of the 2019–2020 eLife Community Ambassador program.","file_date_updated":"2021-08-05T12:06:49Z","issue":"7","_id":"9759","oa":1,"pmid":1,"intvolume":"        17","file":[{"date_created":"2021-08-05T12:06:49Z","checksum":"e56d91f0eeadb36f143a90e2c1b3ab63","access_level":"open_access","relation":"main_file","file_size":693633,"file_name":"2021_PlosCompBio_Bartlett.pdf","date_updated":"2021-08-05T12:06:49Z","content_type":"application/pdf","file_id":"9771","creator":"cchlebak"}],"ddc":["613"],"has_accepted_license":"1","language":[{"iso":"eng"}],"publication":"PLoS Computational Biology","scopus_import":"1","date_created":"2021-08-01T22:01:21Z","status":"public","title":"Ten simple rules to improve academic work- life balance","isi":1,"article_number":"e1009124","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"department":[{"_id":"CaHe"}],"month":"07","date_updated":"2025-07-10T12:02:02Z","publication_status":"published","doi":"10.1371/journal.pcbi.1009124","publisher":"Public Library of Science","article_processing_charge":"Yes","publication_identifier":{"eissn":["1553-7358"],"issn":["1553-734X"]},"citation":{"short":"M.J. Bartlett, F.N. Arslan, A. Bankston, S. Sarabipour, PLoS Computational Biology 17 (2021).","apa":"Bartlett, M. J., Arslan, F. N., Bankston, A., &#38; Sarabipour, S. (2021). Ten simple rules to improve academic work- life balance. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1009124\">https://doi.org/10.1371/journal.pcbi.1009124</a>","ama":"Bartlett MJ, Arslan FN, Bankston A, Sarabipour S. Ten simple rules to improve academic work- life balance. <i>PLoS Computational Biology</i>. 2021;17(7). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1009124\">10.1371/journal.pcbi.1009124</a>","chicago":"Bartlett, Michael John, Feyza N Arslan, Adriana Bankston, and Sarvenaz Sarabipour. “Ten Simple Rules to Improve Academic Work- Life Balance.” <i>PLoS Computational Biology</i>. Public Library of Science, 2021. <a href=\"https://doi.org/10.1371/journal.pcbi.1009124\">https://doi.org/10.1371/journal.pcbi.1009124</a>.","ista":"Bartlett MJ, Arslan FN, Bankston A, Sarabipour S. 2021. Ten simple rules to improve academic work- life balance. PLoS Computational Biology. 17(7), e1009124.","ieee":"M. J. Bartlett, F. N. Arslan, A. Bankston, and S. Sarabipour, “Ten simple rules to improve academic work- life balance,” <i>PLoS Computational Biology</i>, vol. 17, no. 7. Public Library of Science, 2021.","mla":"Bartlett, Michael John, et al. “Ten Simple Rules to Improve Academic Work- Life Balance.” <i>PLoS Computational Biology</i>, vol. 17, no. 7, e1009124, Public Library of Science, 2021, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1009124\">10.1371/journal.pcbi.1009124</a>."},"year":"2021","date_published":"2021-07-15T00:00:00Z","article_type":"letter_note","external_id":{"pmid":["34264932"],"isi":["000677713500008"]},"day":"15","oa_version":"Published Version","volume":17},{"date_updated":"2025-06-12T07:02:01Z","month":"11","department":[{"_id":"KrCh"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"isi":1,"article_number":"e1008402","title":"The Moran process on 2-chromatic graphs","year":"2020","date_published":"2020-11-05T00:00:00Z","citation":{"chicago":"Kaveh, Kamran, Alex McAvoy, Krishnendu Chatterjee, and Martin A. Nowak. “The Moran Process on 2-Chromatic Graphs.” <i>PLOS Computational Biology</i>. Public Library of Science, 2020. <a href=\"https://doi.org/10.1371/journal.pcbi.1008402\">https://doi.org/10.1371/journal.pcbi.1008402</a>.","ama":"Kaveh K, McAvoy A, Chatterjee K, Nowak MA. The Moran process on 2-chromatic graphs. <i>PLOS Computational Biology</i>. 2020;16(11). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1008402\">10.1371/journal.pcbi.1008402</a>","apa":"Kaveh, K., McAvoy, A., Chatterjee, K., &#38; Nowak, M. A. (2020). The Moran process on 2-chromatic graphs. <i>PLOS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1008402\">https://doi.org/10.1371/journal.pcbi.1008402</a>","short":"K. Kaveh, A. McAvoy, K. Chatterjee, M.A. Nowak, PLOS Computational Biology 16 (2020).","mla":"Kaveh, Kamran, et al. “The Moran Process on 2-Chromatic Graphs.” <i>PLOS Computational Biology</i>, vol. 16, no. 11, e1008402, Public Library of Science, 2020, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1008402\">10.1371/journal.pcbi.1008402</a>.","ieee":"K. Kaveh, A. McAvoy, K. Chatterjee, and M. A. Nowak, “The Moran process on 2-chromatic graphs,” <i>PLOS Computational Biology</i>, vol. 16, no. 11. Public Library of Science, 2020.","ista":"Kaveh K, McAvoy A, Chatterjee K, Nowak MA. 2020. The Moran process on 2-chromatic graphs. PLOS Computational Biology. 16(11), e1008402."},"publisher":"Public Library of Science","article_processing_charge":"No","publication_identifier":{"issn":["1553-734X"],"eissn":["1553-7358"]},"doi":"10.1371/journal.pcbi.1008402","publication_status":"published","article_type":"original","abstract":[{"text":"Resources are rarely distributed uniformly within a population. Heterogeneity in the concentration of a drug, the quality of breeding sites, or wealth can all affect evolutionary dynamics. In this study, we represent a collection of properties affecting the fitness at a given location using a color. A green node is rich in resources while a red node is poorer. More colors can represent a broader spectrum of resource qualities. For a population evolving according to the birth-death Moran model, the first question we address is which structures, identified by graph connectivity and graph coloring, are evolutionarily equivalent. We prove that all properly two-colored, undirected, regular graphs are evolutionarily equivalent (where “properly colored” means that no two neighbors have the same color). We then compare the effects of background heterogeneity on properly two-colored graphs to those with alternative schemes in which the colors are permuted. Finally, we discuss dynamic coloring as a model for spatiotemporal resource fluctuations, and we illustrate that random dynamic colorings often diminish the effects of background heterogeneity relative to a proper two-coloring.","lang":"eng"}],"day":"05","volume":16,"oa_version":"Published Version","external_id":{"pmid":["33151935"],"isi":["000591317200004"]},"quality_controlled":"1","issue":"11","file_date_updated":"2020-11-18T07:26:10Z","acknowledgement":"We thank Igor Erovenko for many helpful comments on an earlier version of this paper. : Army Research Laboratory (grant W911NF-18-2-0265) (M.A.N.); the Bill & Melinda Gates Foundation (grant OPP1148627) (M.A.N.); the NVIDIA Corporation (A.M.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"first_name":"Kamran","last_name":"Kaveh","full_name":"Kaveh, Kamran"},{"full_name":"McAvoy, Alex","last_name":"McAvoy","first_name":"Alex"},{"last_name":"Chatterjee","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","full_name":"Chatterjee, Krishnendu","orcid":"0000-0002-4561-241X","first_name":"Krishnendu"},{"first_name":"Martin A.","last_name":"Nowak","full_name":"Nowak, Martin A."}],"type":"journal_article","oa":1,"_id":"8767","ddc":["000"],"file":[{"success":1,"access_level":"open_access","relation":"main_file","file_size":2498594,"date_created":"2020-11-18T07:26:10Z","checksum":"555456dd0e47bcf9e0994bcb95577e88","creator":"dernst","file_id":"8768","file_name":"2020_PlosCompBio_Kaveh.pdf","date_updated":"2020-11-18T07:26:10Z","content_type":"application/pdf"}],"intvolume":"        16","pmid":1,"keyword":["Ecology","Modelling and Simulation","Computational Theory and Mathematics","Genetics","Ecology","Evolution","Behavior and Systematics","Molecular Biology","Cellular and Molecular Neuroscience"],"scopus_import":"1","date_created":"2020-11-18T07:20:23Z","status":"public","publication":"PLOS Computational Biology","language":[{"iso":"eng"}],"has_accepted_license":"1"},{"day":"17","oa_version":"Published Version","volume":16,"ec_funded":1,"quality_controlled":"1","external_id":{"isi":["000510916500025"],"arxiv":["1906.02785"]},"project":[{"grant_number":"279307","_id":"2581B60A-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"Quantitative Graph Games: Theory and Applications"},{"grant_number":"P 23499-N23","name":"Modern Graph Algorithmic Techniques in Formal Verification","call_identifier":"FWF","_id":"2584A770-B435-11E9-9278-68D0E5697425"},{"grant_number":"S11407","call_identifier":"FWF","_id":"25863FF4-B435-11E9-9278-68D0E5697425","name":"Game Theory"}],"abstract":[{"lang":"eng","text":"The fixation probability of a single mutant invading a population of residents is among the most widely-studied quantities in evolutionary dynamics. Amplifiers of natural selection are population structures that increase the fixation probability of advantageous mutants, compared to well-mixed populations. Extensive studies have shown that many amplifiers exist for the Birth-death Moran process, some of them substantially increasing the fixation probability or even guaranteeing fixation in the limit of large population size. On the other hand, no amplifiers are known for the death-Birth Moran process, and computer-assisted exhaustive searches have failed to discover amplification. In this work we resolve this disparity, by showing that any amplification under death-Birth updating is necessarily bounded and transient. Our boundedness result states that even if a population structure does amplify selection, the resulting fixation probability is close to that of the well-mixed population. Our transience result states that for any population structure there exists a threshold r⋆ such that the population structure ceases to amplify selection if the mutant fitness advantage r is larger than r⋆. Finally, we also extend the above results to δ-death-Birth updating, which is a combination of Birth-death and death-Birth updating. On the positive side, we identify population structures that maintain amplification for a wide range of values r and δ. These results demonstrate that amplification of natural selection depends on the specific mechanisms of the evolutionary process."}],"article_type":"original","date_published":"2020-01-17T00:00:00Z","year":"2020","citation":{"ista":"Tkadlec J, Pavlogiannis A, Chatterjee K, Nowak MA. 2020. Limits on amplifiers of natural selection under death-Birth updating. PLoS computational biology. 16, e1007494.","mla":"Tkadlec, Josef, et al. “Limits on Amplifiers of Natural Selection under Death-Birth Updating.” <i>PLoS Computational Biology</i>, vol. 16, e1007494, Public Library of Science, 2020, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007494\">10.1371/journal.pcbi.1007494</a>.","ieee":"J. Tkadlec, A. Pavlogiannis, K. Chatterjee, and M. A. Nowak, “Limits on amplifiers of natural selection under death-Birth updating,” <i>PLoS computational biology</i>, vol. 16. Public Library of Science, 2020.","apa":"Tkadlec, J., Pavlogiannis, A., Chatterjee, K., &#38; Nowak, M. A. (2020). Limits on amplifiers of natural selection under death-Birth updating. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1007494\">https://doi.org/10.1371/journal.pcbi.1007494</a>","short":"J. Tkadlec, A. Pavlogiannis, K. Chatterjee, M.A. Nowak, PLoS Computational Biology 16 (2020).","chicago":"Tkadlec, Josef, Andreas Pavlogiannis, Krishnendu Chatterjee, and Martin A. Nowak. “Limits on Amplifiers of Natural Selection under Death-Birth Updating.” <i>PLoS Computational Biology</i>. Public Library of Science, 2020. <a href=\"https://doi.org/10.1371/journal.pcbi.1007494\">https://doi.org/10.1371/journal.pcbi.1007494</a>.","ama":"Tkadlec J, Pavlogiannis A, Chatterjee K, Nowak MA. Limits on amplifiers of natural selection under death-Birth updating. <i>PLoS computational biology</i>. 2020;16. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007494\">10.1371/journal.pcbi.1007494</a>"},"publication_status":"published","publication_identifier":{"issn":["1553-734X"],"eissn":["1553-7358"]},"article_processing_charge":"No","publisher":"Public Library of Science","doi":"10.1371/journal.pcbi.1007494","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"date_updated":"2026-04-16T08:32:38Z","month":"01","department":[{"_id":"KrCh"}],"title":"Limits on amplifiers of natural selection under death-Birth updating","isi":1,"article_number":"e1007494","date_created":"2019-12-23T13:45:11Z","status":"public","scopus_import":"1","has_accepted_license":"1","publication":"PLoS computational biology","language":[{"iso":"eng"}],"file":[{"creator":"dernst","file_id":"7441","date_updated":"2020-07-14T12:47:53Z","file_name":"2020_PlosCompBio_Tkadlec.pdf","content_type":"application/pdf","access_level":"open_access","file_size":1817531,"relation":"main_file","date_created":"2020-02-03T07:32:42Z","checksum":"ce32ee2d2f53aed832f78bbd47e882df"}],"ddc":["000"],"intvolume":"        16","related_material":{"record":[{"status":"public","id":"7196","relation":"part_of_dissertation"}]},"oa":1,"_id":"7212","arxiv":1,"file_date_updated":"2020-07-14T12:47:53Z","author":[{"first_name":"Josef","orcid":"0000-0002-1097-9684","id":"3F24CCC8-F248-11E8-B48F-1D18A9856A87","full_name":"Tkadlec, Josef","last_name":"Tkadlec"},{"orcid":"0000-0002-8943-0722","first_name":"Andreas","full_name":"Pavlogiannis, Andreas","id":"49704004-F248-11E8-B48F-1D18A9856A87","last_name":"Pavlogiannis"},{"first_name":"Krishnendu","orcid":"0000-0002-4561-241X","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","full_name":"Chatterjee, Krishnendu","last_name":"Chatterjee"},{"last_name":"Nowak","full_name":"Nowak, Martin A.","first_name":"Martin A."}],"user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","type":"journal_article"},{"pmid":1,"intvolume":"        15","page":"e1007290","ddc":["570"],"file":[{"relation":"main_file","file_size":3081855,"access_level":"open_access","checksum":"81bdce1361c9aa8395d6fa635fb6ab47","date_created":"2019-10-01T10:53:45Z","file_id":"6925","creator":"kschuh","content_type":"application/pdf","file_name":"2019_PLoS_Cepeda-Humerez.pdf","date_updated":"2020-07-14T12:47:44Z"}],"language":[{"iso":"eng"}],"publication":"PLoS computational biology","has_accepted_license":"1","scopus_import":"1","date_created":"2019-09-22T22:00:37Z","status":"public","type":"journal_article","author":[{"first_name":"Sarah A","last_name":"Cepeda Humerez","id":"3DEE19A4-F248-11E8-B48F-1D18A9856A87","full_name":"Cepeda Humerez, Sarah A"},{"last_name":"Ruess","full_name":"Ruess, Jakob","first_name":"Jakob","orcid":"0000-0003-1615-3282"},{"orcid":"0000-0002-6699-1455","first_name":"Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","last_name":"Tkačik"}],"user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","issue":"9","file_date_updated":"2020-07-14T12:47:44Z","_id":"6900","related_material":{"record":[{"relation":"part_of_dissertation","id":"6473","status":"public"}]},"oa":1,"abstract":[{"text":"Across diverse biological systems—ranging from neural networks to intracellular signaling and genetic regulatory networks—the information about changes in the environment is frequently encoded in the full temporal dynamics of the network nodes. A pressing data-analysis challenge has thus been to efficiently estimate the amount of information that these dynamics convey from experimental data. Here we develop and evaluate decoding-based estimation methods to lower bound the mutual information about a finite set of inputs, encoded in single-cell high-dimensional time series data. For biological reaction networks governed by the chemical Master equation, we derive model-based information approximations and analytical upper bounds, against which we benchmark our proposed model-free decoding estimators. In contrast to the frequently-used k-nearest-neighbor estimator, decoding-based estimators robustly extract a large fraction of the available information from high-dimensional trajectories with a realistic number of data samples. We apply these estimators to previously published data on Erk and Ca2+ signaling in mammalian cells and to yeast stress-response, and find that substantial amount of information about environmental state can be encoded by non-trivial response statistics even in stationary signals. We argue that these single-cell, decoding-based information estimates, rather than the commonly-used tests for significant differences between selected population response statistics, provide a proper and unbiased measure for the performance of biological signaling networks.","lang":"eng"}],"project":[{"grant_number":"P28844-B27","name":"Biophysics of information processing in gene regulation","call_identifier":"FWF","_id":"254E9036-B435-11E9-9278-68D0E5697425"}],"quality_controlled":"1","external_id":{"isi":["000489741800021"],"pmid":["31479447"]},"oa_version":"Published Version","day":"03","volume":15,"isi":1,"title":"Estimating information in time-varying signals","department":[{"_id":"GaTk"}],"month":"09","date_updated":"2026-04-16T08:37:39Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"doi":"10.1371/journal.pcbi.1007290","publisher":"Public Library of Science","article_processing_charge":"No","publication_identifier":{"issn":["1553-734X"],"eissn":["1553-7358"]},"publication_status":"published","citation":{"ista":"Cepeda Humerez SA, Ruess J, Tkačik G. 2019. Estimating information in time-varying signals. PLoS computational biology. 15(9), e1007290.","ieee":"S. A. Cepeda Humerez, J. Ruess, and G. Tkačik, “Estimating information in time-varying signals,” <i>PLoS computational biology</i>, vol. 15, no. 9. Public Library of Science, p. e1007290, 2019.","mla":"Cepeda Humerez, Sarah A., et al. “Estimating Information in Time-Varying Signals.” <i>PLoS Computational Biology</i>, vol. 15, no. 9, Public Library of Science, 2019, p. e1007290, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007290\">10.1371/journal.pcbi.1007290</a>.","short":"S.A. Cepeda Humerez, J. Ruess, G. Tkačik, PLoS Computational Biology 15 (2019) e1007290.","apa":"Cepeda Humerez, S. A., Ruess, J., &#38; Tkačik, G. (2019). Estimating information in time-varying signals. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1007290\">https://doi.org/10.1371/journal.pcbi.1007290</a>","ama":"Cepeda Humerez SA, Ruess J, Tkačik G. Estimating information in time-varying signals. <i>PLoS computational biology</i>. 2019;15(9):e1007290. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007290\">10.1371/journal.pcbi.1007290</a>","chicago":"Cepeda Humerez, Sarah A, Jakob Ruess, and Gašper Tkačik. “Estimating Information in Time-Varying Signals.” <i>PLoS Computational Biology</i>. Public Library of Science, 2019. <a href=\"https://doi.org/10.1371/journal.pcbi.1007290\">https://doi.org/10.1371/journal.pcbi.1007290</a>."},"date_published":"2019-09-03T00:00:00Z","year":"2019"},{"status":"public","scopus_import":"1","date_created":"2018-12-11T11:47:53Z","publication":"PLoS Computational Biology","language":[{"iso":"eng"}],"has_accepted_license":"1","ddc":["571"],"file":[{"creator":"system","file_id":"4645","file_name":"IST-2017-898-v1+1_journal.pcbi.1005582.pdf","date_updated":"2020-07-14T12:47:40Z","content_type":"application/pdf","access_level":"open_access","file_size":14555676,"relation":"main_file","date_created":"2018-12-12T10:07:47Z","checksum":"796a1026076af6f4405a47d985bc7b68"}],"publist_id":"7035","intvolume":"        13","oa":1,"related_material":{"record":[{"id":"9855","status":"public","relation":"research_data"}]},"_id":"680","corr_author":"1","issue":"6","file_date_updated":"2020-07-14T12:47:40Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","author":[{"last_name":"Chalk","id":"2BAAC544-F248-11E8-B48F-1D18A9856A87","full_name":"Chalk, Matthew J","first_name":"Matthew J","orcid":"0000-0001-7782-4436"},{"first_name":"Paul","last_name":"Masset","full_name":"Masset, Paul"},{"full_name":"Gutkin, Boris","last_name":"Gutkin","first_name":"Boris"},{"last_name":"Denève","full_name":"Denève, Sophie","first_name":"Sophie"}],"type":"journal_article","pubrep_id":"898","volume":13,"oa_version":"Published Version","day":"01","quality_controlled":"1","external_id":{"isi":["000404565400034"]},"abstract":[{"text":"In order to respond reliably to specific features of their environment, sensory neurons need to integrate multiple incoming noisy signals. Crucially, they also need to compete for the interpretation of those signals with other neurons representing similar features. The form that this competition should take depends critically on the noise corrupting these signals. In this study we show that for the type of noise commonly observed in sensory systems, whose variance scales with the mean signal, sensory neurons should selectively divide their input signals by their predictions, suppressing ambiguous cues while amplifying others. Any change in the stimulus context alters which inputs are suppressed, leading to a deep dynamic reshaping of neural receptive fields going far beyond simple surround suppression. Paradoxically, these highly variable receptive fields go alongside and are in fact required for an invariant representation of external sensory features. In addition to offering a normative account of context-dependent changes in sensory responses, perceptual inference in the presence of signal-dependent noise accounts for ubiquitous features of sensory neurons such as divisive normalization, gain control and contrast dependent temporal dynamics.","lang":"eng"}],"date_published":"2017-06-01T00:00:00Z","year":"2017","citation":{"ama":"Chalk MJ, Masset P, Gutkin B, Denève S. Sensory noise predicts divisive reshaping of receptive fields. <i>PLoS Computational Biology</i>. 2017;13(6). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005582\">10.1371/journal.pcbi.1005582</a>","chicago":"Chalk, Matthew J, Paul Masset, Boris Gutkin, and Sophie Denève. “Sensory Noise Predicts Divisive Reshaping of Receptive Fields.” <i>PLoS Computational Biology</i>. Public Library of Science, 2017. <a href=\"https://doi.org/10.1371/journal.pcbi.1005582\">https://doi.org/10.1371/journal.pcbi.1005582</a>.","short":"M.J. Chalk, P. Masset, B. Gutkin, S. Denève, PLoS Computational Biology 13 (2017).","apa":"Chalk, M. J., Masset, P., Gutkin, B., &#38; Denève, S. (2017). Sensory noise predicts divisive reshaping of receptive fields. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1005582\">https://doi.org/10.1371/journal.pcbi.1005582</a>","ieee":"M. J. Chalk, P. Masset, B. Gutkin, and S. Denève, “Sensory noise predicts divisive reshaping of receptive fields,” <i>PLoS Computational Biology</i>, vol. 13, no. 6. Public Library of Science, 2017.","mla":"Chalk, Matthew J., et al. “Sensory Noise Predicts Divisive Reshaping of Receptive Fields.” <i>PLoS Computational Biology</i>, vol. 13, no. 6, e1005582, Public Library of Science, 2017, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005582\">10.1371/journal.pcbi.1005582</a>.","ista":"Chalk MJ, Masset P, Gutkin B, Denève S. 2017. Sensory noise predicts divisive reshaping of receptive fields. PLoS Computational Biology. 13(6), e1005582."},"publication_identifier":{"issn":["1553-734X"]},"publisher":"Public Library of Science","article_processing_charge":"No","doi":"10.1371/journal.pcbi.1005582","publication_status":"published","date_updated":"2025-09-10T14:20:48Z","month":"06","department":[{"_id":"GaTk"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"article_number":"e1005582","isi":1,"title":"Sensory noise predicts divisive reshaping of receptive fields"},{"status":"public","scopus_import":"1","date_created":"2018-12-11T11:48:08Z","publication":"PLoS Computational Biology","language":[{"iso":"eng"}],"has_accepted_license":"1","ddc":["530","571"],"file":[{"creator":"system","file_id":"5352","file_name":"IST-2017-884-v1+1_journal.pcbi.1005763.pdf","date_updated":"2020-07-14T12:47:53Z","content_type":"application/pdf","access_level":"open_access","relation":"main_file","file_size":14167050,"date_created":"2018-12-12T10:18:30Z","checksum":"81107096c19771c36ddbe6f0282a3acb"}],"publist_id":"6960","intvolume":"        13","oa":1,"_id":"720","corr_author":"1","issue":"9","file_date_updated":"2020-07-14T12:47:53Z","author":[{"last_name":"Humplik","full_name":"Humplik, Jan","id":"2E9627A8-F248-11E8-B48F-1D18A9856A87","first_name":"Jan"},{"full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik","orcid":"0000-0002-6699-1455","first_name":"Gasper"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","type":"journal_article","pubrep_id":"884","oa_version":"Published Version","day":"19","volume":13,"external_id":{"isi":["000411981000042"]},"quality_controlled":"1","project":[{"_id":"255008E4-B435-11E9-9278-68D0E5697425","name":"Information processing and computation in fish groups","grant_number":"RGP0065/2012"},{"call_identifier":"FWF","name":"Sensitivity to higher-order statistics in natural scenes","_id":"254D1A94-B435-11E9-9278-68D0E5697425","grant_number":"P 25651-N26"}],"abstract":[{"text":"Advances in multi-unit recordings pave the way for statistical modeling of activity patterns in large neural populations. Recent studies have shown that the summed activity of all neurons strongly shapes the population response. A separate recent finding has been that neural populations also exhibit criticality, an anomalously large dynamic range for the probabilities of different population activity patterns. Motivated by these two observations, we introduce a class of probabilistic models which takes into account the prior knowledge that the neural population could be globally coupled and close to critical. These models consist of an energy function which parametrizes interactions between small groups of neurons, and an arbitrary positive, strictly increasing, and twice differentiable function which maps the energy of a population pattern to its probability. We show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an accurate description of the activity of retinal ganglion cells which outperforms previous models based on the summed activity of neurons; 2) prior knowledge that the population is critical translates to prior expectations about the shape of the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous latent variable globally coupling the system whose distribution we can infer from data. Our method is independent of the underlying system’s state space; hence, it can be applied to other systems such as natural scenes or amino acid sequences of proteins which are also known to exhibit criticality.","lang":"eng"}],"year":"2017","date_published":"2017-09-19T00:00:00Z","citation":{"ama":"Humplik J, Tkačik G. Probabilistic models for neural populations that naturally capture global coupling and criticality. <i>PLoS Computational Biology</i>. 2017;13(9). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005763\">10.1371/journal.pcbi.1005763</a>","chicago":"Humplik, Jan, and Gašper Tkačik. “Probabilistic Models for Neural Populations That Naturally Capture Global Coupling and Criticality.” <i>PLoS Computational Biology</i>. Public Library of Science, 2017. <a href=\"https://doi.org/10.1371/journal.pcbi.1005763\">https://doi.org/10.1371/journal.pcbi.1005763</a>.","short":"J. Humplik, G. Tkačik, PLoS Computational Biology 13 (2017).","apa":"Humplik, J., &#38; Tkačik, G. (2017). Probabilistic models for neural populations that naturally capture global coupling and criticality. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1005763\">https://doi.org/10.1371/journal.pcbi.1005763</a>","ieee":"J. Humplik and G. Tkačik, “Probabilistic models for neural populations that naturally capture global coupling and criticality,” <i>PLoS Computational Biology</i>, vol. 13, no. 9. Public Library of Science, 2017.","mla":"Humplik, Jan, and Gašper Tkačik. “Probabilistic Models for Neural Populations That Naturally Capture Global Coupling and Criticality.” <i>PLoS Computational Biology</i>, vol. 13, no. 9, e1005763, Public Library of Science, 2017, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005763\">10.1371/journal.pcbi.1005763</a>.","ista":"Humplik J, Tkačik G. 2017. Probabilistic models for neural populations that naturally capture global coupling and criticality. PLoS Computational Biology. 13(9), e1005763."},"publication_identifier":{"issn":["1553-734X"]},"article_processing_charge":"Yes","publisher":"Public Library of Science","doi":"10.1371/journal.pcbi.1005763","publication_status":"published","date_updated":"2025-09-10T10:58:42Z","month":"09","department":[{"_id":"GaTk"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"isi":1,"article_number":"e1005763","title":"Probabilistic models for neural populations that naturally capture global coupling and criticality"},{"oa":1,"related_material":{"record":[{"relation":"research_data","status":"public","id":"9849"},{"relation":"research_data","id":"9850","status":"public"},{"relation":"research_data","id":"9851","status":"public"},{"status":"public","id":"9852","relation":"research_data"},{"status":"public","id":"6263","relation":"dissertation_contains"}]},"_id":"696","issue":"7","corr_author":"1","file_date_updated":"2020-07-14T12:47:46Z","type":"journal_article","author":[{"last_name":"Lukacisinova","id":"4342E402-F248-11E8-B48F-1D18A9856A87","full_name":"Lukacisinova, Marta","first_name":"Marta","orcid":"0000-0002-2519-8004"},{"full_name":"Novak, Sebastian","id":"461468AE-F248-11E8-B48F-1D18A9856A87","last_name":"Novak","orcid":"0000-0002-2519-824X","first_name":"Sebastian"},{"first_name":"Tiago","orcid":"0000-0003-2361-3953","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87","full_name":"Paixao, Tiago","last_name":"Paixao"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","status":"public","scopus_import":"1","date_created":"2018-12-11T11:47:58Z","language":[{"iso":"eng"}],"publication":"PLoS Computational Biology","has_accepted_license":"1","ddc":["576"],"file":[{"date_updated":"2020-07-14T12:47:46Z","file_name":"IST-2017-894-v1+1_journal.pcbi.1005609.pdf","content_type":"application/pdf","creator":"system","file_id":"5117","date_created":"2018-12-12T10:15:01Z","checksum":"9143c290fa6458ed2563bff4b295554a","access_level":"open_access","relation":"main_file","file_size":3775716}],"publist_id":"7004","intvolume":"        13","citation":{"ista":"Lukacisinova M, Novak S, Paixao T. 2017. Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes. PLoS Computational Biology. 13(7), e1005609.","ieee":"M. Lukacisinova, S. Novak, and T. Paixao, “Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes,” <i>PLoS Computational Biology</i>, vol. 13, no. 7. Public Library of Science, 2017.","mla":"Lukacisinova, Marta, et al. “Stress Induced Mutagenesis: Stress Diversity Facilitates the Persistence of Mutator Genes.” <i>PLoS Computational Biology</i>, vol. 13, no. 7, e1005609, Public Library of Science, 2017, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005609\">10.1371/journal.pcbi.1005609</a>.","short":"M. Lukacisinova, S. Novak, T. Paixao, PLoS Computational Biology 13 (2017).","apa":"Lukacisinova, M., Novak, S., &#38; Paixao, T. (2017). Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1005609\">https://doi.org/10.1371/journal.pcbi.1005609</a>","ama":"Lukacisinova M, Novak S, Paixao T. Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes. <i>PLoS Computational Biology</i>. 2017;13(7). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005609\">10.1371/journal.pcbi.1005609</a>","chicago":"Lukacisinova, Marta, Sebastian Novak, and Tiago Paixao. “Stress Induced Mutagenesis: Stress Diversity Facilitates the Persistence of Mutator Genes.” <i>PLoS Computational Biology</i>. Public Library of Science, 2017. <a href=\"https://doi.org/10.1371/journal.pcbi.1005609\">https://doi.org/10.1371/journal.pcbi.1005609</a>."},"date_published":"2017-07-18T00:00:00Z","year":"2017","doi":"10.1371/journal.pcbi.1005609","publication_identifier":{"issn":["1553-734X"]},"article_processing_charge":"No","publisher":"Public Library of Science","publication_status":"published","month":"07","department":[{"_id":"ToBo"},{"_id":"NiBa"},{"_id":"CaGu"}],"date_updated":"2026-04-22T22:30:52Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"isi":1,"article_number":"e1005609","title":"Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes","ec_funded":1,"pubrep_id":"894","day":"18","volume":13,"oa_version":"Published Version","project":[{"_id":"25B1EC9E-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"Speed of Adaptation in Population Genetics and Evolutionary Computation","grant_number":"618091"}],"quality_controlled":"1","external_id":{"isi":["000406619800014"]},"article_type":"original","abstract":[{"lang":"eng","text":"Mutator strains are expected to evolve when the availability and effect of beneficial mutations are high enough to counteract the disadvantage from deleterious mutations that will inevitably accumulate. As the population becomes more adapted to its environment, both availability and effect of beneficial mutations necessarily decrease and mutation rates are predicted to decrease. It has been shown that certain molecular mechanisms can lead to increased mutation rates when the organism finds itself in a stressful environment. While this may be a correlated response to other functions, it could also be an adaptive mechanism, raising mutation rates only when it is most advantageous. Here, we use a mathematical model to investigate the plausibility of the adaptive hypothesis. We show that such a mechanism can be mantained if the population is subjected to diverse stresses. By simulating various antibiotic treatment schemes, we find that combination treatments can reduce the effectiveness of second-order selection on stress-induced mutagenesis. We discuss the implications of our results to strategies of antibiotic therapy."}]},{"publication_identifier":{"issn":["1553-734X"]},"article_processing_charge":"No","publisher":"Public Library of Science","doi":"10.1371/journal.pcbi.1003408","publication_status":"published","year":"2014","date_published":"2014-01-02T00:00:00Z","citation":{"ama":"Tkačik G, Marre O, Amodei D, Schneidman E, Bialek W, Berry M. Searching for collective behavior in a large network of sensory neurons. <i>PLoS Computational Biology</i>. 2014;10(1). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1003408\">10.1371/journal.pcbi.1003408</a>","chicago":"Tkačik, Gašper, Olivier Marre, Dario Amodei, Elad Schneidman, William Bialek, and Michael Berry. “Searching for Collective Behavior in a Large Network of Sensory Neurons.” <i>PLoS Computational Biology</i>. Public Library of Science, 2014. <a href=\"https://doi.org/10.1371/journal.pcbi.1003408\">https://doi.org/10.1371/journal.pcbi.1003408</a>.","short":"G. Tkačik, O. Marre, D. Amodei, E. Schneidman, W. Bialek, M. Berry, PLoS Computational Biology 10 (2014).","apa":"Tkačik, G., Marre, O., Amodei, D., Schneidman, E., Bialek, W., &#38; Berry, M. (2014). Searching for collective behavior in a large network of sensory neurons. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1003408\">https://doi.org/10.1371/journal.pcbi.1003408</a>","ieee":"G. Tkačik, O. Marre, D. Amodei, E. Schneidman, W. Bialek, and M. Berry, “Searching for collective behavior in a large network of sensory neurons,” <i>PLoS Computational Biology</i>, vol. 10, no. 1. Public Library of Science, 2014.","mla":"Tkačik, Gašper, et al. “Searching for Collective Behavior in a Large Network of Sensory Neurons.” <i>PLoS Computational Biology</i>, vol. 10, no. 1, e1003408, Public Library of Science, 2014, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1003408\">10.1371/journal.pcbi.1003408</a>.","ista":"Tkačik G, Marre O, Amodei D, Schneidman E, Bialek W, Berry M. 2014. Searching for collective behavior in a large network of sensory neurons. PLoS Computational Biology. 10(1), e1003408."},"article_number":"e1003408","isi":1,"title":"Searching for collective behavior in a large network of sensory neurons","date_updated":"2025-09-29T11:14:06Z","month":"01","department":[{"_id":"GaTk"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"quality_controlled":"1","external_id":{"isi":["000337948500010"]},"pubrep_id":"436","oa_version":"Published Version","day":"02","volume":10,"abstract":[{"text":"Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such “K-pairwise” models—being systematic extensions of the previously used pairwise Ising models—provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population's capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction.","lang":"eng"}],"_id":"2257","related_material":{"record":[{"status":"public","id":"5562","relation":"research_data"}]},"oa":1,"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","acknowledgement":"This work was funded by NSF grant IIS-0613435, NSF grant PHY-0957573, NSF grant CCF-0939370, NIH grant R01 EY14196, NIH grant P50 GM071508, the Fannie and John Hertz Foundation, the Swartz Foundation, the WM Keck Foundation, ANR Optima and the French State program “Investissements d'Avenir” [LIFESENSES: ANR-10-LABX-65], and the Austrian Research Foundation FWF P25651.","author":[{"last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper","first_name":"Gasper","orcid":"0000-0002-6699-1455"},{"first_name":"Olivier","full_name":"Marre, Olivier","last_name":"Marre"},{"last_name":"Amodei","full_name":"Amodei, Dario","first_name":"Dario"},{"last_name":"Schneidman","full_name":"Schneidman, Elad","first_name":"Elad"},{"full_name":"Bialek, William","last_name":"Bialek","first_name":"William"},{"last_name":"Berry","full_name":"Berry, Michael","first_name":"Michael"}],"type":"journal_article","corr_author":"1","issue":"1","file_date_updated":"2020-07-14T12:45:35Z","publication":"PLoS Computational Biology","language":[{"iso":"eng"}],"has_accepted_license":"1","date_created":"2018-12-11T11:56:36Z","status":"public","scopus_import":"1","publist_id":"4689","intvolume":"        10","ddc":["570"],"file":[{"checksum":"c720222c5e924a4acb17f23b9381a6ca","date_created":"2018-12-12T10:12:46Z","relation":"main_file","file_size":2194790,"access_level":"open_access","content_type":"application/pdf","date_updated":"2020-07-14T12:45:35Z","file_name":"IST-2016-436-v1+1_journal.pcbi.1003408.pdf","creator":"system","file_id":"4965"}]}]