[{"ddc":["570"],"volume":19,"acknowledgement":"The authors thank Corey Ziemba and Zoe Boundy-Singer for valuable discussion and feedback.","isi":1,"external_id":{"pmid":["37289753"],"isi":["001003410200003"]},"date_updated":"2023-08-02T06:33:50Z","citation":{"ama":"Charlton JA, Mlynarski WF, Bai YH, Hermundstad AM, Goris RLT. Environmental dynamics shape perceptual decision bias. <i>PLoS Computational Biology</i>. 2023;19(6). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1011104\">10.1371/journal.pcbi.1011104</a>","apa":"Charlton, J. A., Mlynarski, W. F., Bai, Y. H., Hermundstad, A. M., &#38; Goris, R. L. T. (2023). Environmental dynamics shape perceptual decision bias. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1011104\">https://doi.org/10.1371/journal.pcbi.1011104</a>","chicago":"Charlton, Julie A., Wiktor F Mlynarski, Yoon H. Bai, Ann M. Hermundstad, and Robbe L.T. Goris. “Environmental Dynamics Shape Perceptual Decision Bias.” <i>PLoS Computational Biology</i>. Public Library of Science, 2023. <a href=\"https://doi.org/10.1371/journal.pcbi.1011104\">https://doi.org/10.1371/journal.pcbi.1011104</a>.","ieee":"J. A. Charlton, W. F. Mlynarski, Y. H. Bai, A. M. Hermundstad, and R. L. T. Goris, “Environmental dynamics shape perceptual decision bias,” <i>PLoS Computational Biology</i>, vol. 19, no. 6. Public Library of Science, 2023.","short":"J.A. Charlton, W.F. Mlynarski, Y.H. Bai, A.M. Hermundstad, R.L.T. Goris, PLoS Computational Biology 19 (2023).","mla":"Charlton, Julie A., et al. “Environmental Dynamics Shape Perceptual Decision Bias.” <i>PLoS Computational Biology</i>, vol. 19, no. 6, e1011104, Public Library of Science, 2023, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1011104\">10.1371/journal.pcbi.1011104</a>.","ista":"Charlton JA, Mlynarski WF, Bai YH, Hermundstad AM, Goris RLT. 2023. Environmental dynamics shape perceptual decision bias. PLoS Computational Biology. 19(6), e1011104."},"year":"2023","abstract":[{"lang":"eng","text":"To interpret the sensory environment, the brain combines ambiguous sensory measurements with knowledge that reflects context-specific prior experience. But environmental contexts can change abruptly and unpredictably, resulting in uncertainty about the current context. Here we address two questions: how should context-specific prior knowledge optimally guide the interpretation of sensory stimuli in changing environments, and do human decision-making strategies resemble this optimum? We probe these questions with a task in which subjects report the orientation of ambiguous visual stimuli that were drawn from three dynamically switching distributions, representing different environmental contexts. We derive predictions for an ideal Bayesian observer that leverages knowledge about the statistical structure of the task to maximize decision accuracy, including knowledge about the dynamics of the environment. We show that its decisions are biased by the dynamically changing task context. The magnitude of this decision bias depends on the observer’s continually evolving belief about the current context. The model therefore not only predicts that decision bias will grow as the context is indicated more reliably, but also as the stability of the environment increases, and as the number of trials since the last context switch grows. Analysis of human choice data validates all three predictions, suggesting that the brain leverages knowledge of the statistical structure of environmental change when interpreting ambiguous sensory signals."}],"doi":"10.1371/journal.pcbi.1011104","day":"08","file_date_updated":"2023-07-18T08:07:59Z","quality_controlled":"1","article_type":"original","publisher":"Public Library of Science","author":[{"first_name":"Julie A.","last_name":"Charlton","full_name":"Charlton, Julie A."},{"last_name":"Mlynarski","first_name":"Wiktor F","full_name":"Mlynarski, Wiktor F","id":"358A453A-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Bai, Yoon H.","first_name":"Yoon H.","last_name":"Bai"},{"full_name":"Hermundstad, Ann M.","last_name":"Hermundstad","first_name":"Ann M."},{"first_name":"Robbe L.T.","last_name":"Goris","full_name":"Goris, Robbe L.T."}],"issue":"6","_id":"13230","pmid":1,"scopus_import":"1","license":"https://creativecommons.org/licenses/by/4.0/","title":"Environmental dynamics shape perceptual decision bias","intvolume":"        19","publication_status":"published","date_created":"2023-07-16T22:01:09Z","department":[{"_id":"MaJö"}],"article_processing_charge":"No","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","status":"public","file":[{"date_updated":"2023-07-18T08:07:59Z","file_name":"2023_PloSCompBio_Charlton.pdf","content_type":"application/pdf","date_created":"2023-07-18T08:07:59Z","checksum":"800761fa2c647fabd6ad034589bc526e","file_size":2281868,"file_id":"13247","creator":"dernst","relation":"main_file","success":1,"access_level":"open_access"}],"date_published":"2023-06-08T00:00:00Z","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"oa":1,"publication_identifier":{"eissn":["1553-7358"]},"language":[{"iso":"eng"}],"publication":"PLoS Computational Biology","has_accepted_license":"1","month":"06","article_number":"e1011104","oa_version":"Published Version"},{"has_accepted_license":"1","publication":"PLoS Computational Biology","article_number":"e1010983","month":"04","oa_version":"Published Version","language":[{"iso":"eng"}],"type":"journal_article","date_published":"2023-04-01T00:00:00Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"oa":1,"publication_identifier":{"eissn":["1553-7358"]},"status":"public","related_material":{"link":[{"url":"https://github.com/shervinsafavi/gpla.git","relation":"software"}]},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","file":[{"date_created":"2023-04-25T08:59:18Z","file_size":4737671,"checksum":"edeb9d09f3e41ba7c0251308b9e372e7","date_updated":"2023-04-25T08:59:18Z","file_name":"2023_PLoSCompBio_Safavi.pdf","content_type":"application/pdf","access_level":"open_access","relation":"main_file","success":1,"file_id":"12867","creator":"dernst"}],"issue":"4","author":[{"first_name":"Shervin","last_name":"Safavi","full_name":"Safavi, Shervin"},{"full_name":"Panagiotaropoulos, Theofanis I.","first_name":"Theofanis I.","last_name":"Panagiotaropoulos"},{"last_name":"Kapoor","first_name":"Vishal","full_name":"Kapoor, Vishal"},{"id":"44B06F76-F248-11E8-B48F-1D18A9856A87","full_name":"Ramirez Villegas, Juan F","first_name":"Juan F","last_name":"Ramirez Villegas"},{"full_name":"Logothetis, Nikos K.","last_name":"Logothetis","first_name":"Nikos K."},{"full_name":"Besserve, Michel","first_name":"Michel","last_name":"Besserve"}],"scopus_import":"1","_id":"12862","intvolume":"        19","title":"Uncovering the organization of neural circuits with Generalized Phase Locking Analysis","article_processing_charge":"No","date_created":"2023-04-23T22:01:03Z","department":[{"_id":"JoCs"}],"publication_status":"published","file_date_updated":"2023-04-25T08:59:18Z","quality_controlled":"1","article_type":"original","publisher":"Public Library of Science","external_id":{"isi":["000962668700002"]},"isi":1,"year":"2023","citation":{"ista":"Safavi S, Panagiotaropoulos TI, Kapoor V, Ramirez Villegas JF, Logothetis NK, Besserve M. 2023. Uncovering the organization of neural circuits with Generalized Phase Locking Analysis. PLoS Computational Biology. 19(4), e1010983.","mla":"Safavi, Shervin, et al. “Uncovering the Organization of Neural Circuits with Generalized Phase Locking Analysis.” <i>PLoS Computational Biology</i>, vol. 19, no. 4, e1010983, Public Library of Science, 2023, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1010983\">10.1371/journal.pcbi.1010983</a>.","short":"S. Safavi, T.I. Panagiotaropoulos, V. Kapoor, J.F. Ramirez Villegas, N.K. Logothetis, M. Besserve, PLoS Computational Biology 19 (2023).","chicago":"Safavi, Shervin, Theofanis I. Panagiotaropoulos, Vishal Kapoor, Juan F Ramirez Villegas, Nikos K. Logothetis, and Michel Besserve. “Uncovering the Organization of Neural Circuits with Generalized Phase Locking Analysis.” <i>PLoS Computational Biology</i>. Public Library of Science, 2023. <a href=\"https://doi.org/10.1371/journal.pcbi.1010983\">https://doi.org/10.1371/journal.pcbi.1010983</a>.","ieee":"S. Safavi, T. I. Panagiotaropoulos, V. Kapoor, J. F. Ramirez Villegas, N. K. Logothetis, and M. Besserve, “Uncovering the organization of neural circuits with Generalized Phase Locking Analysis,” <i>PLoS Computational Biology</i>, vol. 19, no. 4. Public Library of Science, 2023.","apa":"Safavi, S., Panagiotaropoulos, T. I., Kapoor, V., Ramirez Villegas, J. F., Logothetis, N. K., &#38; Besserve, M. (2023). Uncovering the organization of neural circuits with Generalized Phase Locking Analysis. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1010983\">https://doi.org/10.1371/journal.pcbi.1010983</a>","ama":"Safavi S, Panagiotaropoulos TI, Kapoor V, Ramirez Villegas JF, Logothetis NK, Besserve M. Uncovering the organization of neural circuits with Generalized Phase Locking Analysis. <i>PLoS Computational Biology</i>. 2023;19(4). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1010983\">10.1371/journal.pcbi.1010983</a>"},"date_updated":"2023-08-01T14:15:16Z","abstract":[{"text":"Despite the considerable progress of in vivo neural recording techniques, inferring the biophysical mechanisms underlying large scale coordination of brain activity from neural data remains challenging. One obstacle is the difficulty to link high dimensional functional connectivity measures to mechanistic models of network activity. We address this issue by investigating spike-field coupling (SFC) measurements, which quantify the synchronization between, on the one hand, the action potentials produced by neurons, and on the other hand mesoscopic “field” signals, reflecting subthreshold activities at possibly multiple recording sites. As the number of recording sites gets large, the amount of pairwise SFC measurements becomes overwhelmingly challenging to interpret. We develop Generalized Phase Locking Analysis (GPLA) as an interpretable dimensionality reduction of this multivariate SFC. GPLA describes the dominant coupling between field activity and neural ensembles across space and frequencies. We show that GPLA features are biophysically interpretable when used in conjunction with appropriate network models, such that we can identify the influence of underlying circuit properties on these features. We demonstrate the statistical benefits and interpretability of this approach in various computational models and Utah array recordings. The results suggest that GPLA, used jointly with biophysical modeling, can help uncover the contribution of recurrent microcircuits to the spatio-temporal dynamics observed in multi-channel experimental recordings.","lang":"eng"}],"day":"01","doi":"10.1371/journal.pcbi.1010983","ddc":["570"],"acknowledgement":"We thank Britni Crocker for help with preprocessing of the data and spike sorting; Joachim Werner and Michael Schnabel for their excellent IT support; Andreas Tolias for help with the initial implantation’s of the Utah arrays.\r\nAll authors were supported by the Max Planck Society. M.B. was supported by the German\r\nFederal Ministry of Education and Research (BMBF) through the funding scheme received by\r\nthe Tübingen AI Center, FKZ: 01IS18039B. N.K.L. and V.K. acknowledge the support from the\r\nShanghai Municipal Science and Technology Major Project (Grant No. 2019SHZDZX02). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ","volume":19},{"language":[{"iso":"eng"}],"publication":"PLoS Computational Biology","has_accepted_license":"1","month":"03","article_number":"e1009950","oa_version":"Published Version","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","related_material":{"link":[{"url":"https://gitlab.pasteur.fr/adavidov/inferencelnakf","relation":"software"}]},"file":[{"date_updated":"2022-04-04T10:14:39Z","file_name":"2022_PLoSCompBio_Davidovic.pdf","content_type":"application/pdf","date_created":"2022-04-04T10:14:39Z","checksum":"458ef542761fb714ced214f240daf6b2","file_size":2958642,"file_id":"10947","creator":"dernst","access_level":"open_access","relation":"main_file","success":1}],"date_published":"2022-03-18T00:00:00Z","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"oa":1,"publication_identifier":{"issn":["1553-734X"],"eissn":["1553-7358"]},"file_date_updated":"2022-04-04T10:14:39Z","quality_controlled":"1","article_type":"original","publisher":"Public Library of Science","author":[{"last_name":"Davidović","first_name":"Anđela","full_name":"Davidović, Anđela"},{"last_name":"Chait","first_name":"Remy P","full_name":"Chait, Remy P","orcid":"0000-0003-0876-3187","id":"3464AE84-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Batt, Gregory","first_name":"Gregory","last_name":"Batt"},{"id":"4A245D00-F248-11E8-B48F-1D18A9856A87","last_name":"Ruess","first_name":"Jakob","full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282"}],"issue":"3","_id":"10939","scopus_import":"1","title":"Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level","intvolume":"        18","publication_status":"published","date_created":"2022-04-03T22:01:42Z","department":[{"_id":"CaGu"}],"article_processing_charge":"No","ddc":["570","000"],"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.","volume":18,"date_updated":"2022-04-04T10:21:53Z","year":"2022","citation":{"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>.","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.","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>","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>","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>.","short":"A. Davidović, R.P. Chait, G. Batt, J. Ruess, PLoS Computational Biology 18 (2022)."},"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."}],"doi":"10.1371/journal.pcbi.1009950","day":"18"},{"date_updated":"2023-08-03T14:06:29Z","citation":{"mla":"Christodoulou, Georgia, et al. “Regimes and Mechanisms of Transient Amplification in Abstract and Biological Neural Networks.” <i>PLoS Computational Biology</i>, vol. 18, no. 8, e1010365, Public Library of Science, 2022, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1010365\">10.1371/journal.pcbi.1010365</a>.","short":"G. Christodoulou, T.P. Vogels, E.J. Agnes, PLoS Computational Biology 18 (2022).","ista":"Christodoulou G, Vogels TP, Agnes EJ. 2022. Regimes and mechanisms of transient amplification in abstract and biological neural networks. PLoS Computational Biology. 18(8), e1010365.","apa":"Christodoulou, G., Vogels, T. P., &#38; Agnes, E. J. (2022). Regimes and mechanisms of transient amplification in abstract and biological neural networks. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1010365\">https://doi.org/10.1371/journal.pcbi.1010365</a>","ama":"Christodoulou G, Vogels TP, Agnes EJ. Regimes and mechanisms of transient amplification in abstract and biological neural networks. <i>PLoS Computational Biology</i>. 2022;18(8). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1010365\">10.1371/journal.pcbi.1010365</a>","chicago":"Christodoulou, Georgia, Tim P Vogels, and Everton J. Agnes. “Regimes and Mechanisms of Transient Amplification in Abstract and Biological Neural Networks.” <i>PLoS Computational Biology</i>. Public Library of Science, 2022. <a href=\"https://doi.org/10.1371/journal.pcbi.1010365\">https://doi.org/10.1371/journal.pcbi.1010365</a>.","ieee":"G. Christodoulou, T. P. Vogels, and E. J. Agnes, “Regimes and mechanisms of transient amplification in abstract and biological neural networks,” <i>PLoS Computational Biology</i>, vol. 18, no. 8. Public Library of Science, 2022."},"year":"2022","isi":1,"external_id":{"isi":["000937227700001"]},"doi":"10.1371/journal.pcbi.1010365","day":"15","abstract":[{"text":"Neuronal networks encode information through patterns of activity that define the networks’ function. The neurons’ activity relies on specific connectivity structures, yet the link between structure and function is not fully understood. Here, we tackle this structure-function problem with a new conceptual approach. Instead of manipulating the connectivity directly, we focus on upper triangular matrices, which represent the network dynamics in a given orthonormal basis obtained by the Schur decomposition. This abstraction allows us to independently manipulate the eigenspectrum and feedforward structures of a connectivity matrix. Using this method, we describe a diverse repertoire of non-normal transient amplification, and to complement the analysis of the dynamical regimes, we quantify the geometry of output trajectories through the effective rank of both the eigenvector and the dynamics matrices. Counter-intuitively, we find that shrinking the eigenspectrum’s imaginary distribution leads to highly amplifying regimes in linear and long-lasting dynamics in nonlinear networks. We also find a trade-off between amplification and dimensionality of neuronal dynamics, i.e., trajectories in neuronal state-space. Networks that can amplify a large number of orthogonal initial conditions produce neuronal trajectories that lie in the same subspace of the neuronal state-space. Finally, we examine networks of excitatory and inhibitory neurons. We find that the strength of global inhibition is directly linked with the amplitude of amplification, such that weakening inhibitory weights also decreases amplification, and that the eigenspectrum’s imaginary distribution grows with an increase in the ratio between excitatory-to-inhibitory and excitatory-to-excitatory connectivity strengths. Consequently, the strength of global inhibition reveals itself as a strong signature for amplification and a potential control mechanism to switch dynamical regimes. Our results shed a light on how biological networks, i.e., networks constrained by Dale’s law, may be optimised for specific dynamical regimes.","lang":"eng"}],"volume":18,"acknowledgement":"We thank Friedemann Zenke for his comments, especially on the effect of the self loops on the spectrum. We also thank Ken Miller and Bill Podlaski for helpful comments. This research was funded by a Wellcome Trust and Royal Society Henry Dale Research Fellowship (WT100000; TPV), a Wellcome Senior Research Fellowship (214316/Z/18/Z; GC, EJA, and TPV), and a Research Project Grant by the Leverhulme Trust (RPG-2016-446; EJA and TPV). ","ddc":["570"],"_id":"12084","scopus_import":"1","author":[{"full_name":"Christodoulou, Georgia","last_name":"Christodoulou","first_name":"Georgia"},{"id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","full_name":"Vogels, Tim P","orcid":"0000-0003-3295-6181","last_name":"Vogels","first_name":"Tim P"},{"last_name":"Agnes","first_name":"Everton J.","full_name":"Agnes, Everton J."}],"issue":"8","publication_status":"published","department":[{"_id":"TiVo"}],"article_processing_charge":"No","date_created":"2022-09-11T22:01:56Z","title":"Regimes and mechanisms of transient amplification in abstract and biological neural networks","intvolume":"        18","quality_controlled":"1","file_date_updated":"2022-09-12T07:47:55Z","publisher":"Public Library of Science","article_type":"original","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"date_published":"2022-08-15T00:00:00Z","type":"journal_article","publication_identifier":{"eissn":["1553-7358"]},"oa":1,"file":[{"date_updated":"2022-09-12T07:47:55Z","file_name":"2022_PLoSCompBio_Christodoulou.pdf","content_type":"application/pdf","date_created":"2022-09-12T07:47:55Z","checksum":"8a81ab29f837991ee0ea770817c4a50e","file_size":2867337,"file_id":"12090","creator":"dernst","relation":"main_file","access_level":"open_access","success":1}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","status":"public","publication":"PLoS Computational Biology","has_accepted_license":"1","oa_version":"Published Version","project":[{"grant_number":"214316/Z/18/Z","name":"What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks.","_id":"c084a126-5a5b-11eb-8a69-d75314a70a87"}],"month":"08","article_number":"e1010365","language":[{"iso":"eng"}]},{"publication":"PLOS Computational Biology","has_accepted_license":"1","oa_version":"Published Version","project":[{"call_identifier":"H2020","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","name":"Formal Methods for Stochastic Models: Algorithms and Applications","grant_number":"863818"}],"month":"06","article_number":"e1010149","language":[{"iso":"eng"}],"keyword":["Computational Theory and Mathematics","Cellular and Molecular Neuroscience","Genetics","Molecular Biology","Ecology","Modeling and Simulation","Ecology","Evolution","Behavior and Systematics"],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"date_published":"2022-06-14T00:00:00Z","type":"journal_article","publication_identifier":{"eissn":["1553-7358"]},"oa":1,"file":[{"file_size":3143222,"checksum":"31b6b311b6731f1658277a9dfff6632c","date_created":"2023-01-30T11:28:13Z","file_name":"2022_PlosCompBio_Schmid.pdf","content_type":"application/pdf","date_updated":"2023-01-30T11:28:13Z","access_level":"open_access","success":1,"relation":"main_file","creator":"dernst","file_id":"12460"}],"status":"public","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","pmid":1,"_id":"12280","scopus_import":"1","author":[{"id":"38B437DE-F248-11E8-B48F-1D18A9856A87","first_name":"Laura","last_name":"Schmid","orcid":"0000-0002-6978-7329","full_name":"Schmid, Laura"},{"orcid":"0000-0001-5116-955X","full_name":"Hilbe, Christian","first_name":"Christian","last_name":"Hilbe","id":"2FDF8F3C-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Krishnendu","last_name":"Chatterjee","orcid":"0000-0002-4561-241X","full_name":"Chatterjee, Krishnendu","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Nowak, Martin","last_name":"Nowak","first_name":"Martin"}],"issue":"6","publication_status":"published","article_processing_charge":"No","date_created":"2023-01-16T10:02:51Z","department":[{"_id":"KrCh"}],"title":"Direct reciprocity between individuals that use different strategy spaces","intvolume":"        18","ec_funded":1,"quality_controlled":"1","file_date_updated":"2023-01-30T11:28:13Z","publisher":"Public Library of Science","article_type":"original","date_updated":"2025-07-14T09:09:49Z","year":"2022","citation":{"ista":"Schmid L, Hilbe C, Chatterjee K, Nowak M. 2022. Direct reciprocity between individuals that use different strategy spaces. PLOS Computational Biology. 18(6), e1010149.","short":"L. Schmid, C. Hilbe, K. Chatterjee, M. Nowak, PLOS Computational Biology 18 (2022).","mla":"Schmid, Laura, et al. “Direct Reciprocity between Individuals That Use Different Strategy Spaces.” <i>PLOS Computational Biology</i>, vol. 18, no. 6, e1010149, Public Library of Science, 2022, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1010149\">10.1371/journal.pcbi.1010149</a>.","chicago":"Schmid, Laura, Christian Hilbe, Krishnendu Chatterjee, and Martin Nowak. “Direct Reciprocity between Individuals That Use Different Strategy Spaces.” <i>PLOS Computational Biology</i>. Public Library of Science, 2022. <a href=\"https://doi.org/10.1371/journal.pcbi.1010149\">https://doi.org/10.1371/journal.pcbi.1010149</a>.","ieee":"L. Schmid, C. Hilbe, K. Chatterjee, and M. Nowak, “Direct reciprocity between individuals that use different strategy spaces,” <i>PLOS Computational Biology</i>, vol. 18, no. 6. Public Library of Science, 2022.","apa":"Schmid, L., Hilbe, C., Chatterjee, K., &#38; Nowak, M. (2022). Direct reciprocity between individuals that use different strategy spaces. <i>PLOS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1010149\">https://doi.org/10.1371/journal.pcbi.1010149</a>","ama":"Schmid L, Hilbe C, Chatterjee K, Nowak M. Direct reciprocity between individuals that use different strategy spaces. <i>PLOS Computational Biology</i>. 2022;18(6). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1010149\">10.1371/journal.pcbi.1010149</a>"},"isi":1,"external_id":{"pmid":["35700167"],"isi":["000843626800031"]},"doi":"10.1371/journal.pcbi.1010149","day":"14","abstract":[{"text":"In repeated interactions, players can use strategies that respond to the outcome of previous rounds. Much of the existing literature on direct reciprocity assumes that all competing individuals use the same strategy space. Here, we study both learning and evolutionary dynamics of players that differ in the strategy space they explore. We focus on the infinitely repeated donation game and compare three natural strategy spaces: memory-1 strategies, which consider the last moves of both players, reactive strategies, which respond to the last move of the co-player, and unconditional strategies. These three strategy spaces differ in the memory capacity that is needed. We compute the long term average payoff that is achieved in a pairwise learning process. We find that smaller strategy spaces can dominate larger ones. For weak selection, unconditional players dominate both reactive and memory-1 players. For intermediate selection, reactive players dominate memory-1 players. Only for strong selection and low cost-to-benefit ratio, memory-1 players dominate the others. We observe that the supergame between strategy spaces can be a social dilemma: maximum payoff is achieved if both players explore a larger strategy space, but smaller strategy spaces dominate.","lang":"eng"}],"volume":18,"acknowledgement":"This work was supported by the European Research Council (https://erc.europa.eu/)\r\nCoG 863818 (ForM-SMArt) (to K.C.), and the European Research Council Starting Grant 850529: E-DIRECT (to C.H.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","ddc":["000","570"]},{"external_id":{"arxiv":["2102.03669"],"pmid":["34851948"]},"year":"2021","citation":{"short":"K. Bodova, E. Szep, N.H. Barton, PLoS Computational Biology 17 (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.","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>","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.","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>."},"date_updated":"2022-08-01T10:48:04Z","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."}],"day":"01","doi":"10.1371/journal.pcbi.1009661","arxiv":1,"ddc":["570"],"volume":17,"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.","issue":"12","author":[{"full_name":"Bod'ová, Katarína","orcid":"0000-0002-7214-0171","last_name":"Bod'ová","first_name":"Katarína","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Szep, Eniko","last_name":"Szep","first_name":"Eniko","id":"485BB5A4-F248-11E8-B48F-1D18A9856A87"},{"id":"4880FE40-F248-11E8-B48F-1D18A9856A87","full_name":"Barton, Nicholas H","orcid":"0000-0002-8548-5240","last_name":"Barton","first_name":"Nicholas H"}],"scopus_import":"1","_id":"10535","pmid":1,"intvolume":"        17","title":"Dynamic maximum entropy provides accurate approximation of structured population dynamics","department":[{"_id":"NiBa"},{"_id":"GaTk"}],"article_processing_charge":"No","date_created":"2021-12-12T23:01:27Z","publication_status":"published","file_date_updated":"2022-05-16T08:53:11Z","quality_controlled":"1","article_type":"original","publisher":"Public Library of Science","type":"journal_article","date_published":"2021-12-01T00:00:00Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"oa":1,"publication_identifier":{"eissn":["1553-7358"],"issn":["1553-734X"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","file":[{"content_type":"application/pdf","file_name":"2021_PLOsComBio_Bodova.pdf","date_updated":"2022-05-16T08:53:11Z","file_size":2299486,"checksum":"dcd185d4f7e0acee25edf1d6537f447e","date_created":"2022-05-16T08:53:11Z","creator":"dernst","file_id":"11383","success":1,"access_level":"open_access","relation":"main_file"}],"has_accepted_license":"1","publication":"PLoS Computational Biology","article_number":"e1009661","month":"12","acknowledged_ssus":[{"_id":"ScienComp"}],"oa_version":"Published Version","language":[{"iso":"eng"}]},{"publication_identifier":{"issn":["1553-734X"],"eissn":["1553-7358"]},"oa":1,"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"type":"journal_article","date_published":"2020-11-05T00:00:00Z","file":[{"date_updated":"2020-11-18T07:26:10Z","file_name":"2020_PlosCompBio_Kaveh.pdf","content_type":"application/pdf","date_created":"2020-11-18T07:26:10Z","checksum":"555456dd0e47bcf9e0994bcb95577e88","file_size":2498594,"file_id":"8768","creator":"dernst","success":1,"access_level":"open_access","relation":"main_file"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","status":"public","oa_version":"Published Version","article_number":"e1008402","month":"11","has_accepted_license":"1","publication":"PLOS Computational Biology","keyword":["Ecology","Modelling and Simulation","Computational Theory and Mathematics","Genetics","Ecology","Evolution","Behavior and Systematics","Molecular Biology","Cellular and Molecular Neuroscience"],"language":[{"iso":"eng"}],"day":"05","doi":"10.1371/journal.pcbi.1008402","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"}],"year":"2020","citation":{"ista":"Kaveh K, McAvoy A, Chatterjee K, Nowak MA. 2020. The Moran process on 2-chromatic graphs. PLOS Computational Biology. 16(11), e1008402.","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>.","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>.","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.","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>"},"date_updated":"2023-08-22T12:49:18Z","external_id":{"isi":["000591317200004"]},"isi":1,"volume":16,"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.","ddc":["000"],"article_processing_charge":"No","date_created":"2020-11-18T07:20:23Z","department":[{"_id":"KrCh"}],"publication_status":"published","intvolume":"        16","title":"The Moran process on 2-chromatic graphs","scopus_import":"1","_id":"8767","issue":"11","author":[{"full_name":"Kaveh, Kamran","first_name":"Kamran","last_name":"Kaveh"},{"full_name":"McAvoy, Alex","last_name":"McAvoy","first_name":"Alex"},{"orcid":"0000-0002-4561-241X","full_name":"Chatterjee, Krishnendu","first_name":"Krishnendu","last_name":"Chatterjee","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Nowak, Martin A.","last_name":"Nowak","first_name":"Martin A."}],"publisher":"Public Library of Science","article_type":"original","quality_controlled":"1","file_date_updated":"2020-11-18T07:26:10Z"},{"language":[{"iso":"eng"}],"project":[{"_id":"251D65D8-B435-11E9-9278-68D0E5697425","grant_number":"24210","name":"Effects of Stochasticity on the Function of Restriction-Modi cation Systems at the Single-Cell Level"},{"_id":"251BCBEC-B435-11E9-9278-68D0E5697425","name":"Multi-Level Conflicts in Evolutionary Dynamics of Restriction-Modification Systems","grant_number":"RGY0079/2011"}],"oa_version":"Published Version","article_number":"e1007168","month":"07","has_accepted_license":"1","publication":"PLoS Computational Biology","file":[{"content_type":"application/pdf","file_name":"2019_PlosComputBiology_Ruess.pdf","date_updated":"2020-07-14T12:47:40Z","checksum":"7ded4721b41c2a0fc66a1c634540416a","file_size":2200003,"date_created":"2019-08-12T12:27:26Z","creator":"dernst","file_id":"6803","relation":"main_file","access_level":"open_access"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","related_material":{"record":[{"status":"public","id":"9786","relation":"research_data"}]},"status":"public","publication_identifier":{"eissn":["1553-7358"]},"oa":1,"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"type":"journal_article","date_published":"2019-07-02T00:00:00Z","publisher":"Public Library of Science","article_type":"original","quality_controlled":"1","file_date_updated":"2020-07-14T12:47:40Z","date_created":"2019-08-11T21:59:19Z","article_processing_charge":"No","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"publication_status":"published","intvolume":"        15","title":"Molecular noise of innate immunity shapes bacteria-phage ecologies","scopus_import":"1","_id":"6784","issue":"7","author":[{"last_name":"Ruess","first_name":"Jakob","full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282","id":"4A245D00-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Pleska, Maros","orcid":"0000-0001-7460-7479","last_name":"Pleska","first_name":"Maros","id":"4569785E-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Guet","first_name":"Calin C","full_name":"Guet, Calin C","orcid":"0000-0001-6220-2052","id":"47F8433E-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0002-6699-1455","full_name":"Tkačik, Gašper","first_name":"Gašper","last_name":"Tkačik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"volume":15,"ddc":["570"],"day":"02","doi":"10.1371/journal.pcbi.1007168","abstract":[{"lang":"eng","text":"Mathematical models have been used successfully at diverse scales of biological organization, ranging from ecology and population dynamics to stochastic reaction events occurring between individual molecules in single cells. Generally, many biological processes unfold across multiple scales, with mutations being the best studied example of how stochasticity at the molecular scale can influence outcomes at the population scale. In many other contexts, however, an analogous link between micro- and macro-scale remains elusive, primarily due to the challenges involved in setting up and analyzing multi-scale models. Here, we employ such a model to investigate how stochasticity propagates from individual biochemical reaction events in the bacterial innate immune system to the ecology of bacteria and bacterial viruses. We show analytically how the dynamics of bacterial populations are shaped by the activities of immunity-conferring enzymes in single cells and how the ecological consequences imply optimal bacterial defense strategies against viruses. Our results suggest that bacterial populations in the presence of viruses can either optimize their initial growth rate or their population size, with the first strategy favoring simple immunity featuring a single restriction modification system and the second strategy favoring complex bacterial innate immunity featuring several simultaneously active restriction modification systems."}],"year":"2019","citation":{"chicago":"Ruess, Jakob, Maros Pleska, Calin C Guet, and Gašper Tkačik. “Molecular Noise of Innate Immunity Shapes Bacteria-Phage Ecologies.” <i>PLoS Computational Biology</i>. Public Library of Science, 2019. <a href=\"https://doi.org/10.1371/journal.pcbi.1007168\">https://doi.org/10.1371/journal.pcbi.1007168</a>.","ieee":"J. Ruess, M. Pleska, C. C. Guet, and G. Tkačik, “Molecular noise of innate immunity shapes bacteria-phage ecologies,” <i>PLoS Computational Biology</i>, vol. 15, no. 7. Public Library of Science, 2019.","ama":"Ruess J, Pleska M, Guet CC, Tkačik G. Molecular noise of innate immunity shapes bacteria-phage ecologies. <i>PLoS Computational Biology</i>. 2019;15(7). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007168\">10.1371/journal.pcbi.1007168</a>","apa":"Ruess, J., Pleska, M., Guet, C. C., &#38; Tkačik, G. (2019). Molecular noise of innate immunity shapes bacteria-phage ecologies. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1007168\">https://doi.org/10.1371/journal.pcbi.1007168</a>","ista":"Ruess J, Pleska M, Guet CC, Tkačik G. 2019. Molecular noise of innate immunity shapes bacteria-phage ecologies. PLoS Computational Biology. 15(7), e1007168.","short":"J. Ruess, M. Pleska, C.C. Guet, G. Tkačik, PLoS Computational Biology 15 (2019).","mla":"Ruess, Jakob, et al. “Molecular Noise of Innate Immunity Shapes Bacteria-Phage Ecologies.” <i>PLoS Computational Biology</i>, vol. 15, no. 7, e1007168, Public Library of Science, 2019, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007168\">10.1371/journal.pcbi.1007168</a>."},"date_updated":"2023-08-29T07:10:06Z","external_id":{"isi":["000481577700032"]},"isi":1}]