--- _id: '11638' abstract: - lang: eng text: 'Statistical inference is central to many scientific endeavors, yet how it works remains unresolved. Answering this requires a quantitative understanding of the intrinsic interplay between statistical models, inference methods, and the structure in the data. To this end, we characterize the efficacy of direct coupling analysis (DCA)—a highly successful method for analyzing amino acid sequence data—in inferring pairwise interactions from samples of ferromagnetic Ising models on random graphs. Our approach allows for physically motivated exploration of qualitatively distinct data regimes separated by phase transitions. We show that inference quality depends strongly on the nature of data-generating distributions: optimal accuracy occurs at an intermediate temperature where the detrimental effects from macroscopic order and thermal noise are minimal. Importantly our results indicate that DCA does not always outperform its local-statistics-based predecessors; while DCA excels at low temperatures, it becomes inferior to simple correlation thresholding at virtually all temperatures when data are limited. Our findings offer insights into the regime in which DCA operates so successfully, and more broadly, how inference interacts with the structure in the data.' acknowledgement: This work was supported in part by the Alfred P. Sloan Foundation, the Simons Foundation, the National Institutes of Health under Award No. R01EB026943, and the National Science Foundation, through the Center for the Physics of Biological Function (PHY-1734030). article_number: '023240' article_processing_charge: No article_type: original author: - first_name: Vudtiwat full_name: Ngampruetikorn, Vudtiwat last_name: Ngampruetikorn - first_name: Vedant full_name: Sachdeva, Vedant last_name: Sachdeva - first_name: Johanna full_name: Torrence, Johanna last_name: Torrence - first_name: Jan full_name: Humplik, Jan id: 2E9627A8-F248-11E8-B48F-1D18A9856A87 last_name: Humplik - first_name: David J. full_name: Schwab, David J. last_name: Schwab - first_name: Stephanie E. full_name: Palmer, Stephanie E. last_name: Palmer citation: ama: Ngampruetikorn V, Sachdeva V, Torrence J, Humplik J, Schwab DJ, Palmer SE. Inferring couplings in networks across order-disorder phase transitions. Physical Review Research. 2022;4(2). doi:10.1103/PhysRevResearch.4.023240 apa: Ngampruetikorn, V., Sachdeva, V., Torrence, J., Humplik, J., Schwab, D. J., & Palmer, S. E. (2022). Inferring couplings in networks across order-disorder phase transitions. Physical Review Research. American Physical Society. https://doi.org/10.1103/PhysRevResearch.4.023240 chicago: Ngampruetikorn, Vudtiwat, Vedant Sachdeva, Johanna Torrence, Jan Humplik, David J. Schwab, and Stephanie E. Palmer. “Inferring Couplings in Networks across Order-Disorder Phase Transitions.” Physical Review Research. American Physical Society, 2022. https://doi.org/10.1103/PhysRevResearch.4.023240. ieee: V. Ngampruetikorn, V. Sachdeva, J. Torrence, J. Humplik, D. J. Schwab, and S. E. Palmer, “Inferring couplings in networks across order-disorder phase transitions,” Physical Review Research, vol. 4, no. 2. American Physical Society, 2022. ista: Ngampruetikorn V, Sachdeva V, Torrence J, Humplik J, Schwab DJ, Palmer SE. 2022. Inferring couplings in networks across order-disorder phase transitions. Physical Review Research. 4(2), 023240. mla: Ngampruetikorn, Vudtiwat, et al. “Inferring Couplings in Networks across Order-Disorder Phase Transitions.” Physical Review Research, vol. 4, no. 2, 023240, American Physical Society, 2022, doi:10.1103/PhysRevResearch.4.023240. short: V. Ngampruetikorn, V. Sachdeva, J. Torrence, J. Humplik, D.J. Schwab, S.E. Palmer, Physical Review Research 4 (2022). date_created: 2022-07-24T22:01:42Z date_published: 2022-06-24T00:00:00Z date_updated: 2022-07-25T07:52:35Z day: '24' ddc: - '530' department: - _id: GaTk doi: 10.1103/PhysRevResearch.4.023240 external_id: arxiv: - '2106.02349' file: - access_level: open_access checksum: ed6fdc2a3a096df785fa5f7b17b716c6 content_type: application/pdf creator: dernst date_created: 2022-07-25T07:47:23Z date_updated: 2022-07-25T07:47:23Z file_id: '11644' file_name: 2022_PhysicalReviewResearch_Ngampruetikorn.pdf file_size: 1379683 relation: main_file success: 1 file_date_updated: 2022-07-25T07:47:23Z funded_apc: '1' has_accepted_license: '1' intvolume: ' 4' issue: '2' language: - iso: eng month: '06' oa: 1 oa_version: Published Version publication: Physical Review Research publication_identifier: issn: - 2643-1564 publication_status: published publisher: American Physical Society quality_controlled: '1' scopus_import: '1' status: public title: Inferring couplings in networks across order-disorder phase transitions tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 4 year: '2022' ... --- _id: '12156' abstract: - lang: eng text: Models of transcriptional regulation that assume equilibrium binding of transcription factors have been less successful at predicting gene expression from sequence in eukaryotes than in bacteria. This could be due to the non-equilibrium nature of eukaryotic regulation. Unfortunately, the space of possible non-equilibrium mechanisms is vast and predominantly uninteresting. The key question is therefore how this space can be navigated efficiently, to focus on mechanisms and models that are biologically relevant. In this review, we advocate for the normative role of theory—theory that prescribes rather than just describes—in providing such a focus. Theory should expand its remit beyond inferring mechanistic models from data, towards identifying non-equilibrium gene regulatory schemes that may have been evolutionarily selected, despite their energy consumption, because they are precise, reliable, fast, or otherwise outperform regulation at equilibrium. We illustrate our reasoning by toy examples for which we provide simulation code. acknowledgement: 'This work was supported through the Center for the Physics of Biological Function (PHYe1734030) and by National Institutes of Health Grants R01GM097275 and U01DK127429 (TG). GT acknowledges the support of the Austrian Science Fund grant FWF P28844 and the Human Frontiers Science Program. ' article_number: '100435' article_processing_charge: Yes (via OA deal) article_type: original author: - first_name: Benjamin full_name: Zoller, Benjamin last_name: Zoller - first_name: Thomas full_name: Gregor, Thomas last_name: Gregor - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: '1' citation: ama: Zoller B, Gregor T, Tkačik G. Eukaryotic gene regulation at equilibrium, or non? Current Opinion in Systems Biology. 2022;31(9). doi:10.1016/j.coisb.2022.100435 apa: Zoller, B., Gregor, T., & Tkačik, G. (2022). Eukaryotic gene regulation at equilibrium, or non? Current Opinion in Systems Biology. Elsevier. https://doi.org/10.1016/j.coisb.2022.100435 chicago: Zoller, Benjamin, Thomas Gregor, and Gašper Tkačik. “Eukaryotic Gene Regulation at Equilibrium, or Non?” Current Opinion in Systems Biology. Elsevier, 2022. https://doi.org/10.1016/j.coisb.2022.100435. ieee: B. Zoller, T. Gregor, and G. Tkačik, “Eukaryotic gene regulation at equilibrium, or non?,” Current Opinion in Systems Biology, vol. 31, no. 9. Elsevier, 2022. ista: Zoller B, Gregor T, Tkačik G. 2022. Eukaryotic gene regulation at equilibrium, or non? Current Opinion in Systems Biology. 31(9), 100435. mla: Zoller, Benjamin, et al. “Eukaryotic Gene Regulation at Equilibrium, or Non?” Current Opinion in Systems Biology, vol. 31, no. 9, 100435, Elsevier, 2022, doi:10.1016/j.coisb.2022.100435. short: B. Zoller, T. Gregor, G. Tkačik, Current Opinion in Systems Biology 31 (2022). date_created: 2023-01-12T12:08:51Z date_published: 2022-09-01T00:00:00Z date_updated: 2023-02-13T09:20:34Z day: '01' ddc: - '570' department: - _id: GaTk doi: 10.1016/j.coisb.2022.100435 file: - access_level: open_access checksum: 97ef01e0cc60cdc84f45640a0f248fb0 content_type: application/pdf creator: dernst date_created: 2023-01-24T12:14:10Z date_updated: 2023-01-24T12:14:10Z file_id: '12362' file_name: 2022_CurrentBiology_Zoller.pdf file_size: 2214944 relation: main_file success: 1 file_date_updated: 2023-01-24T12:14:10Z has_accepted_license: '1' intvolume: ' 31' issue: '9' keyword: - Applied Mathematics - Computer Science Applications - Drug Discovery - General Biochemistry - Genetics and Molecular Biology - Modeling and Simulation language: - iso: eng month: '09' oa: 1 oa_version: Published Version project: - _id: 254E9036-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P28844-B27 name: Biophysics of information processing in gene regulation publication: Current Opinion in Systems Biology publication_identifier: issn: - 2452-3100 publication_status: published publisher: Elsevier quality_controlled: '1' scopus_import: '1' status: public title: Eukaryotic gene regulation at equilibrium, or non? tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 31 year: '2022' ... --- _id: '10530' abstract: - lang: eng text: "Cell dispersion from a confined area is fundamental in a number of biological processes,\r\nincluding cancer metastasis. To date, a quantitative understanding of the interplay of single\r\ncell motility, cell proliferation, and intercellular contacts remains elusive. In particular, the role\r\nof E- and N-Cadherin junctions, central components of intercellular contacts, is still\r\ncontroversial. Combining theoretical modeling with in vitro observations, we investigate the\r\ncollective spreading behavior of colonies of human cancer cells (T24). The spreading of these\r\ncolonies is driven by stochastic single-cell migration with frequent transient cell-cell contacts.\r\nWe find that inhibition of E- and N-Cadherin junctions decreases colony spreading and average\r\nspreading velocities, without affecting the strength of correlations in spreading velocities of\r\nneighboring cells. Based on a biophysical simulation model for cell migration, we show that the\r\nbehavioral changes upon disruption of these junctions can be explained by reduced repulsive\r\nexcluded volume interactions between cells. This suggests that in cancer cell migration,\r\ncadherin-based intercellular contacts sharpen cell boundaries leading to repulsive rather than\r\ncohesive interactions between cells, thereby promoting efficient cell spreading during collective\r\nmigration.\r\n" acknowledgement: Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project-ID 201269156 - SFB 1032 (Projects B8 and B12). D.B.B. is supported in part by a DFG fellowship within the Graduate School of Quantitative Biosciences Munich (QBM) and by the Joachim Herz Stiftung. article_processing_charge: No article_type: original author: - first_name: Themistoklis full_name: Zisis, Themistoklis last_name: Zisis - first_name: David full_name: Brückner, David id: e1e86031-6537-11eb-953a-f7ab92be508d last_name: Brückner orcid: 0000-0001-7205-2975 - first_name: Tom full_name: Brandstätter, Tom last_name: Brandstätter - first_name: Wei Xiong full_name: Siow, Wei Xiong last_name: Siow - first_name: Joseph full_name: d’Alessandro, Joseph last_name: d’Alessandro - first_name: Angelika M. full_name: Vollmar, Angelika M. last_name: Vollmar - first_name: Chase P. full_name: Broedersz, Chase P. last_name: Broedersz - first_name: Stefan full_name: Zahler, Stefan last_name: Zahler citation: ama: Zisis T, Brückner D, Brandstätter T, et al. Disentangling cadherin-mediated cell-cell interactions in collective cancer cell migration. Biophysical Journal. 2022;121(1):P44-60. doi:10.1016/j.bpj.2021.12.006 apa: Zisis, T., Brückner, D., Brandstätter, T., Siow, W. X., d’Alessandro, J., Vollmar, A. M., … Zahler, S. (2022). Disentangling cadherin-mediated cell-cell interactions in collective cancer cell migration. Biophysical Journal. Elsevier. https://doi.org/10.1016/j.bpj.2021.12.006 chicago: Zisis, Themistoklis, David Brückner, Tom Brandstätter, Wei Xiong Siow, Joseph d’Alessandro, Angelika M. Vollmar, Chase P. Broedersz, and Stefan Zahler. “Disentangling Cadherin-Mediated Cell-Cell Interactions in Collective Cancer Cell Migration.” Biophysical Journal. Elsevier, 2022. https://doi.org/10.1016/j.bpj.2021.12.006. ieee: T. Zisis et al., “Disentangling cadherin-mediated cell-cell interactions in collective cancer cell migration,” Biophysical Journal, vol. 121, no. 1. Elsevier, pp. P44-60, 2022. ista: Zisis T, Brückner D, Brandstätter T, Siow WX, d’Alessandro J, Vollmar AM, Broedersz CP, Zahler S. 2022. Disentangling cadherin-mediated cell-cell interactions in collective cancer cell migration. Biophysical Journal. 121(1), P44-60. mla: Zisis, Themistoklis, et al. “Disentangling Cadherin-Mediated Cell-Cell Interactions in Collective Cancer Cell Migration.” Biophysical Journal, vol. 121, no. 1, Elsevier, 2022, pp. P44-60, doi:10.1016/j.bpj.2021.12.006. short: T. Zisis, D. Brückner, T. Brandstätter, W.X. Siow, J. d’Alessandro, A.M. Vollmar, C.P. Broedersz, S. Zahler, Biophysical Journal 121 (2022) P44-60. date_created: 2021-12-10T09:48:19Z date_published: 2022-01-04T00:00:00Z date_updated: 2023-08-02T13:34:25Z day: '04' ddc: - '570' department: - _id: EdHa - _id: GaTk doi: 10.1016/j.bpj.2021.12.006 external_id: isi: - '000740815400007' file: - access_level: open_access checksum: 1aa7c3478e0c8256b973b632efd1f6b4 content_type: application/pdf creator: dernst date_created: 2022-07-29T10:17:10Z date_updated: 2022-07-29T10:17:10Z file_id: '11697' file_name: 2022_BiophysicalJour_Zisis.pdf file_size: 4475504 relation: main_file success: 1 file_date_updated: 2022-07-29T10:17:10Z has_accepted_license: '1' intvolume: ' 121' isi: 1 issue: '1' keyword: - Biophysics language: - iso: eng month: '01' oa: 1 oa_version: Published Version page: P44-60 project: - _id: 9B861AAC-BA93-11EA-9121-9846C619BF3A name: NOMIS Fellowship Program publication: Biophysical Journal publication_identifier: issn: - 0006-3495 publication_status: published publisher: Elsevier quality_controlled: '1' status: public title: Disentangling cadherin-mediated cell-cell interactions in collective cancer cell migration tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 121 year: '2022' ... --- _id: '10736' abstract: - lang: eng text: Predicting function from sequence is a central problem of biology. Currently, this is possible only locally in a narrow mutational neighborhood around a wildtype sequence rather than globally from any sequence. Using random mutant libraries, we developed a biophysical model that accounts for multiple features of σ70 binding bacterial promoters to predict constitutive gene expression levels from any sequence. We experimentally and theoretically estimated that 10–20% of random sequences lead to expression and ~80% of non-expressing sequences are one mutation away from a functional promoter. The potential for generating expression from random sequences is so pervasive that selection acts against σ70-RNA polymerase binding sites even within inter-genic, promoter-containing regions. This pervasiveness of σ70-binding sites implies that emergence of promoters is not the limiting step in gene regulatory evolution. Ultimately, the inclusion of novel features of promoter function into a mechanistic model enabled not only more accurate predictions of gene expression levels, but also identified that promoters evolve more rapidly than previously thought. acknowledgement: 'We thank Hande Acar, Nicholas H Barton, Rok Grah, Tiago Paixao, Maros Pleska, Anna Staron, and Murat Tugrul for insightful comments and input on the manuscript. This work was supported by: Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (grant number 216779/Z/19/Z) to ML; IPC Grant from IST Austria to ML and SS; European Research Council Funding Programme 7 (2007–2013, grant agreement number 648440) to JPB.' article_number: e64543 article_processing_charge: No article_type: original author: - first_name: Mato full_name: Lagator, Mato id: 345D25EC-F248-11E8-B48F-1D18A9856A87 last_name: Lagator - first_name: Srdjan full_name: Sarikas, Srdjan id: 35F0286E-F248-11E8-B48F-1D18A9856A87 last_name: Sarikas - first_name: Magdalena full_name: Steinrueck, Magdalena last_name: Steinrueck - first_name: David full_name: Toledo-Aparicio, David last_name: Toledo-Aparicio - first_name: Jonathan P full_name: Bollback, Jonathan P id: 2C6FA9CC-F248-11E8-B48F-1D18A9856A87 last_name: Bollback orcid: 0000-0002-4624-4612 - first_name: Calin C full_name: Guet, Calin C id: 47F8433E-F248-11E8-B48F-1D18A9856A87 last_name: Guet orcid: 0000-0001-6220-2052 - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 citation: ama: Lagator M, Sarikas S, Steinrueck M, et al. Predicting bacterial promoter function and evolution from random sequences. eLife. 2022;11. doi:10.7554/eLife.64543 apa: Lagator, M., Sarikas, S., Steinrueck, M., Toledo-Aparicio, D., Bollback, J. P., Guet, C. C., & Tkačik, G. (2022). Predicting bacterial promoter function and evolution from random sequences. ELife. eLife Sciences Publications. https://doi.org/10.7554/eLife.64543 chicago: Lagator, Mato, Srdjan Sarikas, Magdalena Steinrueck, David Toledo-Aparicio, Jonathan P Bollback, Calin C Guet, and Gašper Tkačik. “Predicting Bacterial Promoter Function and Evolution from Random Sequences.” ELife. eLife Sciences Publications, 2022. https://doi.org/10.7554/eLife.64543. ieee: M. Lagator et al., “Predicting bacterial promoter function and evolution from random sequences,” eLife, vol. 11. eLife Sciences Publications, 2022. ista: Lagator M, Sarikas S, Steinrueck M, Toledo-Aparicio D, Bollback JP, Guet CC, Tkačik G. 2022. Predicting bacterial promoter function and evolution from random sequences. eLife. 11, e64543. mla: Lagator, Mato, et al. “Predicting Bacterial Promoter Function and Evolution from Random Sequences.” ELife, vol. 11, e64543, eLife Sciences Publications, 2022, doi:10.7554/eLife.64543. short: M. Lagator, S. Sarikas, M. Steinrueck, D. Toledo-Aparicio, J.P. Bollback, C.C. Guet, G. Tkačik, ELife 11 (2022). date_created: 2022-02-06T23:01:32Z date_published: 2022-01-26T00:00:00Z date_updated: 2023-08-02T14:09:02Z day: '26' ddc: - '576' department: - _id: CaGu - _id: GaTk - _id: NiBa doi: 10.7554/eLife.64543 ec_funded: 1 external_id: isi: - '000751104400001' pmid: - '35080492' file: - access_level: open_access checksum: decdcdf600ff51e9a9703b49ca114170 content_type: application/pdf creator: cchlebak date_created: 2022-02-07T07:14:09Z date_updated: 2022-02-07T07:14:09Z file_id: '10739' file_name: 2022_ELife_Lagator.pdf file_size: 5604343 relation: main_file success: 1 file_date_updated: 2022-02-07T07:14:09Z has_accepted_license: '1' intvolume: ' 11' isi: 1 language: - iso: eng month: '01' oa: 1 oa_version: Published Version pmid: 1 project: - _id: 2578D616-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '648440' name: Selective Barriers to Horizontal Gene Transfer publication: eLife publication_identifier: eissn: - 2050-084X publication_status: published publisher: eLife Sciences Publications quality_controlled: '1' scopus_import: '1' status: public title: Predicting bacterial promoter function and evolution from random sequences tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 11 year: '2022' ... --- _id: '12332' abstract: - lang: eng text: Activity of sensory neurons is driven not only by external stimuli but also by feedback signals from higher brain areas. Attention is one particularly important internal signal whose presumed role is to modulate sensory representations such that they only encode information currently relevant to the organism at minimal cost. This hypothesis has, however, not yet been expressed in a normative computational framework. Here, by building on normative principles of probabilistic inference and efficient coding, we developed a model of dynamic population coding in the visual cortex. By continuously adapting the sensory code to changing demands of the perceptual observer, an attention-like modulation emerges. This modulation can dramatically reduce the amount of neural activity without deteriorating the accuracy of task-specific inferences. Our results suggest that a range of seemingly disparate cortical phenomena such as intrinsic gain modulation, attention-related tuning modulation, and response variability could be manifestations of the same underlying principles, which combine efficient sensory coding with optimal probabilistic inference in dynamic environments. acknowledgement: "We thank Robbe Goris for generously providing figures from his work and Ann M. Hermundstad for helpful discussions.\r\nGT & WM were supported by the Austrian Science Fund Standalone Grant P 34015 \"Efficient Coding with Biophysical Realism\" (https://pf.fwf.ac.at/) WM was additionally supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 754411 (https://ec.europa.eu/research/mariecurieactions/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript." article_processing_charge: No article_type: original author: - first_name: Wiktor F full_name: Mlynarski, Wiktor F id: 358A453A-F248-11E8-B48F-1D18A9856A87 last_name: Mlynarski - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: '1' citation: ama: Mlynarski WF, Tkačik G. Efficient coding theory of dynamic attentional modulation. PLoS Biology. 2022;20(12):e3001889. doi:10.1371/journal.pbio.3001889 apa: Mlynarski, W. F., & Tkačik, G. (2022). Efficient coding theory of dynamic attentional modulation. PLoS Biology. Public Library of Science. https://doi.org/10.1371/journal.pbio.3001889 chicago: Mlynarski, Wiktor F, and Gašper Tkačik. “Efficient Coding Theory of Dynamic Attentional Modulation.” PLoS Biology. Public Library of Science, 2022. https://doi.org/10.1371/journal.pbio.3001889. ieee: W. F. Mlynarski and G. Tkačik, “Efficient coding theory of dynamic attentional modulation,” PLoS Biology, vol. 20, no. 12. Public Library of Science, p. e3001889, 2022. ista: Mlynarski WF, Tkačik G. 2022. Efficient coding theory of dynamic attentional modulation. PLoS Biology. 20(12), e3001889. mla: Mlynarski, Wiktor F., and Gašper Tkačik. “Efficient Coding Theory of Dynamic Attentional Modulation.” PLoS Biology, vol. 20, no. 12, Public Library of Science, 2022, p. e3001889, doi:10.1371/journal.pbio.3001889. short: W.F. Mlynarski, G. Tkačik, PLoS Biology 20 (2022) e3001889. date_created: 2023-01-22T23:00:55Z date_published: 2022-12-21T00:00:00Z date_updated: 2023-08-03T14:23:49Z day: '21' ddc: - '570' department: - _id: GaTk doi: 10.1371/journal.pbio.3001889 ec_funded: 1 external_id: isi: - '000925192000001' file: - access_level: open_access checksum: 5d7f1111a87e5f2c1bf92f8886738894 content_type: application/pdf creator: dernst date_created: 2023-01-23T08:46:40Z date_updated: 2023-01-23T08:46:40Z file_id: '12337' file_name: 2022_PloSBiology_Mlynarski.pdf file_size: 4248838 relation: main_file success: 1 file_date_updated: 2023-01-23T08:46:40Z has_accepted_license: '1' intvolume: ' 20' isi: 1 issue: '12' language: - iso: eng month: '12' oa: 1 oa_version: Published Version page: e3001889 project: - _id: 626c45b5-2b32-11ec-9570-e509828c1ba6 grant_number: P34015 name: Efficient coding with biophysical realism - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships publication: PLoS Biology publication_identifier: eissn: - 1545-7885 publication_status: published publisher: Public Library of Science quality_controlled: '1' scopus_import: '1' status: public title: Efficient coding theory of dynamic attentional modulation tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 20 year: '2022' ... --- _id: '12081' abstract: - lang: eng text: 'Selection accumulates information in the genome—it guides stochastically evolving populations toward states (genotype frequencies) that would be unlikely under neutrality. This can be quantified as the Kullback–Leibler (KL) divergence between the actual distribution of genotype frequencies and the corresponding neutral distribution. First, we show that this population-level information sets an upper bound on the information at the level of genotype and phenotype, limiting how precisely they can be specified by selection. Next, we study how the accumulation and maintenance of information is limited by the cost of selection, measured as the genetic load or the relative fitness variance, both of which we connect to the control-theoretic KL cost of control. The information accumulation rate is upper bounded by the population size times the cost of selection. This bound is very general, and applies across models (Wright–Fisher, Moran, diffusion) and to arbitrary forms of selection, mutation, and recombination. Finally, the cost of maintaining information depends on how it is encoded: Specifying a single allele out of two is expensive, but one bit encoded among many weakly specified loci (as in a polygenic trait) is cheap.' acknowledgement: We thank Ksenia Khudiakova, Wiktor Młynarski, Sean Stankowski, and two anonymous reviewers for discussions and comments on the manuscript. G.T. and M.H. acknowledge funding from the Human Frontier Science Program Grant RGP0032/2018. N.B. acknowledges funding from ERC Grant 250152 “Information and Evolution.” article_number: e2123152119 article_processing_charge: No article_type: original author: - first_name: Michal full_name: Hledik, Michal id: 4171253A-F248-11E8-B48F-1D18A9856A87 last_name: Hledik - first_name: Nicholas H full_name: Barton, Nicholas H id: 4880FE40-F248-11E8-B48F-1D18A9856A87 last_name: Barton orcid: 0000-0002-8548-5240 - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: '1' citation: ama: Hledik M, Barton NH, Tkačik G. Accumulation and maintenance of information in evolution. Proceedings of the National Academy of Sciences. 2022;119(36). doi:10.1073/pnas.2123152119 apa: Hledik, M., Barton, N. H., & Tkačik, G. (2022). Accumulation and maintenance of information in evolution. Proceedings of the National Academy of Sciences. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.2123152119 chicago: Hledik, Michal, Nicholas H Barton, and Gašper Tkačik. “Accumulation and Maintenance of Information in Evolution.” Proceedings of the National Academy of Sciences. Proceedings of the National Academy of Sciences, 2022. https://doi.org/10.1073/pnas.2123152119. ieee: M. Hledik, N. H. Barton, and G. Tkačik, “Accumulation and maintenance of information in evolution,” Proceedings of the National Academy of Sciences, vol. 119, no. 36. Proceedings of the National Academy of Sciences, 2022. ista: Hledik M, Barton NH, Tkačik G. 2022. Accumulation and maintenance of information in evolution. Proceedings of the National Academy of Sciences. 119(36), e2123152119. mla: Hledik, Michal, et al. “Accumulation and Maintenance of Information in Evolution.” Proceedings of the National Academy of Sciences, vol. 119, no. 36, e2123152119, Proceedings of the National Academy of Sciences, 2022, doi:10.1073/pnas.2123152119. short: M. Hledik, N.H. Barton, G. Tkačik, Proceedings of the National Academy of Sciences 119 (2022). date_created: 2022-09-11T22:01:55Z date_published: 2022-08-29T00:00:00Z date_updated: 2024-03-06T14:22:51Z day: '29' ddc: - '570' department: - _id: NiBa - _id: GaTk doi: 10.1073/pnas.2123152119 ec_funded: 1 external_id: isi: - '000889278400014' pmid: - '36037343' file: - access_level: open_access checksum: 6dec51f6567da9039982a571508a8e4d content_type: application/pdf creator: dernst date_created: 2022-09-12T08:08:12Z date_updated: 2022-09-12T08:08:12Z file_id: '12091' file_name: 2022_PNAS_Hledik.pdf file_size: 2165752 relation: main_file success: 1 file_date_updated: 2022-09-12T08:08:12Z has_accepted_license: '1' intvolume: ' 119' isi: 1 issue: '36' language: - iso: eng month: '08' oa: 1 oa_version: Published Version pmid: 1 project: - _id: 25B07788-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '250152' name: Limits to selection in biology and in evolutionary computation - _id: 2665AAFE-B435-11E9-9278-68D0E5697425 grant_number: RGP0034/2018 name: Can evolution minimize spurious signaling crosstalk to reach optimal performance? publication: Proceedings of the National Academy of Sciences publication_identifier: eissn: - 1091-6490 issn: - 0027-8424 publication_status: published publisher: Proceedings of the National Academy of Sciences quality_controlled: '1' related_material: record: - id: '15020' relation: dissertation_contains status: public scopus_import: '1' status: public title: Accumulation and maintenance of information in evolution tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 119 year: '2022' ... --- _id: '10535' 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. acknowledged_ssus: - _id: ScienComp 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." article_number: e1009661 article_processing_charge: No article_type: original author: - first_name: Katarína full_name: Bod'ová, Katarína id: 2BA24EA0-F248-11E8-B48F-1D18A9856A87 last_name: Bod'ová orcid: 0000-0002-7214-0171 - first_name: Eniko full_name: Szep, Eniko id: 485BB5A4-F248-11E8-B48F-1D18A9856A87 last_name: Szep - first_name: Nicholas H full_name: Barton, Nicholas H id: 4880FE40-F248-11E8-B48F-1D18A9856A87 last_name: Barton orcid: 0000-0002-8548-5240 citation: ama: Bodova K, Szep E, Barton NH. Dynamic maximum entropy provides accurate approximation of structured population dynamics. PLoS Computational Biology. 2021;17(12). doi:10.1371/journal.pcbi.1009661 apa: Bodova, K., Szep, E., & Barton, N. H. (2021). Dynamic maximum entropy provides accurate approximation of structured population dynamics. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1009661 chicago: Bodova, Katarina, Eniko Szep, and Nicholas H Barton. “Dynamic Maximum Entropy Provides Accurate Approximation of Structured Population Dynamics.” PLoS Computational Biology. Public Library of Science, 2021. https://doi.org/10.1371/journal.pcbi.1009661. ieee: K. Bodova, E. Szep, and N. H. Barton, “Dynamic maximum entropy provides accurate approximation of structured population dynamics,” PLoS Computational Biology, vol. 17, no. 12. Public Library of Science, 2021. ista: Bodova K, Szep E, Barton NH. 2021. Dynamic maximum entropy provides accurate approximation of structured population dynamics. PLoS Computational Biology. 17(12), e1009661. mla: Bodova, Katarina, et al. “Dynamic Maximum Entropy Provides Accurate Approximation of Structured Population Dynamics.” PLoS Computational Biology, vol. 17, no. 12, e1009661, Public Library of Science, 2021, doi:10.1371/journal.pcbi.1009661. short: K. Bodova, E. Szep, N.H. Barton, PLoS Computational Biology 17 (2021). date_created: 2021-12-12T23:01:27Z date_published: 2021-12-01T00:00:00Z date_updated: 2022-08-01T10:48:04Z day: '01' ddc: - '570' department: - _id: NiBa - _id: GaTk doi: 10.1371/journal.pcbi.1009661 external_id: arxiv: - '2102.03669' pmid: - '34851948' file: - access_level: open_access checksum: dcd185d4f7e0acee25edf1d6537f447e content_type: application/pdf creator: dernst date_created: 2022-05-16T08:53:11Z date_updated: 2022-05-16T08:53:11Z file_id: '11383' file_name: 2021_PLOsComBio_Bodova.pdf file_size: 2299486 relation: main_file success: 1 file_date_updated: 2022-05-16T08:53:11Z has_accepted_license: '1' intvolume: ' 17' issue: '12' language: - iso: eng month: '12' oa: 1 oa_version: Published Version pmid: 1 publication: PLoS Computational Biology publication_identifier: eissn: - 1553-7358 issn: - 1553-734X publication_status: published publisher: Public Library of Science quality_controlled: '1' scopus_import: '1' status: public title: Dynamic maximum entropy provides accurate approximation of structured population dynamics tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 17 year: '2021' ... --- _id: '10912' abstract: - lang: eng text: Brain dynamics display collective phenomena as diverse as neuronal oscillations and avalanches. Oscillations are rhythmic, with fluctuations occurring at a characteristic scale, whereas avalanches are scale-free cascades of neural activity. Here we show that such antithetic features can coexist in a very generic class of adaptive neural networks. In the most simple yet fully microscopic model from this class we make direct contact with human brain resting-state activity recordings via tractable inference of the model's two essential parameters. The inferred model quantitatively captures the dynamics over a broad range of scales, from single sensor fluctuations, collective behaviors of nearly-synchronous extreme events on multiple sensors, to neuronal avalanches unfolding over multiple sensors across multiple time-bins. Importantly, the inferred parameters correlate with model-independent signatures of "closeness to criticality", suggesting that the coexistence of scale-specific (neural oscillations) and scale-free (neuronal avalanches) dynamics in brain activity occurs close to a non-equilibrium critical point at the onset of self-sustained oscillations. acknowledgement: "FL acknowledges support from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 754411. GT\r\nacknowledges the support of the Austrian Science Fund (FWF) under Stand-Alone Grant\r\nNo. P34015." article_processing_charge: No author: - first_name: Fabrizio full_name: Lombardi, Fabrizio id: A057D288-3E88-11E9-986D-0CF4E5697425 last_name: Lombardi orcid: 0000-0003-2623-5249 - first_name: Selver full_name: Pepic, Selver id: F93245C4-C3CA-11E9-B4F0-C6F4E5697425 last_name: Pepic - first_name: Oren full_name: Shriki, Oren last_name: Shriki - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 - first_name: Daniele full_name: De Martino, Daniele last_name: De Martino citation: ama: Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Quantifying the coexistence of neuronal oscillations and avalanches. doi:10.48550/ARXIV.2108.06686 apa: Lombardi, F., Pepic, S., Shriki, O., Tkačik, G., & De Martino, D. (n.d.). Quantifying the coexistence of neuronal oscillations and avalanches. arXiv. https://doi.org/10.48550/ARXIV.2108.06686 chicago: Lombardi, Fabrizio, Selver Pepic, Oren Shriki, Gašper Tkačik, and Daniele De Martino. “Quantifying the Coexistence of Neuronal Oscillations and Avalanches.” arXiv, n.d. https://doi.org/10.48550/ARXIV.2108.06686. ieee: F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, and D. De Martino, “Quantifying the coexistence of neuronal oscillations and avalanches.” arXiv. ista: Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Quantifying the coexistence of neuronal oscillations and avalanches. 10.48550/ARXIV.2108.06686. mla: Lombardi, Fabrizio, et al. Quantifying the Coexistence of Neuronal Oscillations and Avalanches. arXiv, doi:10.48550/ARXIV.2108.06686. short: F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, D. De Martino, (n.d.). date_created: 2022-03-21T11:41:28Z date_published: 2021-08-17T00:00:00Z date_updated: 2022-03-22T07:53:18Z day: '17' ddc: - '570' department: - _id: GaTk doi: 10.48550/ARXIV.2108.06686 ec_funded: 1 external_id: arxiv: - '2108.06686' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2108.06686 month: '08' oa: 1 oa_version: Preprint page: '37' project: - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships - _id: 626c45b5-2b32-11ec-9570-e509828c1ba6 grant_number: P34015 name: Efficient coding with biophysical realism publication_status: submitted publisher: arXiv status: public title: Quantifying the coexistence of neuronal oscillations and avalanches type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '10579' abstract: - lang: eng text: 'We consider a totally asymmetric simple exclusion process (TASEP) consisting of particles on a lattice that require binding by a "token" to move. Using a combination of theory and simulations, we address the following questions: (i) How token binding kinetics affects the current-density relation; (ii) How the current-density relation depends on the scarcity of tokens; (iii) How tokens propagate the effects of the locally-imposed disorder (such a slow site) over the entire lattice; (iv) How a shared pool of tokens couples concurrent TASEPs running on multiple lattices; (v) How our results translate to TASEPs with open boundaries that exchange particles with the reservoir. Since real particle motion (including in systems that inspired the standard TASEP model, e.g., protein synthesis or movement of molecular motors) is often catalyzed, regulated, actuated, or otherwise mediated, the token-driven TASEP dynamics analyzed in this paper should allow for a better understanding of real systems and enable a closer match between TASEP theory and experimental observations.' acknowledgement: B.K. thanks Stefano Elefante, Simon Rella, and Michal Hledík for their help with the usage of the cluster. B.K. additionally thanks Călin Guet and his group for help and advice. We thank M. Hennessey-Wesen for constructive comments on the manuscript. We thank Ankita Gupta (Indian Institute of Technology) for spotting a typographical error in Eq. (49) in the preprint version of this paper. article_number: '2112.13558' article_processing_charge: No author: - first_name: Bor full_name: Kavcic, Bor id: 350F91D2-F248-11E8-B48F-1D18A9856A87 last_name: Kavcic orcid: 0000-0001-6041-254X - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 citation: ama: Kavcic B, Tkačik G. Token-driven totally asymmetric simple exclusion process. arXiv. doi:10.48550/arXiv.2112.13558 apa: Kavcic, B., & Tkačik, G. (n.d.). Token-driven totally asymmetric simple exclusion process. arXiv. https://doi.org/10.48550/arXiv.2112.13558 chicago: Kavcic, Bor, and Gašper Tkačik. “Token-Driven Totally Asymmetric Simple Exclusion Process.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2112.13558. ieee: B. Kavcic and G. Tkačik, “Token-driven totally asymmetric simple exclusion process,” arXiv. . ista: Kavcic B, Tkačik G. Token-driven totally asymmetric simple exclusion process. arXiv, 2112.13558. mla: Kavcic, Bor, and Gašper Tkačik. “Token-Driven Totally Asymmetric Simple Exclusion Process.” ArXiv, 2112.13558, doi:10.48550/arXiv.2112.13558. short: B. Kavcic, G. Tkačik, ArXiv (n.d.). date_created: 2021-12-28T06:52:09Z date_published: 2021-12-27T00:00:00Z date_updated: 2023-05-03T10:54:05Z day: '27' ddc: - '530' department: - _id: GaTk doi: 10.48550/arXiv.2112.13558 external_id: arxiv: - '2112.13558' has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2112.13558 month: '12' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: Token-driven totally asymmetric simple exclusion process tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '7463' abstract: - lang: eng text: Resting-state brain activity is characterized by the presence of neuronal avalanches showing absence of characteristic size. Such evidence has been interpreted in the context of criticality and associated with the normal functioning of the brain. A distinctive attribute of systems at criticality is the presence of long-range correlations. Thus, to verify the hypothesis that the brain operates close to a critical point and consequently assess deviations from criticality for diagnostic purposes, it is of primary importance to robustly and reliably characterize correlations in resting-state brain activity. Recent works focused on the analysis of narrow-band electroencephalography (EEG) and magnetoencephalography (MEG) signal amplitude envelope, showing evidence of long-range temporal correlations (LRTC) in neural oscillations. However, brain activity is a broadband phenomenon, and a significant piece of information useful to precisely discriminate between normal (critical) and pathological behavior (non-critical), may be encoded in the broadband spatio-temporal cortical dynamics. Here we propose to characterize the temporal correlations in the broadband brain activity through the lens of neuronal avalanches. To this end, we consider resting-state EEG and long-term MEG recordings, extract the corresponding neuronal avalanche sequences, and study their temporal correlations. We demonstrate that the broadband resting-state brain activity consistently exhibits long-range power-law correlations in both EEG and MEG recordings, with similar values of the scaling exponents. Importantly, although we observe that the avalanche size distribution depends on scale parameters, scaling exponents characterizing long-range correlations are quite robust. In particular, they are independent of the temporal binning (scale of analysis), indicating that our analysis captures intrinsic characteristics of the underlying dynamics. Because neuronal avalanches constitute a fundamental feature of neural systems with universal characteristics, the proposed approach may serve as a general, systems- and experiment-independent procedure to infer the existence of underlying long-range correlations in extended neural systems, and identify pathological behaviors in the complex spatio-temporal interplay of cortical rhythms. acknowledgement: LdA would like to acknowledge the financial support from MIUR-PRIN2017 WZFTZP and VALERE:VAnviteLli pEr la RicErca 2019. FL acknowledges support from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 754411. HJH would like to thank the Agencies CAPES and FUNCAP for financial support. article_processing_charge: No article_type: original author: - first_name: Fabrizio full_name: Lombardi, Fabrizio id: A057D288-3E88-11E9-986D-0CF4E5697425 last_name: Lombardi orcid: 0000-0003-2623-5249 - first_name: Oren full_name: Shriki, Oren last_name: Shriki - first_name: Hans J full_name: Herrmann, Hans J last_name: Herrmann - first_name: Lucilla full_name: de Arcangelis, Lucilla last_name: de Arcangelis citation: ama: Lombardi F, Shriki O, Herrmann HJ, de Arcangelis L. Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches. Neurocomputing. 2021;461:657-666. doi:10.1016/j.neucom.2020.05.126 apa: Lombardi, F., Shriki, O., Herrmann, H. J., & de Arcangelis, L. (2021). Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches. Neurocomputing. Elsevier. https://doi.org/10.1016/j.neucom.2020.05.126 chicago: Lombardi, Fabrizio, Oren Shriki, Hans J Herrmann, and Lucilla de Arcangelis. “Long-Range Temporal Correlations in the Broadband Resting State Activity of the Human Brain Revealed by Neuronal Avalanches.” Neurocomputing. Elsevier, 2021. https://doi.org/10.1016/j.neucom.2020.05.126. ieee: F. Lombardi, O. Shriki, H. J. Herrmann, and L. de Arcangelis, “Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches,” Neurocomputing, vol. 461. Elsevier, pp. 657–666, 2021. ista: Lombardi F, Shriki O, Herrmann HJ, de Arcangelis L. 2021. Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches. Neurocomputing. 461, 657–666. mla: Lombardi, Fabrizio, et al. “Long-Range Temporal Correlations in the Broadband Resting State Activity of the Human Brain Revealed by Neuronal Avalanches.” Neurocomputing, vol. 461, Elsevier, 2021, pp. 657–66, doi:10.1016/j.neucom.2020.05.126. short: F. Lombardi, O. Shriki, H.J. Herrmann, L. de Arcangelis, Neurocomputing 461 (2021) 657–666. date_created: 2020-02-06T16:09:14Z date_published: 2021-05-13T00:00:00Z date_updated: 2023-08-04T10:46:29Z day: '13' department: - _id: GaTk doi: 10.1016/j.neucom.2020.05.126 ec_funded: 1 external_id: isi: - '000704086300015' intvolume: ' 461' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1101/2020.02.03.930966 month: '05' oa: 1 oa_version: Preprint page: 657-666 project: - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships publication: Neurocomputing publication_identifier: eissn: - 1872-8286 issn: - 0925-2312 publication_status: published publisher: Elsevier quality_controlled: '1' scopus_import: '1' status: public title: Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 461 year: '2021' ...