--- _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' ... --- _id: '9226' abstract: - lang: eng text: 'Half a century after Lewis Wolpert''s seminal conceptual advance on how cellular fates distribute in space, we provide a brief historical perspective on how the concept of positional information emerged and influenced the field of developmental biology and beyond. We focus on a modern interpretation of this concept in terms of information theory, largely centered on its application to cell specification in the early Drosophila embryo. We argue that a true physical variable (position) is encoded in local concentrations of patterning molecules, that this mapping is stochastic, and that the processes by which positions and corresponding cell fates are determined based on these concentrations need to take such stochasticity into account. With this approach, we shift the focus from biological mechanisms, molecules, genes and pathways to quantitative systems-level questions: where does positional information reside, how it is transformed and accessed during development, and what fundamental limits it is subject to?' acknowledgement: This work was supported in part by the National Science Foundation, through the Center for the Physics of Biological Function (PHY-1734030), by the National Institutes of Health (R01GM097275) and by the Fonds zur Förderung der wissenschaftlichen Forschung (FWF P28844). Deposited in PMC for release after 12 months. article_number: dev176065 article_processing_charge: No article_type: original author: - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 - first_name: Thomas full_name: Gregor, Thomas last_name: Gregor citation: ama: Tkačik G, Gregor T. The many bits of positional information. Development. 2021;148(2). doi:10.1242/dev.176065 apa: Tkačik, G., & Gregor, T. (2021). The many bits of positional information. Development. The Company of Biologists. https://doi.org/10.1242/dev.176065 chicago: Tkačik, Gašper, and Thomas Gregor. “The Many Bits of Positional Information.” Development. The Company of Biologists, 2021. https://doi.org/10.1242/dev.176065. ieee: G. Tkačik and T. Gregor, “The many bits of positional information,” Development, vol. 148, no. 2. The Company of Biologists, 2021. ista: Tkačik G, Gregor T. 2021. The many bits of positional information. Development. 148(2), dev176065. mla: Tkačik, Gašper, and Thomas Gregor. “The Many Bits of Positional Information.” Development, vol. 148, no. 2, dev176065, The Company of Biologists, 2021, doi:10.1242/dev.176065. short: G. Tkačik, T. Gregor, Development 148 (2021). date_created: 2021-03-07T23:01:25Z date_published: 2021-02-01T00:00:00Z date_updated: 2023-08-07T13:57:30Z day: '01' department: - _id: GaTk doi: 10.1242/dev.176065 external_id: isi: - '000613906000007' pmid: - '33526425' intvolume: ' 148' isi: 1 issue: '2' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1242/dev.176065 month: '02' oa: 1 oa_version: Published Version pmid: 1 project: - _id: 254E9036-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P28844-B27 name: Biophysics of information processing in gene regulation publication: Development publication_identifier: eissn: - 1477-9129 publication_status: published publisher: The Company of Biologists quality_controlled: '1' scopus_import: '1' status: public title: The many bits of positional information type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 148 year: '2021' ... --- _id: '9439' abstract: - lang: eng text: The ability to adapt to changes in stimulus statistics is a hallmark of sensory systems. Here, we developed a theoretical framework that can account for the dynamics of adaptation from an information processing perspective. We use this framework to optimize and analyze adaptive sensory codes, and we show that codes optimized for stationary environments can suffer from prolonged periods of poor performance when the environment changes. To mitigate the adversarial effects of these environmental changes, sensory systems must navigate tradeoffs between the ability to accurately encode incoming stimuli and the ability to rapidly detect and adapt to changes in the distribution of these stimuli. We derive families of codes that balance these objectives, and we demonstrate their close match to experimentally observed neural dynamics during mean and variance adaptation. Our results provide a unifying perspective on adaptation across a range of sensory systems, environments, and sensory tasks. acknowledgement: We thank D. Kastner and T. Münch for generously providing figures from their work. We also thank V. Jayaraman, M. Noorman, T. Ma, and K. Krishnamurthy for useful discussions and feedback on the manuscript. W.F.M. was funded by the European Union’s Horizon 2020 Research and Innovation Programme under Marie Skłodowska-Curie Grant Agreement No. 754411. A.M.H. was supported by the Howard Hughes Medical Institute. 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: Ann M. full_name: Hermundstad, Ann M. last_name: Hermundstad citation: ama: Mlynarski WF, Hermundstad AM. Efficient and adaptive sensory codes. Nature Neuroscience. 2021;24:998-1009. doi:10.1038/s41593-021-00846-0 apa: Mlynarski, W. F., & Hermundstad, A. M. (2021). Efficient and adaptive sensory codes. Nature Neuroscience. Springer Nature. https://doi.org/10.1038/s41593-021-00846-0 chicago: Mlynarski, Wiktor F, and Ann M. Hermundstad. “Efficient and Adaptive Sensory Codes.” Nature Neuroscience. Springer Nature, 2021. https://doi.org/10.1038/s41593-021-00846-0. ieee: W. F. Mlynarski and A. M. Hermundstad, “Efficient and adaptive sensory codes,” Nature Neuroscience, vol. 24. Springer Nature, pp. 998–1009, 2021. ista: Mlynarski WF, Hermundstad AM. 2021. Efficient and adaptive sensory codes. Nature Neuroscience. 24, 998–1009. mla: Mlynarski, Wiktor F., and Ann M. Hermundstad. “Efficient and Adaptive Sensory Codes.” Nature Neuroscience, vol. 24, Springer Nature, 2021, pp. 998–1009, doi:10.1038/s41593-021-00846-0. short: W.F. Mlynarski, A.M. Hermundstad, Nature Neuroscience 24 (2021) 998–1009. date_created: 2021-05-30T22:01:24Z date_published: 2021-05-20T00:00:00Z date_updated: 2023-08-08T13:51:14Z day: '20' department: - _id: GaTk doi: 10.1038/s41593-021-00846-0 ec_funded: 1 external_id: isi: - '000652577300003' intvolume: ' 24' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: 'https://doi.org/10.1101/669200 ' month: '05' oa: 1 oa_version: Preprint page: 998-1009 project: - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships publication: Nature Neuroscience publication_identifier: eissn: - 1546-1726 issn: - 1097-6256 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Efficient and adaptive sensory codes type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 24 year: '2021' ... --- _id: '9822' abstract: - lang: eng text: Attachment of adhesive molecules on cell culture surfaces to restrict cell adhesion to defined areas and shapes has been vital for the progress of in vitro research. In currently existing patterning methods, a combination of pattern properties such as stability, precision, specificity, high-throughput outcome, and spatiotemporal control is highly desirable but challenging to achieve. Here, we introduce a versatile and high-throughput covalent photoimmobilization technique, comprising a light-dose-dependent patterning step and a subsequent functionalization of the pattern via click chemistry. This two-step process is feasible on arbitrary surfaces and allows for generation of sustainable patterns and gradients. The method is validated in different biological systems by patterning adhesive ligands on cell-repellent surfaces, thereby constraining the growth and migration of cells to the designated areas. We then implement a sequential photopatterning approach by adding a second switchable patterning step, allowing for spatiotemporal control over two distinct surface patterns. As a proof of concept, we reconstruct the dynamics of the tip/stalk cell switch during angiogenesis. Our results show that the spatiotemporal control provided by our “sequential photopatterning” system is essential for mimicking dynamic biological processes and that our innovative approach has great potential for further applications in cell science. acknowledgement: We would like to thank Charlott Leu for the production of our chromium wafers, Louise Ritter for her contribution of the IF stainings in Figure 4, Shokoufeh Teymouri for her help with the Bioinert coated slides, and finally Prof. Dr. Joachim Rädler for his valuable scientific guidance. article_processing_charge: Yes (in subscription journal) article_type: original author: - first_name: Themistoklis full_name: Zisis, Themistoklis last_name: Zisis - first_name: Jan full_name: Schwarz, Jan id: 346C1EC6-F248-11E8-B48F-1D18A9856A87 last_name: Schwarz - first_name: Miriam full_name: Balles, Miriam last_name: Balles - first_name: Maibritt full_name: Kretschmer, Maibritt last_name: Kretschmer - first_name: Maria full_name: Nemethova, Maria id: 34E27F1C-F248-11E8-B48F-1D18A9856A87 last_name: Nemethova - first_name: Remy P full_name: Chait, Remy P id: 3464AE84-F248-11E8-B48F-1D18A9856A87 last_name: Chait orcid: 0000-0003-0876-3187 - first_name: Robert full_name: Hauschild, Robert id: 4E01D6B4-F248-11E8-B48F-1D18A9856A87 last_name: Hauschild orcid: 0000-0001-9843-3522 - first_name: Janina full_name: Lange, Janina last_name: Lange - 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: Michael K full_name: Sixt, Michael K id: 41E9FBEA-F248-11E8-B48F-1D18A9856A87 last_name: Sixt orcid: 0000-0002-4561-241X - first_name: Stefan full_name: Zahler, Stefan last_name: Zahler citation: ama: Zisis T, Schwarz J, Balles M, et al. Sequential and switchable patterning for studying cellular processes under spatiotemporal control. ACS Applied Materials and Interfaces. 2021;13(30):35545–35560. doi:10.1021/acsami.1c09850 apa: Zisis, T., Schwarz, J., Balles, M., Kretschmer, M., Nemethova, M., Chait, R. P., … Zahler, S. (2021). Sequential and switchable patterning for studying cellular processes under spatiotemporal control. ACS Applied Materials and Interfaces. American Chemical Society. https://doi.org/10.1021/acsami.1c09850 chicago: Zisis, Themistoklis, Jan Schwarz, Miriam Balles, Maibritt Kretschmer, Maria Nemethova, Remy P Chait, Robert Hauschild, et al. “Sequential and Switchable Patterning for Studying Cellular Processes under Spatiotemporal Control.” ACS Applied Materials and Interfaces. American Chemical Society, 2021. https://doi.org/10.1021/acsami.1c09850. ieee: T. Zisis et al., “Sequential and switchable patterning for studying cellular processes under spatiotemporal control,” ACS Applied Materials and Interfaces, vol. 13, no. 30. American Chemical Society, pp. 35545–35560, 2021. ista: Zisis T, Schwarz J, Balles M, Kretschmer M, Nemethova M, Chait RP, Hauschild R, Lange J, Guet CC, Sixt MK, Zahler S. 2021. Sequential and switchable patterning for studying cellular processes under spatiotemporal control. ACS Applied Materials and Interfaces. 13(30), 35545–35560. mla: Zisis, Themistoklis, et al. “Sequential and Switchable Patterning for Studying Cellular Processes under Spatiotemporal Control.” ACS Applied Materials and Interfaces, vol. 13, no. 30, American Chemical Society, 2021, pp. 35545–35560, doi:10.1021/acsami.1c09850. short: T. Zisis, J. Schwarz, M. Balles, M. Kretschmer, M. Nemethova, R.P. Chait, R. Hauschild, J. Lange, C.C. Guet, M.K. Sixt, S. Zahler, ACS Applied Materials and Interfaces 13 (2021) 35545–35560. date_created: 2021-08-08T22:01:28Z date_published: 2021-08-04T00:00:00Z date_updated: 2023-08-10T14:22:48Z day: '04' ddc: - '620' - '570' department: - _id: MiSi - _id: GaTk - _id: Bio - _id: CaGu doi: 10.1021/acsami.1c09850 ec_funded: 1 external_id: isi: - '000683741400026' pmid: - '34283577' file: - access_level: open_access checksum: b043a91d9f9200e467b970b692687ed3 content_type: application/pdf creator: asandaue date_created: 2021-08-09T09:44:03Z date_updated: 2021-08-09T09:44:03Z file_id: '9833' file_name: 2021_ACSAppliedMaterialsAndInterfaces_Zisis.pdf file_size: 7123293 relation: main_file success: 1 file_date_updated: 2021-08-09T09:44:03Z has_accepted_license: '1' intvolume: ' 13' isi: 1 issue: '30' language: - iso: eng month: '08' oa: 1 oa_version: Published Version page: 35545–35560 pmid: 1 project: - _id: 25FE9508-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '724373' name: Cellular navigation along spatial gradients publication: ACS Applied Materials and Interfaces publication_identifier: eissn: - '19448252' issn: - '19448244' publication_status: published publisher: American Chemical Society quality_controlled: '1' scopus_import: '1' status: public title: Sequential and switchable patterning for studying cellular processes under spatiotemporal control 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: 13 year: '2021' ... --- _id: '9828' abstract: - lang: eng text: Amplitude demodulation is a classical operation used in signal processing. For a long time, its effective applications in practice have been limited to narrowband signals. In this work, we generalize amplitude demodulation to wideband signals. We pose demodulation as a recovery problem of an oversampled corrupted signal and introduce special iterative schemes belonging to the family of alternating projection algorithms to solve it. Sensibly chosen structural assumptions on the demodulation outputs allow us to reveal the high inferential accuracy of the method over a rich set of relevant signals. This new approach surpasses current state-of-the-art demodulation techniques apt to wideband signals in computational efficiency by up to many orders of magnitude with no sacrifice in quality. Such performance opens the door for applications of the amplitude demodulation procedure in new contexts. In particular, the new method makes online and large-scale offline data processing feasible, including the calculation of modulator-carrier pairs in higher dimensions and poor sampling conditions, independent of the signal bandwidth. We illustrate the utility and specifics of applications of the new method in practice by using natural speech and synthetic signals. acknowledgement: The author thanks his colleagues K. Huszár and G. Tkačik for valuable discussions and comments on the manuscript. article_processing_charge: No article_type: original author: - first_name: Mantas full_name: Gabrielaitis, Mantas id: 4D5B0CBC-F248-11E8-B48F-1D18A9856A87 last_name: Gabrielaitis orcid: 0000-0002-7758-2016 citation: ama: Gabrielaitis M. Fast and accurate amplitude demodulation of wideband signals. IEEE Transactions on Signal Processing. 2021;69:4039-4054. doi:10.1109/TSP.2021.3087899 apa: Gabrielaitis, M. (2021). Fast and accurate amplitude demodulation of wideband signals. IEEE Transactions on Signal Processing. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/TSP.2021.3087899 chicago: Gabrielaitis, Mantas. “Fast and Accurate Amplitude Demodulation of Wideband Signals.” IEEE Transactions on Signal Processing. Institute of Electrical and Electronics Engineers, 2021. https://doi.org/10.1109/TSP.2021.3087899. ieee: M. Gabrielaitis, “Fast and accurate amplitude demodulation of wideband signals,” IEEE Transactions on Signal Processing, vol. 69. Institute of Electrical and Electronics Engineers, pp. 4039–4054, 2021. ista: Gabrielaitis M. 2021. Fast and accurate amplitude demodulation of wideband signals. IEEE Transactions on Signal Processing. 69, 4039–4054. mla: Gabrielaitis, Mantas. “Fast and Accurate Amplitude Demodulation of Wideband Signals.” IEEE Transactions on Signal Processing, vol. 69, Institute of Electrical and Electronics Engineers, 2021, pp. 4039–54, doi:10.1109/TSP.2021.3087899. short: M. Gabrielaitis, IEEE Transactions on Signal Processing 69 (2021) 4039–4054. date_created: 2021-08-08T22:01:31Z date_published: 2021-06-09T00:00:00Z date_updated: 2023-08-10T14:19:33Z day: '09' department: - _id: GaTk doi: 10.1109/TSP.2021.3087899 external_id: arxiv: - '2102.04832' isi: - '000682123900002' intvolume: ' 69' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2102.04832 month: '06' oa: 1 oa_version: Preprint page: 4039 - 4054 publication: IEEE Transactions on Signal Processing publication_identifier: eissn: - 1941-0476 issn: - 1053-587X publication_status: published publisher: Institute of Electrical and Electronics Engineers quality_controlled: '1' scopus_import: '1' status: public title: Fast and accurate amplitude demodulation of wideband signals type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 69 year: '2021' ... --- _id: '9362' abstract: - lang: eng text: A central goal in systems neuroscience is to understand the functions performed by neural circuits. Previous top-down models addressed this question by comparing the behaviour of an ideal model circuit, optimised to perform a given function, with neural recordings. However, this requires guessing in advance what function is being performed, which may not be possible for many neural systems. To address this, we propose an inverse reinforcement learning (RL) framework for inferring the function performed by a neural network from data. We assume that the responses of each neuron in a network are optimised so as to drive the network towards ‘rewarded’ states, that are desirable for performing a given function. We then show how one can use inverse RL to infer the reward function optimised by the network from observing its responses. This inferred reward function can be used to predict how the neural network should adapt its dynamics to perform the same function when the external environment or network structure changes. This could lead to theoretical predictions about how neural network dynamics adapt to deal with cell death and/or varying sensory stimulus statistics. acknowledgement: The authors would like to thank Ulisse Ferrari for useful discussions and feedback. article_number: e0248940 article_processing_charge: No article_type: original author: - first_name: Matthew J full_name: Chalk, Matthew J id: 2BAAC544-F248-11E8-B48F-1D18A9856A87 last_name: Chalk orcid: 0000-0001-7782-4436 - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 - first_name: Olivier full_name: Marre, Olivier last_name: Marre citation: ama: Chalk MJ, Tkačik G, Marre O. Inferring the function performed by a recurrent neural network. PLoS ONE. 2021;16(4). doi:10.1371/journal.pone.0248940 apa: Chalk, M. J., Tkačik, G., & Marre, O. (2021). Inferring the function performed by a recurrent neural network. PLoS ONE. Public Library of Science. https://doi.org/10.1371/journal.pone.0248940 chicago: Chalk, Matthew J, Gašper Tkačik, and Olivier Marre. “Inferring the Function Performed by a Recurrent Neural Network.” PLoS ONE. Public Library of Science, 2021. https://doi.org/10.1371/journal.pone.0248940. ieee: M. J. Chalk, G. Tkačik, and O. Marre, “Inferring the function performed by a recurrent neural network,” PLoS ONE, vol. 16, no. 4. Public Library of Science, 2021. ista: Chalk MJ, Tkačik G, Marre O. 2021. Inferring the function performed by a recurrent neural network. PLoS ONE. 16(4), e0248940. mla: Chalk, Matthew J., et al. “Inferring the Function Performed by a Recurrent Neural Network.” PLoS ONE, vol. 16, no. 4, e0248940, Public Library of Science, 2021, doi:10.1371/journal.pone.0248940. short: M.J. Chalk, G. Tkačik, O. Marre, PLoS ONE 16 (2021). date_created: 2021-05-02T22:01:28Z date_published: 2021-04-15T00:00:00Z date_updated: 2023-10-18T08:17:42Z day: '15' ddc: - '570' department: - _id: GaTk doi: 10.1371/journal.pone.0248940 external_id: isi: - '000641474900072' pmid: - '33857170' file: - access_level: open_access checksum: c52da133850307d2031f552d998f00e8 content_type: application/pdf creator: kschuh date_created: 2021-05-04T13:22:19Z date_updated: 2021-05-04T13:22:19Z file_id: '9371' file_name: 2021_pone_Chalk.pdf file_size: 2768282 relation: main_file success: 1 file_date_updated: 2021-05-04T13:22:19Z has_accepted_license: '1' intvolume: ' 16' isi: 1 issue: '4' language: - iso: eng month: '04' oa: 1 oa_version: Published Version pmid: 1 publication: PLoS ONE publication_identifier: eissn: - '19326203' publication_status: published publisher: Public Library of Science quality_controlled: '1' scopus_import: '1' status: public title: Inferring the function performed by a recurrent neural network 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: 16 year: '2021' ... --- _id: '8997' abstract: - lang: eng text: Phenomenological relations such as Ohm’s or Fourier’s law have a venerable history in physics but are still scarce in biology. This situation restrains predictive theory. Here, we build on bacterial “growth laws,” which capture physiological feedback between translation and cell growth, to construct a minimal biophysical model for the combined action of ribosome-targeting antibiotics. Our model predicts drug interactions like antagonism or synergy solely from responses to individual drugs. We provide analytical results for limiting cases, which agree well with numerical results. We systematically refine the model by including direct physical interactions of different antibiotics on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions that were derived using entropy maximization. We further refine the model to include the effects of antibiotics that mimic starvation and the presence of resistance genes. We describe the impact of a starvation-mimicking antibiotic on drug interactions analytically and verify it experimentally. Our extended model suggests a change in the type of drug interaction that depends on the strength of resistance, which challenges established rescaling paradigms. We experimentally show that the presence of unregulated resistance genes can lead to altered drug interaction, which agrees with the prediction of the model. While minimal, the model is readily adaptable and opens the door to predicting interactions of second and higher-order in a broad range of biological systems. acknowledgement: 'This work was supported in part by Tum stipend of Knafelj foundation (to B.K.), Austrian Science Fund (FWF) standalone grants P 27201-B22 (to T.B.) and P 28844(to G.T.), HFSP program Grant RGP0042/2013 (to T.B.), German Research Foundation (DFG) individual grant BO 3502/2-1 (to T.B.), and German Research Foundation (DFG) Collaborative Research Centre (SFB) 1310 (to T.B.). ' article_number: e1008529 article_processing_charge: Yes article_type: original 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 - first_name: Tobias full_name: Bollenbach, Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Kavcic B, Tkačik G, Bollenbach MT. Minimal biophysical model of combined antibiotic action. PLOS Computational Biology. 2021;17. doi:10.1371/journal.pcbi.1008529 apa: Kavcic, B., Tkačik, G., & Bollenbach, M. T. (2021). Minimal biophysical model of combined antibiotic action. PLOS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1008529 chicago: Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “Minimal Biophysical Model of Combined Antibiotic Action.” PLOS Computational Biology. Public Library of Science, 2021. https://doi.org/10.1371/journal.pcbi.1008529. ieee: B. Kavcic, G. Tkačik, and M. T. Bollenbach, “Minimal biophysical model of combined antibiotic action,” PLOS Computational Biology, vol. 17. Public Library of Science, 2021. ista: Kavcic B, Tkačik G, Bollenbach MT. 2021. Minimal biophysical model of combined antibiotic action. PLOS Computational Biology. 17, e1008529. mla: Kavcic, Bor, et al. “Minimal Biophysical Model of Combined Antibiotic Action.” PLOS Computational Biology, vol. 17, e1008529, Public Library of Science, 2021, doi:10.1371/journal.pcbi.1008529. short: B. Kavcic, G. Tkačik, M.T. Bollenbach, PLOS Computational Biology 17 (2021). date_created: 2021-01-08T07:16:18Z date_published: 2021-01-07T00:00:00Z date_updated: 2024-02-21T12:41:41Z day: '07' ddc: - '570' department: - _id: GaTk doi: 10.1371/journal.pcbi.1008529 external_id: isi: - '000608045000010' file: - access_level: open_access checksum: e29f2b42651bef8e034781de8781ffac content_type: application/pdf creator: dernst date_created: 2021-02-04T12:30:48Z date_updated: 2021-02-04T12:30:48Z file_id: '9092' file_name: 2021_PlosComBio_Kavcic.pdf file_size: 3690053 relation: main_file success: 1 file_date_updated: 2021-02-04T12:30:48Z has_accepted_license: '1' intvolume: ' 17' isi: 1 keyword: - Modelling and Simulation - Genetics - Molecular Biology - Antibiotics - Drug interactions language: - iso: eng month: '01' oa: 1 oa_version: Published Version project: - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions - _id: 254E9036-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P28844-B27 name: Biophysics of information processing in gene regulation publication: PLOS Computational Biology publication_identifier: issn: - 1553-7358 publication_status: published publisher: Public Library of Science quality_controlled: '1' related_material: record: - id: '7673' relation: earlier_version status: public - id: '8930' relation: research_data status: public status: public title: Minimal biophysical model of combined antibiotic action 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: 17 year: '2021' ... --- _id: '9283' abstract: - lang: eng text: Gene expression levels are influenced by multiple coexisting molecular mechanisms. Some of these interactions such as those of transcription factors and promoters have been studied extensively. However, predicting phenotypes of gene regulatory networks (GRNs) remains a major challenge. Here, we use a well-defined synthetic GRN to study in Escherichia coli how network phenotypes depend on local genetic context, i.e. the genetic neighborhood of a transcription factor and its relative position. We show that one GRN with fixed topology can display not only quantitatively but also qualitatively different phenotypes, depending solely on the local genetic context of its components. Transcriptional read-through is the main molecular mechanism that places one transcriptional unit (TU) within two separate regulons without the need for complex regulatory sequences. We propose that relative order of individual TUs, with its potential for combinatorial complexity, plays an important role in shaping phenotypes of GRNs. acknowledgement: "We thank J Bollback, L Hurst, M Lagator, C Nizak, O Rivoire, M Savageau, G Tkacik, and B Vicozo\r\nfor helpful discussions; A Dolinar and A Greshnova for technical assistance; T Bollenbach for supplying the strain JW0336; C Rusnac, and members of the Guet lab for comments. The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA grant agreement n˚\r\n628377 (ANS) and an Austrian Science Fund (FWF) grant n˚ I 3901-B32 (CCG)." article_number: e65993 article_processing_charge: Yes article_type: original author: - first_name: Anna A full_name: Nagy-Staron, Anna A id: 3ABC5BA6-F248-11E8-B48F-1D18A9856A87 last_name: Nagy-Staron orcid: 0000-0002-1391-8377 - first_name: Kathrin full_name: Tomasek, Kathrin id: 3AEC8556-F248-11E8-B48F-1D18A9856A87 last_name: Tomasek orcid: 0000-0003-3768-877X - first_name: Caroline full_name: Caruso Carter, Caroline last_name: Caruso Carter - first_name: Elisabeth full_name: Sonnleitner, Elisabeth last_name: Sonnleitner - first_name: Bor full_name: Kavcic, Bor id: 350F91D2-F248-11E8-B48F-1D18A9856A87 last_name: Kavcic orcid: 0000-0001-6041-254X - first_name: Tiago full_name: Paixão, Tiago last_name: Paixão - first_name: Calin C full_name: Guet, Calin C id: 47F8433E-F248-11E8-B48F-1D18A9856A87 last_name: Guet orcid: 0000-0001-6220-2052 citation: ama: Nagy-Staron AA, Tomasek K, Caruso Carter C, et al. Local genetic context shapes the function of a gene regulatory network. eLife. 2021;10. doi:10.7554/elife.65993 apa: Nagy-Staron, A. A., Tomasek, K., Caruso Carter, C., Sonnleitner, E., Kavcic, B., Paixão, T., & Guet, C. C. (2021). Local genetic context shapes the function of a gene regulatory network. ELife. eLife Sciences Publications. https://doi.org/10.7554/elife.65993 chicago: Nagy-Staron, Anna A, Kathrin Tomasek, Caroline Caruso Carter, Elisabeth Sonnleitner, Bor Kavcic, Tiago Paixão, and Calin C Guet. “Local Genetic Context Shapes the Function of a Gene Regulatory Network.” ELife. eLife Sciences Publications, 2021. https://doi.org/10.7554/elife.65993. ieee: A. A. Nagy-Staron et al., “Local genetic context shapes the function of a gene regulatory network,” eLife, vol. 10. eLife Sciences Publications, 2021. ista: Nagy-Staron AA, Tomasek K, Caruso Carter C, Sonnleitner E, Kavcic B, Paixão T, Guet CC. 2021. Local genetic context shapes the function of a gene regulatory network. eLife. 10, e65993. mla: Nagy-Staron, Anna A., et al. “Local Genetic Context Shapes the Function of a Gene Regulatory Network.” ELife, vol. 10, e65993, eLife Sciences Publications, 2021, doi:10.7554/elife.65993. short: A.A. Nagy-Staron, K. Tomasek, C. Caruso Carter, E. Sonnleitner, B. Kavcic, T. Paixão, C.C. Guet, ELife 10 (2021). date_created: 2021-03-23T10:11:46Z date_published: 2021-03-08T00:00:00Z date_updated: 2024-02-21T12:41:57Z day: '08' ddc: - '570' department: - _id: GaTk - _id: CaGu doi: 10.7554/elife.65993 ec_funded: 1 external_id: isi: - '000631050900001' file: - access_level: open_access checksum: 3c2f44058c2dd45a5a1027f09d263f8e content_type: application/pdf creator: bkavcic date_created: 2021-03-23T10:12:58Z date_updated: 2021-03-23T10:12:58Z file_id: '9284' file_name: elife-65993-v2.pdf file_size: 1390469 relation: main_file success: 1 file_date_updated: 2021-03-23T10:12:58Z has_accepted_license: '1' intvolume: ' 10' isi: 1 keyword: - Genetics and Molecular Biology language: - iso: eng month: '03' oa: 1 oa_version: Published Version project: - _id: 2517526A-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '628377' name: 'The Systems Biology of Transcriptional Read-Through in Bacteria: from Synthetic Networks to Genomic Studies' - _id: 268BFA92-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: I03901 name: 'CyberCircuits: Cybergenetic circuits to test composability of gene networks' publication: eLife publication_identifier: issn: - 2050-084X publication_status: published publisher: eLife Sciences Publications quality_controlled: '1' related_material: record: - id: '8951' relation: research_data status: public status: public title: Local genetic context shapes the function of a gene regulatory network 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: 10 year: '2021' ... --- _id: '7553' abstract: - lang: eng text: Normative theories and statistical inference provide complementary approaches for the study of biological systems. A normative theory postulates that organisms have adapted to efficiently solve essential tasks, and proceeds to mathematically work out testable consequences of such optimality; parameters that maximize the hypothesized organismal function can be derived ab initio, without reference to experimental data. In contrast, statistical inference focuses on efficient utilization of data to learn model parameters, without reference to any a priori notion of biological function, utility, or fitness. Traditionally, these two approaches were developed independently and applied separately. Here we unify them in a coherent Bayesian framework that embeds a normative theory into a family of maximum-entropy “optimization priors.” This family defines a smooth interpolation between a data-rich inference regime (characteristic of “bottom-up” statistical models), and a data-limited ab inito prediction regime (characteristic of “top-down” normative theory). We demonstrate the applicability of our framework using data from the visual cortex, and argue that the flexibility it affords is essential to address a number of fundamental challenges relating to inference and prediction in complex, high-dimensional biological problems. acknowledgement: The authors thank Dario Ringach for providing the V1 receptive fields and Olivier Marre for providing the retinal receptive fields. W.M. was funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 754411. M.H. was funded in part by Human Frontiers Science grant no. HFSP RGP0032/2018. article_processing_charge: No author: - first_name: Wiktor F full_name: Mlynarski, Wiktor F id: 358A453A-F248-11E8-B48F-1D18A9856A87 last_name: Mlynarski - first_name: Michal full_name: Hledik, Michal id: 4171253A-F248-11E8-B48F-1D18A9856A87 last_name: Hledik - first_name: Thomas R full_name: Sokolowski, Thomas R id: 3E999752-F248-11E8-B48F-1D18A9856A87 last_name: Sokolowski orcid: 0000-0002-1287-3779 - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 citation: ama: Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. Statistical analysis and optimality of neural systems. Neuron. 2021;109(7):1227-1241.e5. doi:10.1016/j.neuron.2021.01.020 apa: Mlynarski, W. F., Hledik, M., Sokolowski, T. R., & Tkačik, G. (2021). Statistical analysis and optimality of neural systems. Neuron. Cell Press. https://doi.org/10.1016/j.neuron.2021.01.020 chicago: Mlynarski, Wiktor F, Michal Hledik, Thomas R Sokolowski, and Gašper Tkačik. “Statistical Analysis and Optimality of Neural Systems.” Neuron. Cell Press, 2021. https://doi.org/10.1016/j.neuron.2021.01.020. ieee: W. F. Mlynarski, M. Hledik, T. R. Sokolowski, and G. Tkačik, “Statistical analysis and optimality of neural systems,” Neuron, vol. 109, no. 7. Cell Press, p. 1227–1241.e5, 2021. ista: Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. 2021. Statistical analysis and optimality of neural systems. Neuron. 109(7), 1227–1241.e5. mla: Mlynarski, Wiktor F., et al. “Statistical Analysis and Optimality of Neural Systems.” Neuron, vol. 109, no. 7, Cell Press, 2021, p. 1227–1241.e5, doi:10.1016/j.neuron.2021.01.020. short: W.F. Mlynarski, M. Hledik, T.R. Sokolowski, G. Tkačik, Neuron 109 (2021) 1227–1241.e5. date_created: 2020-02-28T11:00:12Z date_published: 2021-04-07T00:00:00Z date_updated: 2024-03-06T14:22:51Z day: '07' department: - _id: GaTk doi: 10.1016/j.neuron.2021.01.020 ec_funded: 1 external_id: isi: - '000637809600006' intvolume: ' 109' isi: 1 issue: '7' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1101/848374 month: '04' oa: 1 oa_version: Preprint page: 1227-1241.e5 project: - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships publication: Neuron publication_status: published publisher: Cell Press quality_controlled: '1' related_material: link: - description: News on IST Homepage relation: press_release url: https://ist.ac.at/en/news/can-evolution-be-predicted/ record: - id: '15020' relation: dissertation_contains status: public scopus_import: '1' status: public title: Statistical analysis and optimality of neural systems type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 109 year: '2021' ... --- _id: '10077' abstract: - lang: eng text: Although much is known about how single neurons in the hippocampus represent an animal’s position, how cell-cell interactions contribute to spatial coding remains poorly understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured cell-to-cell interactions whose statistics depend on familiar vs. novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the signal-to-noise ratio of their spatial inputs. Moreover, the topology of the interactions facilitates linear decodability, making the information easy to read out by downstream circuits. These findings suggest that the efficient coding hypothesis is not applicable only to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain. acknowledgement: We thank Peter Baracskay, Karola Kaefer and Hugo Malagon-Vina for the acquisition of the data. We thank Federico Stella for comments on an earlier version of the manuscript. MN was supported by European Union Horizon 2020 grant 665385, JC was supported by European Research Council consolidator grant 281511, GT was supported by the Austrian Science Fund (FWF) grant P34015, CS was supported by an IST fellow grant, National Institute of Mental Health Award 1R01MH125571-01, by the National Science Foundation under NSF Award No. 1922658 and a Google faculty award. article_processing_charge: No author: - first_name: Michele full_name: Nardin, Michele id: 30BD0376-F248-11E8-B48F-1D18A9856A87 last_name: Nardin orcid: 0000-0001-8849-6570 - first_name: Jozsef L full_name: Csicsvari, Jozsef L id: 3FA14672-F248-11E8-B48F-1D18A9856A87 last_name: Csicsvari orcid: 0000-0002-5193-4036 - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 - first_name: Cristina full_name: Savin, Cristina id: 3933349E-F248-11E8-B48F-1D18A9856A87 last_name: Savin citation: ama: Nardin M, Csicsvari JL, Tkačik G, Savin C. The structure of hippocampal CA1 interactions optimizes spatial coding across experience. bioRxiv. doi:10.1101/2021.09.28.460602 apa: Nardin, M., Csicsvari, J. L., Tkačik, G., & Savin, C. (n.d.). The structure of hippocampal CA1 interactions optimizes spatial coding across experience. bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2021.09.28.460602 chicago: Nardin, Michele, Jozsef L Csicsvari, Gašper Tkačik, and Cristina Savin. “The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across Experience.” BioRxiv. Cold Spring Harbor Laboratory, n.d. https://doi.org/10.1101/2021.09.28.460602. ieee: M. Nardin, J. L. Csicsvari, G. Tkačik, and C. Savin, “The structure of hippocampal CA1 interactions optimizes spatial coding across experience,” bioRxiv. Cold Spring Harbor Laboratory. ista: Nardin M, Csicsvari JL, Tkačik G, Savin C. The structure of hippocampal CA1 interactions optimizes spatial coding across experience. bioRxiv, 10.1101/2021.09.28.460602. mla: Nardin, Michele, et al. “The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across Experience.” BioRxiv, Cold Spring Harbor Laboratory, doi:10.1101/2021.09.28.460602. short: M. Nardin, J.L. Csicsvari, G. Tkačik, C. Savin, BioRxiv (n.d.). date_created: 2021-10-04T06:23:34Z date_published: 2021-09-29T00:00:00Z date_updated: 2024-03-27T23:30:16Z day: '29' department: - _id: GradSch - _id: JoCs - _id: GaTk doi: 10.1101/2021.09.28.460602 ec_funded: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://www.biorxiv.org/content/10.1101/2021.09.28.460602 month: '09' oa: 1 oa_version: Preprint project: - _id: 25681D80-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '291734' name: International IST Postdoc Fellowship Programme - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program - _id: 257A4776-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '281511' name: Memory-related information processing in neuronal circuits of the hippocampus and entorhinal cortex - _id: 626c45b5-2b32-11ec-9570-e509828c1ba6 grant_number: P34015 name: Efficient coding with biophysical realism publication: bioRxiv publication_status: submitted publisher: Cold Spring Harbor Laboratory related_material: record: - id: '11932' relation: dissertation_contains status: public status: public title: The structure of hippocampal CA1 interactions optimizes spatial coding across experience 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: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2021' ... --- _id: '8105' abstract: - lang: eng text: Physical and biological systems often exhibit intermittent dynamics with bursts or avalanches (active states) characterized by power-law size and duration distributions. These emergent features are typical of systems at the critical point of continuous phase transitions, and have led to the hypothesis that such systems may self-organize at criticality, i.e. without any fine tuning of parameters. Since the introduction of the Bak-Tang-Wiesenfeld (BTW) model, the paradigm of self-organized criticality (SOC) has been very fruitful for the analysis of emergent collective behaviors in a number of systems, including the brain. Although considerable effort has been devoted in identifying and modeling scaling features of burst and avalanche statistics, dynamical aspects related to the temporal organization of bursts remain often poorly understood or controversial. Of crucial importance to understand the mechanisms responsible for emergent behaviors is the relationship between active and quiet periods, and the nature of the correlations. Here we investigate the dynamics of active (θ-bursts) and quiet states (δ-bursts) in brain activity during the sleep-wake cycle. We show the duality of power-law (θ, active phase) and exponential-like (δ, quiescent phase) duration distributions, typical of SOC, jointly emerge with power-law temporal correlations and anti-correlated coupling between active and quiet states. Importantly, we demonstrate that such temporal organization shares important similarities with earthquake dynamics, and propose that specific power-law correlations and coupling between active and quiet states are distinctive characteristics of a class of systems with self-organization at criticality. article_number: '00005' 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: Jilin W.J.L. full_name: Wang, Jilin W.J.L. last_name: Wang - first_name: Xiyun full_name: Zhang, Xiyun last_name: Zhang - first_name: Plamen Ch full_name: Ivanov, Plamen Ch last_name: Ivanov citation: ama: Lombardi F, Wang JWJL, Zhang X, Ivanov PC. Power-law correlations and coupling of active and quiet states underlie a class of complex systems with self-organization at criticality. EPJ Web of Conferences. 2020;230. doi:10.1051/epjconf/202023000005 apa: Lombardi, F., Wang, J. W. J. L., Zhang, X., & Ivanov, P. C. (2020). Power-law correlations and coupling of active and quiet states underlie a class of complex systems with self-organization at criticality. EPJ Web of Conferences. EDP Sciences. https://doi.org/10.1051/epjconf/202023000005 chicago: Lombardi, Fabrizio, Jilin W.J.L. Wang, Xiyun Zhang, and Plamen Ch Ivanov. “Power-Law Correlations and Coupling of Active and Quiet States Underlie a Class of Complex Systems with Self-Organization at Criticality.” EPJ Web of Conferences. EDP Sciences, 2020. https://doi.org/10.1051/epjconf/202023000005. ieee: F. Lombardi, J. W. J. L. Wang, X. Zhang, and P. C. Ivanov, “Power-law correlations and coupling of active and quiet states underlie a class of complex systems with self-organization at criticality,” EPJ Web of Conferences, vol. 230. EDP Sciences, 2020. ista: Lombardi F, Wang JWJL, Zhang X, Ivanov PC. 2020. Power-law correlations and coupling of active and quiet states underlie a class of complex systems with self-organization at criticality. EPJ Web of Conferences. 230, 00005. mla: Lombardi, Fabrizio, et al. “Power-Law Correlations and Coupling of Active and Quiet States Underlie a Class of Complex Systems with Self-Organization at Criticality.” EPJ Web of Conferences, vol. 230, 00005, EDP Sciences, 2020, doi:10.1051/epjconf/202023000005. short: F. Lombardi, J.W.J.L. Wang, X. Zhang, P.C. Ivanov, EPJ Web of Conferences 230 (2020). date_created: 2020-07-12T16:20:33Z date_published: 2020-03-11T00:00:00Z date_updated: 2021-01-12T08:16:55Z day: '11' ddc: - '530' department: - _id: GaTk doi: 10.1051/epjconf/202023000005 file: - access_level: open_access content_type: application/pdf creator: dernst date_created: 2020-07-22T06:17:11Z date_updated: 2020-07-22T06:17:11Z file_id: '8144' file_name: 2020_EPJWebConf_Lombardi.pdf file_size: 2197543 relation: main_file success: 1 file_date_updated: 2020-07-22T06:17:11Z has_accepted_license: '1' intvolume: ' 230' language: - iso: eng month: '03' oa: 1 oa_version: Published Version publication: EPJ Web of Conferences publication_identifier: issn: - 2100-014X publication_status: published publisher: EDP Sciences quality_controlled: '1' status: public title: Power-law correlations and coupling of active and quiet states underlie a class of complex systems with self-organization at criticality 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: 230 year: '2020' ... --- _id: '7490' abstract: - lang: eng text: In plants, clathrin mediated endocytosis (CME) represents the major route for cargo internalisation from the cell surface. It has been assumed to operate in an evolutionary conserved manner as in yeast and animals. Here we report characterisation of ultrastructure, dynamics and mechanisms of plant CME as allowed by our advancement in electron microscopy and quantitative live imaging techniques. Arabidopsis CME appears to follow the constant curvature model and the bona fide CME population generates vesicles of a predominantly hexagonal-basket type; larger and with faster kinetics than in other models. Contrary to the existing paradigm, actin is dispensable for CME events at the plasma membrane but plays a unique role in collecting endocytic vesicles, sorting of internalised cargos and directional endosome movement that itself actively promote CME events. Internalized vesicles display a strongly delayed and sequential uncoating. These unique features highlight the independent evolution of the plant CME mechanism during the autonomous rise of multicellularity in eukaryotes. acknowledged_ssus: - _id: LifeSc - _id: Bio - _id: EM-Fac article_number: e52067 article_processing_charge: No article_type: original author: - first_name: Madhumitha full_name: Narasimhan, Madhumitha id: 44BF24D0-F248-11E8-B48F-1D18A9856A87 last_name: Narasimhan orcid: 0000-0002-8600-0671 - first_name: Alexander J full_name: Johnson, Alexander J id: 46A62C3A-F248-11E8-B48F-1D18A9856A87 last_name: Johnson orcid: 0000-0002-2739-8843 - first_name: Roshan full_name: Prizak, Roshan id: 4456104E-F248-11E8-B48F-1D18A9856A87 last_name: Prizak - first_name: Walter full_name: Kaufmann, Walter id: 3F99E422-F248-11E8-B48F-1D18A9856A87 last_name: Kaufmann orcid: 0000-0001-9735-5315 - first_name: Shutang full_name: Tan, Shutang id: 2DE75584-F248-11E8-B48F-1D18A9856A87 last_name: Tan orcid: 0000-0002-0471-8285 - first_name: Barbara E full_name: Casillas Perez, Barbara E id: 351ED2AA-F248-11E8-B48F-1D18A9856A87 last_name: Casillas Perez - first_name: Jiří full_name: Friml, Jiří id: 4159519E-F248-11E8-B48F-1D18A9856A87 last_name: Friml orcid: 0000-0002-8302-7596 citation: ama: Narasimhan M, Johnson AJ, Prizak R, et al. Evolutionarily unique mechanistic framework of clathrin-mediated endocytosis in plants. eLife. 2020;9. doi:10.7554/eLife.52067 apa: Narasimhan, M., Johnson, A. J., Prizak, R., Kaufmann, W., Tan, S., Casillas Perez, B. E., & Friml, J. (2020). Evolutionarily unique mechanistic framework of clathrin-mediated endocytosis in plants. ELife. eLife Sciences Publications. https://doi.org/10.7554/eLife.52067 chicago: Narasimhan, Madhumitha, Alexander J Johnson, Roshan Prizak, Walter Kaufmann, Shutang Tan, Barbara E Casillas Perez, and Jiří Friml. “Evolutionarily Unique Mechanistic Framework of Clathrin-Mediated Endocytosis in Plants.” ELife. eLife Sciences Publications, 2020. https://doi.org/10.7554/eLife.52067. ieee: M. Narasimhan et al., “Evolutionarily unique mechanistic framework of clathrin-mediated endocytosis in plants,” eLife, vol. 9. eLife Sciences Publications, 2020. ista: Narasimhan M, Johnson AJ, Prizak R, Kaufmann W, Tan S, Casillas Perez BE, Friml J. 2020. Evolutionarily unique mechanistic framework of clathrin-mediated endocytosis in plants. eLife. 9, e52067. mla: Narasimhan, Madhumitha, et al. “Evolutionarily Unique Mechanistic Framework of Clathrin-Mediated Endocytosis in Plants.” ELife, vol. 9, e52067, eLife Sciences Publications, 2020, doi:10.7554/eLife.52067. short: M. Narasimhan, A.J. Johnson, R. Prizak, W. Kaufmann, S. Tan, B.E. Casillas Perez, J. Friml, ELife 9 (2020). date_created: 2020-02-16T23:00:50Z date_published: 2020-01-23T00:00:00Z date_updated: 2023-08-18T06:33:07Z day: '23' ddc: - '570' - '580' department: - _id: JiFr - _id: GaTk - _id: EM-Fac - _id: SyCr doi: 10.7554/eLife.52067 ec_funded: 1 external_id: isi: - '000514104100001' pmid: - '31971511' file: - access_level: open_access checksum: 2052daa4be5019534f3a42f200a09f32 content_type: application/pdf creator: dernst date_created: 2020-02-18T07:21:16Z date_updated: 2020-07-14T12:47:59Z file_id: '7494' file_name: 2020_eLife_Narasimhan.pdf file_size: 7247468 relation: main_file file_date_updated: 2020-07-14T12:47:59Z has_accepted_license: '1' intvolume: ' 9' isi: 1 language: - iso: eng month: '01' oa: 1 oa_version: Published Version pmid: 1 project: - _id: 261099A6-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '742985' name: Tracing Evolution of Auxin Transport and Polarity in Plants - _id: 26538374-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: I03630 name: Molecular mechanisms of endocytic cargo recognition in plants publication: eLife publication_identifier: eissn: - 2050-084X publication_status: published publisher: eLife Sciences Publications quality_controlled: '1' scopus_import: '1' status: public title: Evolutionarily unique mechanistic framework of clathrin-mediated endocytosis in plants 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: 9 year: '2020' ... --- _id: '9779' article_processing_charge: No author: - first_name: Rok full_name: Grah, Rok id: 483E70DE-F248-11E8-B48F-1D18A9856A87 last_name: Grah orcid: 0000-0003-2539-3560 - first_name: Tamar full_name: Friedlander, Tamar last_name: Friedlander citation: ama: Grah R, Friedlander T. Distribution of crosstalk values. 2020. doi:10.1371/journal.pcbi.1007642.s003 apa: Grah, R., & Friedlander, T. (2020). Distribution of crosstalk values. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1007642.s003 chicago: Grah, Rok, and Tamar Friedlander. “Distribution of Crosstalk Values.” Public Library of Science, 2020. https://doi.org/10.1371/journal.pcbi.1007642.s003. ieee: R. Grah and T. Friedlander, “Distribution of crosstalk values.” Public Library of Science, 2020. ista: Grah R, Friedlander T. 2020. Distribution of crosstalk values, Public Library of Science, 10.1371/journal.pcbi.1007642.s003. mla: Grah, Rok, and Tamar Friedlander. Distribution of Crosstalk Values. Public Library of Science, 2020, doi:10.1371/journal.pcbi.1007642.s003. short: R. Grah, T. Friedlander, (2020). date_created: 2021-08-06T07:24:37Z date_published: 2020-02-25T00:00:00Z date_updated: 2023-08-18T06:47:47Z day: '25' department: - _id: GaTk doi: 10.1371/journal.pcbi.1007642.s003 month: '02' oa_version: Published Version publisher: Public Library of Science related_material: record: - id: '7569' relation: research_data status: public status: public title: Distribution of crosstalk values type: research_data_reference user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf year: '2020' ... --- _id: '9776' article_processing_charge: No author: - first_name: Rok full_name: Grah, Rok id: 483E70DE-F248-11E8-B48F-1D18A9856A87 last_name: Grah orcid: 0000-0003-2539-3560 - first_name: Tamar full_name: Friedlander, Tamar last_name: Friedlander citation: ama: Grah R, Friedlander T. Supporting information. 2020. doi:10.1371/journal.pcbi.1007642.s001 apa: Grah, R., & Friedlander, T. (2020). Supporting information. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1007642.s001 chicago: Grah, Rok, and Tamar Friedlander. “Supporting Information.” Public Library of Science, 2020. https://doi.org/10.1371/journal.pcbi.1007642.s001. ieee: R. Grah and T. Friedlander, “Supporting information.” Public Library of Science, 2020. ista: Grah R, Friedlander T. 2020. Supporting information, Public Library of Science, 10.1371/journal.pcbi.1007642.s001. mla: Grah, Rok, and Tamar Friedlander. Supporting Information. Public Library of Science, 2020, doi:10.1371/journal.pcbi.1007642.s001. short: R. Grah, T. Friedlander, (2020). date_created: 2021-08-06T07:15:04Z date_published: 2020-02-25T00:00:00Z date_updated: 2023-08-18T06:47:47Z day: '25' department: - _id: GaTk doi: 10.1371/journal.pcbi.1007642.s001 month: '02' oa_version: Published Version publisher: Public Library of Science related_material: record: - id: '7569' relation: used_in_publication status: public status: public title: Supporting information type: research_data_reference user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf year: '2020' ... --- _id: '7656' abstract: - lang: eng text: 'We propose that correlations among neurons are generically strong enough to organize neural activity patterns into a discrete set of clusters, which can each be viewed as a population codeword. Our reasoning starts with the analysis of retinal ganglion cell data using maximum entropy models, showing that the population is robustly in a frustrated, marginally sub-critical, or glassy, state. This leads to an argument that neural populations in many other brain areas might share this structure. Next, we use latent variable models to show that this glassy state possesses well-defined clusters of neural activity. Clusters have three appealing properties: (i) clusters exhibit error correction, i.e., they are reproducibly elicited by the same stimulus despite variability at the level of constituent neurons; (ii) clusters encode qualitatively different visual features than their constituent neurons; and (iii) clusters can be learned by downstream neural circuits in an unsupervised fashion. We hypothesize that these properties give rise to a “learnable” neural code which the cortical hierarchy uses to extract increasingly complex features without supervision or reinforcement.' article_number: '20' article_processing_charge: No article_type: original author: - first_name: Michael J. full_name: Berry, Michael J. last_name: Berry - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 citation: ama: 'Berry MJ, Tkačik G. Clustering of neural activity: A design principle for population codes. Frontiers in Computational Neuroscience. 2020;14. doi:10.3389/fncom.2020.00020' apa: 'Berry, M. J., & Tkačik, G. (2020). Clustering of neural activity: A design principle for population codes. Frontiers in Computational Neuroscience. Frontiers. https://doi.org/10.3389/fncom.2020.00020' chicago: 'Berry, Michael J., and Gašper Tkačik. “Clustering of Neural Activity: A Design Principle for Population Codes.” Frontiers in Computational Neuroscience. Frontiers, 2020. https://doi.org/10.3389/fncom.2020.00020.' ieee: 'M. J. Berry and G. Tkačik, “Clustering of neural activity: A design principle for population codes,” Frontiers in Computational Neuroscience, vol. 14. Frontiers, 2020.' ista: 'Berry MJ, Tkačik G. 2020. Clustering of neural activity: A design principle for population codes. Frontiers in Computational Neuroscience. 14, 20.' mla: 'Berry, Michael J., and Gašper Tkačik. “Clustering of Neural Activity: A Design Principle for Population Codes.” Frontiers in Computational Neuroscience, vol. 14, 20, Frontiers, 2020, doi:10.3389/fncom.2020.00020.' short: M.J. Berry, G. Tkačik, Frontiers in Computational Neuroscience 14 (2020). date_created: 2020-04-12T22:00:40Z date_published: 2020-03-13T00:00:00Z date_updated: 2023-08-18T10:30:11Z day: '13' ddc: - '570' department: - _id: GaTk doi: 10.3389/fncom.2020.00020 external_id: isi: - '000525543200001' pmid: - '32231528' file: - access_level: open_access checksum: 2b1da23823eae9cedbb42d701945b61e content_type: application/pdf creator: dernst date_created: 2020-04-14T12:20:39Z date_updated: 2020-07-14T12:48:01Z file_id: '7659' file_name: 2020_Frontiers_Berry.pdf file_size: 4082937 relation: main_file file_date_updated: 2020-07-14T12:48:01Z has_accepted_license: '1' intvolume: ' 14' isi: 1 language: - iso: eng month: '03' oa: 1 oa_version: Published Version pmid: 1 publication: Frontiers in Computational Neuroscience publication_identifier: eissn: - '16625188' publication_status: published publisher: Frontiers quality_controlled: '1' scopus_import: '1' status: public title: 'Clustering of neural activity: A design principle for population codes' 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: 14 year: '2020' ... --- _id: '8698' abstract: - lang: eng text: The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is the ability to estimate the surprise or likelihood of incoming high-dimensional neural activity patterns. Despite progress in statistical modeling of neural responses and deep learning, current approaches either do not scale to large neural populations or cannot be implemented using biologically realistic mechanisms. Inspired by the sparse and random connectivity of real neuronal circuits, we present a model for neural codes that accurately estimates the likelihood of individual spiking patterns and has a straightforward, scalable, efficient, learnable, and realistic neural implementation. This model’s performance on simultaneously recorded spiking activity of >100 neurons in the monkey visual and prefrontal cortices is comparable with or better than that of state-of-the-art models. Importantly, the model can be learned using a small number of samples and using a local learning rule that utilizes noise intrinsic to neural circuits. Slower, structural changes in random connectivity, consistent with rewiring and pruning processes, further improve the efficiency and sparseness of the resulting neural representations. Our results merge insights from neuroanatomy, machine learning, and theoretical neuroscience to suggest random sparse connectivity as a key design principle for neuronal computation. acknowledgement: We thank Udi Karpas, Roy Harpaz, Tal Tamir, Adam Haber, and Amir Bar for discussions and suggestions; and especially Oren Forkosh and Walter Senn for invaluable discussions of the learning rule. This work was supported by European Research Council Grant 311238 (to E.S.) and Israel Science Foundation Grant 1629/12 (to E.S.); as well as research support from Martin Kushner Schnur and Mr. and Mrs. Lawrence Feis (E.S.); National Institute of Mental Health Grant R01MH109180 (to R.K.); a Pew Scholarship in Biomedical Sciences (to R.K.); Simons Collaboration on the Global Brain Grant 542997 (to R.K. and E.S.); and a CRCNS (Collaborative Research in Computational Neuroscience) grant (to R.K. and E.S.). article_processing_charge: No article_type: original author: - first_name: Ori full_name: Maoz, Ori last_name: Maoz - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 - first_name: Mohamad Saleh full_name: Esteki, Mohamad Saleh last_name: Esteki - first_name: Roozbeh full_name: Kiani, Roozbeh last_name: Kiani - first_name: Elad full_name: Schneidman, Elad last_name: Schneidman citation: ama: Maoz O, Tkačik G, Esteki MS, Kiani R, Schneidman E. Learning probabilistic neural representations with randomly connected circuits. Proceedings of the National Academy of Sciences of the United States of America. 2020;117(40):25066-25073. doi:10.1073/pnas.1912804117 apa: Maoz, O., Tkačik, G., Esteki, M. S., Kiani, R., & Schneidman, E. (2020). Learning probabilistic neural representations with randomly connected circuits. Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences. https://doi.org/10.1073/pnas.1912804117 chicago: Maoz, Ori, Gašper Tkačik, Mohamad Saleh Esteki, Roozbeh Kiani, and Elad Schneidman. “Learning Probabilistic Neural Representations with Randomly Connected Circuits.” Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences, 2020. https://doi.org/10.1073/pnas.1912804117. ieee: O. Maoz, G. Tkačik, M. S. Esteki, R. Kiani, and E. Schneidman, “Learning probabilistic neural representations with randomly connected circuits,” Proceedings of the National Academy of Sciences of the United States of America, vol. 117, no. 40. National Academy of Sciences, pp. 25066–25073, 2020. ista: Maoz O, Tkačik G, Esteki MS, Kiani R, Schneidman E. 2020. Learning probabilistic neural representations with randomly connected circuits. Proceedings of the National Academy of Sciences of the United States of America. 117(40), 25066–25073. mla: Maoz, Ori, et al. “Learning Probabilistic Neural Representations with Randomly Connected Circuits.” Proceedings of the National Academy of Sciences of the United States of America, vol. 117, no. 40, National Academy of Sciences, 2020, pp. 25066–73, doi:10.1073/pnas.1912804117. short: O. Maoz, G. Tkačik, M.S. Esteki, R. Kiani, E. Schneidman, Proceedings of the National Academy of Sciences of the United States of America 117 (2020) 25066–25073. date_created: 2020-10-25T23:01:16Z date_published: 2020-10-06T00:00:00Z date_updated: 2023-08-22T12:11:23Z day: '06' ddc: - '570' department: - _id: GaTk doi: 10.1073/pnas.1912804117 external_id: isi: - '000579045200012' pmid: - '32948691' file: - access_level: open_access checksum: c6a24fdecf3f28faf447078e7a274a88 content_type: application/pdf creator: cziletti date_created: 2020-10-27T14:57:50Z date_updated: 2020-10-27T14:57:50Z file_id: '8713' file_name: 2020_PNAS_Maoz.pdf file_size: 1755359 relation: main_file success: 1 file_date_updated: 2020-10-27T14:57:50Z has_accepted_license: '1' intvolume: ' 117' isi: 1 issue: '40' language: - iso: eng month: '10' oa: 1 oa_version: Published Version page: 25066-25073 pmid: 1 publication: Proceedings of the National Academy of Sciences of the United States of America publication_identifier: eissn: - '10916490' issn: - '00278424' publication_status: published publisher: National Academy of Sciences quality_controlled: '1' scopus_import: '1' status: public title: Learning probabilistic neural representations with randomly connected circuits 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: 117 year: '2020' ...