--- _id: '9381' abstract: - lang: eng text: 'A game of rock-paper-scissors is an interesting example of an interaction where none of the pure strategies strictly dominates all others, leading to a cyclic pattern. In this work, we consider an unstable version of rock-paper-scissors dynamics and allow individuals to make behavioural mistakes during the strategy execution. We show that such an assumption can break a cyclic relationship leading to a stable equilibrium emerging with only one strategy surviving. We consider two cases: completely random mistakes when individuals have no bias towards any strategy and a general form of mistakes. Then, we determine conditions for a strategy to dominate all other strategies. However, given that individuals who adopt a dominating strategy are still prone to behavioural mistakes in the observed behaviour, we may still observe extinct strategies. That is, behavioural mistakes in strategy execution stabilise evolutionary dynamics leading to an evolutionary stable and, potentially, mixed co-existence equilibrium.' acknowledgement: Authors would like to thank Christian Hilbe and Martin Nowak for their inspiring and very helpful feedback on the manuscript. article_number: e1008523 article_processing_charge: No article_type: original author: - first_name: Maria full_name: Kleshnina, Maria id: 4E21749C-F248-11E8-B48F-1D18A9856A87 last_name: Kleshnina - first_name: Sabrina S. full_name: Streipert, Sabrina S. last_name: Streipert - first_name: Jerzy A. full_name: Filar, Jerzy A. last_name: Filar - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X citation: ama: Kleshnina M, Streipert SS, Filar JA, Chatterjee K. Mistakes can stabilise the dynamics of rock-paper-scissors games. PLoS Computational Biology. 2021;17(4). doi:10.1371/journal.pcbi.1008523 apa: Kleshnina, M., Streipert, S. S., Filar, J. A., & Chatterjee, K. (2021). Mistakes can stabilise the dynamics of rock-paper-scissors games. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1008523 chicago: Kleshnina, Maria, Sabrina S. Streipert, Jerzy A. Filar, and Krishnendu Chatterjee. “Mistakes Can Stabilise the Dynamics of Rock-Paper-Scissors Games.” PLoS Computational Biology. Public Library of Science, 2021. https://doi.org/10.1371/journal.pcbi.1008523. ieee: M. Kleshnina, S. S. Streipert, J. A. Filar, and K. Chatterjee, “Mistakes can stabilise the dynamics of rock-paper-scissors games,” PLoS Computational Biology, vol. 17, no. 4. Public Library of Science, 2021. ista: Kleshnina M, Streipert SS, Filar JA, Chatterjee K. 2021. Mistakes can stabilise the dynamics of rock-paper-scissors games. PLoS Computational Biology. 17(4), e1008523. mla: Kleshnina, Maria, et al. “Mistakes Can Stabilise the Dynamics of Rock-Paper-Scissors Games.” PLoS Computational Biology, vol. 17, no. 4, e1008523, Public Library of Science, 2021, doi:10.1371/journal.pcbi.1008523. short: M. Kleshnina, S.S. Streipert, J.A. Filar, K. Chatterjee, PLoS Computational Biology 17 (2021). date_created: 2021-05-09T22:01:38Z date_published: 2021-04-01T00:00:00Z date_updated: 2025-07-14T09:10:04Z day: '01' ddc: - '000' department: - _id: KrCh doi: 10.1371/journal.pcbi.1008523 ec_funded: 1 external_id: isi: - '000639711200001' file: - access_level: open_access checksum: a94ebe0c4116f5047eaa6029e54d2dac content_type: application/pdf creator: kschuh date_created: 2021-05-11T13:50:06Z date_updated: 2021-05-11T13:50:06Z file_id: '9385' file_name: 2021_pcbi_Kleshnina.pdf file_size: 1323820 relation: main_file success: 1 file_date_updated: 2021-05-11T13:50:06Z has_accepted_license: '1' intvolume: ' 17' isi: 1 issue: '4' language: - iso: eng month: '04' oa: 1 oa_version: Published Version project: - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' publication: PLoS Computational Biology publication_identifier: eissn: - '15537358' issn: - 1553734X publication_status: published publisher: Public Library of Science quality_controlled: '1' scopus_import: '1' status: public title: Mistakes can stabilise the dynamics of rock-paper-scissors games 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: '9759' acknowledgement: The authors thank Inez Lam of Johns Hopkins University for valuable comments on an earlier version of the manuscript. We also thank the facilitators of the 2019–2020 eLife Community Ambassador program. article_number: e1009124 article_processing_charge: Yes article_type: letter_note author: - first_name: Michael John full_name: Bartlett, Michael John last_name: Bartlett - first_name: Feyza N full_name: Arslan, Feyza N id: 49DA7910-F248-11E8-B48F-1D18A9856A87 last_name: Arslan orcid: 0000-0001-5809-9566 - first_name: Adriana full_name: Bankston, Adriana last_name: Bankston - first_name: Sarvenaz full_name: Sarabipour, Sarvenaz last_name: Sarabipour citation: ama: Bartlett MJ, Arslan FN, Bankston A, Sarabipour S. Ten simple rules to improve academic work- life balance. PLoS Computational Biology. 2021;17(7). doi:10.1371/journal.pcbi.1009124 apa: Bartlett, M. J., Arslan, F. N., Bankston, A., & Sarabipour, S. (2021). Ten simple rules to improve academic work- life balance. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1009124 chicago: Bartlett, Michael John, Feyza N Arslan, Adriana Bankston, and Sarvenaz Sarabipour. “Ten Simple Rules to Improve Academic Work- Life Balance.” PLoS Computational Biology. Public Library of Science, 2021. https://doi.org/10.1371/journal.pcbi.1009124. ieee: M. J. Bartlett, F. N. Arslan, A. Bankston, and S. Sarabipour, “Ten simple rules to improve academic work- life balance,” PLoS Computational Biology, vol. 17, no. 7. Public Library of Science, 2021. ista: Bartlett MJ, Arslan FN, Bankston A, Sarabipour S. 2021. Ten simple rules to improve academic work- life balance. PLoS Computational Biology. 17(7), e1009124. mla: Bartlett, Michael John, et al. “Ten Simple Rules to Improve Academic Work- Life Balance.” PLoS Computational Biology, vol. 17, no. 7, e1009124, Public Library of Science, 2021, doi:10.1371/journal.pcbi.1009124. short: M.J. Bartlett, F.N. Arslan, A. Bankston, S. Sarabipour, PLoS Computational Biology 17 (2021). date_created: 2021-08-01T22:01:21Z date_published: 2021-07-15T00:00:00Z date_updated: 2023-08-10T14:16:46Z day: '15' ddc: - '613' department: - _id: CaHe doi: 10.1371/journal.pcbi.1009124 external_id: isi: - '000677713500008' pmid: - '34264932' file: - access_level: open_access checksum: e56d91f0eeadb36f143a90e2c1b3ab63 content_type: application/pdf creator: cchlebak date_created: 2021-08-05T12:06:49Z date_updated: 2021-08-05T12:06:49Z file_id: '9771' file_name: 2021_PlosCompBio_Bartlett.pdf file_size: 693633 relation: main_file file_date_updated: 2021-08-05T12:06:49Z has_accepted_license: '1' intvolume: ' 17' isi: 1 issue: '7' language: - iso: eng month: '07' oa: 1 oa_version: Published Version pmid: 1 publication: PLoS Computational Biology publication_identifier: eissn: - '15537358' issn: - 1553734X publication_status: published publisher: Public Library of Science scopus_import: '1' status: public title: Ten simple rules to improve academic work- life balance 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: '680' abstract: - lang: eng text: In order to respond reliably to specific features of their environment, sensory neurons need to integrate multiple incoming noisy signals. Crucially, they also need to compete for the interpretation of those signals with other neurons representing similar features. The form that this competition should take depends critically on the noise corrupting these signals. In this study we show that for the type of noise commonly observed in sensory systems, whose variance scales with the mean signal, sensory neurons should selectively divide their input signals by their predictions, suppressing ambiguous cues while amplifying others. Any change in the stimulus context alters which inputs are suppressed, leading to a deep dynamic reshaping of neural receptive fields going far beyond simple surround suppression. Paradoxically, these highly variable receptive fields go alongside and are in fact required for an invariant representation of external sensory features. In addition to offering a normative account of context-dependent changes in sensory responses, perceptual inference in the presence of signal-dependent noise accounts for ubiquitous features of sensory neurons such as divisive normalization, gain control and contrast dependent temporal dynamics. article_number: e1005582 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: Paul full_name: Masset, Paul last_name: Masset - first_name: Boris full_name: Gutkin, Boris last_name: Gutkin - first_name: Sophie full_name: Denève, Sophie last_name: Denève citation: ama: Chalk MJ, Masset P, Gutkin B, Denève S. Sensory noise predicts divisive reshaping of receptive fields. PLoS Computational Biology. 2017;13(6). doi:10.1371/journal.pcbi.1005582 apa: Chalk, M. J., Masset, P., Gutkin, B., & Denève, S. (2017). Sensory noise predicts divisive reshaping of receptive fields. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005582 chicago: Chalk, Matthew J, Paul Masset, Boris Gutkin, and Sophie Denève. “Sensory Noise Predicts Divisive Reshaping of Receptive Fields.” PLoS Computational Biology. Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005582. ieee: M. J. Chalk, P. Masset, B. Gutkin, and S. Denève, “Sensory noise predicts divisive reshaping of receptive fields,” PLoS Computational Biology, vol. 13, no. 6. Public Library of Science, 2017. ista: Chalk MJ, Masset P, Gutkin B, Denève S. 2017. Sensory noise predicts divisive reshaping of receptive fields. PLoS Computational Biology. 13(6), e1005582. mla: Chalk, Matthew J., et al. “Sensory Noise Predicts Divisive Reshaping of Receptive Fields.” PLoS Computational Biology, vol. 13, no. 6, e1005582, Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005582. short: M.J. Chalk, P. Masset, B. Gutkin, S. Denève, PLoS Computational Biology 13 (2017). date_created: 2018-12-11T11:47:53Z date_published: 2017-06-01T00:00:00Z date_updated: 2023-02-23T14:10:54Z day: '01' ddc: - '571' department: - _id: GaTk doi: 10.1371/journal.pcbi.1005582 file: - access_level: open_access checksum: 796a1026076af6f4405a47d985bc7b68 content_type: application/pdf creator: system date_created: 2018-12-12T10:07:47Z date_updated: 2020-07-14T12:47:40Z file_id: '4645' file_name: IST-2017-898-v1+1_journal.pcbi.1005582.pdf file_size: 14555676 relation: main_file file_date_updated: 2020-07-14T12:47:40Z has_accepted_license: '1' intvolume: ' 13' issue: '6' language: - iso: eng month: '06' oa: 1 oa_version: Published Version publication: PLoS Computational Biology publication_identifier: issn: - 1553734X publication_status: published publisher: Public Library of Science publist_id: '7035' pubrep_id: '898' quality_controlled: '1' related_material: record: - id: '9855' relation: research_data status: public scopus_import: 1 status: public title: Sensory noise predicts divisive reshaping of receptive fields 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: 13 year: '2017' ... --- _id: '696' abstract: - lang: eng text: Mutator strains are expected to evolve when the availability and effect of beneficial mutations are high enough to counteract the disadvantage from deleterious mutations that will inevitably accumulate. As the population becomes more adapted to its environment, both availability and effect of beneficial mutations necessarily decrease and mutation rates are predicted to decrease. It has been shown that certain molecular mechanisms can lead to increased mutation rates when the organism finds itself in a stressful environment. While this may be a correlated response to other functions, it could also be an adaptive mechanism, raising mutation rates only when it is most advantageous. Here, we use a mathematical model to investigate the plausibility of the adaptive hypothesis. We show that such a mechanism can be mantained if the population is subjected to diverse stresses. By simulating various antibiotic treatment schemes, we find that combination treatments can reduce the effectiveness of second-order selection on stress-induced mutagenesis. We discuss the implications of our results to strategies of antibiotic therapy. article_number: e1005609 article_type: original author: - first_name: Marta full_name: Lukacisinova, Marta id: 4342E402-F248-11E8-B48F-1D18A9856A87 last_name: Lukacisinova orcid: 0000-0002-2519-8004 - first_name: Sebastian full_name: Novak, Sebastian id: 461468AE-F248-11E8-B48F-1D18A9856A87 last_name: Novak orcid: 0000-0002-2519-824X - first_name: Tiago full_name: Paixao, Tiago id: 2C5658E6-F248-11E8-B48F-1D18A9856A87 last_name: Paixao orcid: 0000-0003-2361-3953 citation: ama: 'Lukacisinova M, Novak S, Paixao T. Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes. PLoS Computational Biology. 2017;13(7). doi:10.1371/journal.pcbi.1005609' apa: 'Lukacisinova, M., Novak, S., & Paixao, T. (2017). Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005609' chicago: 'Lukacisinova, Marta, Sebastian Novak, and Tiago Paixao. “Stress Induced Mutagenesis: Stress Diversity Facilitates the Persistence of Mutator Genes.” PLoS Computational Biology. Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005609.' ieee: 'M. Lukacisinova, S. Novak, and T. Paixao, “Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes,” PLoS Computational Biology, vol. 13, no. 7. Public Library of Science, 2017.' ista: 'Lukacisinova M, Novak S, Paixao T. 2017. Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes. PLoS Computational Biology. 13(7), e1005609.' mla: 'Lukacisinova, Marta, et al. “Stress Induced Mutagenesis: Stress Diversity Facilitates the Persistence of Mutator Genes.” PLoS Computational Biology, vol. 13, no. 7, e1005609, Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005609.' short: M. Lukacisinova, S. Novak, T. Paixao, PLoS Computational Biology 13 (2017). date_created: 2018-12-11T11:47:58Z date_published: 2017-07-18T00:00:00Z date_updated: 2024-03-25T23:30:14Z day: '18' ddc: - '576' department: - _id: ToBo - _id: NiBa - _id: CaGu doi: 10.1371/journal.pcbi.1005609 ec_funded: 1 file: - access_level: open_access checksum: 9143c290fa6458ed2563bff4b295554a content_type: application/pdf creator: system date_created: 2018-12-12T10:15:01Z date_updated: 2020-07-14T12:47:46Z file_id: '5117' file_name: IST-2017-894-v1+1_journal.pcbi.1005609.pdf file_size: 3775716 relation: main_file file_date_updated: 2020-07-14T12:47:46Z has_accepted_license: '1' intvolume: ' 13' issue: '7' language: - iso: eng month: '07' oa: 1 oa_version: Published Version project: - _id: 25B1EC9E-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '618091' name: Speed of Adaptation in Population Genetics and Evolutionary Computation publication: PLoS Computational Biology publication_identifier: issn: - 1553734X publication_status: published publisher: Public Library of Science publist_id: '7004' pubrep_id: '894' quality_controlled: '1' related_material: record: - id: '9849' relation: research_data status: public - id: '9850' relation: research_data status: public - id: '9851' relation: research_data status: public - id: '9852' relation: research_data status: public - id: '6263' relation: dissertation_contains status: public scopus_import: 1 status: public title: 'Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes' 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: 13 year: '2017' ... --- _id: '720' abstract: - lang: eng text: 'Advances in multi-unit recordings pave the way for statistical modeling of activity patterns in large neural populations. Recent studies have shown that the summed activity of all neurons strongly shapes the population response. A separate recent finding has been that neural populations also exhibit criticality, an anomalously large dynamic range for the probabilities of different population activity patterns. Motivated by these two observations, we introduce a class of probabilistic models which takes into account the prior knowledge that the neural population could be globally coupled and close to critical. These models consist of an energy function which parametrizes interactions between small groups of neurons, and an arbitrary positive, strictly increasing, and twice differentiable function which maps the energy of a population pattern to its probability. We show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an accurate description of the activity of retinal ganglion cells which outperforms previous models based on the summed activity of neurons; 2) prior knowledge that the population is critical translates to prior expectations about the shape of the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous latent variable globally coupling the system whose distribution we can infer from data. Our method is independent of the underlying system’s state space; hence, it can be applied to other systems such as natural scenes or amino acid sequences of proteins which are also known to exhibit criticality.' article_number: e1005763 article_processing_charge: Yes author: - first_name: Jan full_name: Humplik, Jan id: 2E9627A8-F248-11E8-B48F-1D18A9856A87 last_name: Humplik - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 citation: ama: Humplik J, Tkačik G. Probabilistic models for neural populations that naturally capture global coupling and criticality. PLoS Computational Biology. 2017;13(9). doi:10.1371/journal.pcbi.1005763 apa: Humplik, J., & Tkačik, G. (2017). Probabilistic models for neural populations that naturally capture global coupling and criticality. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005763 chicago: Humplik, Jan, and Gašper Tkačik. “Probabilistic Models for Neural Populations That Naturally Capture Global Coupling and Criticality.” PLoS Computational Biology. Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005763. ieee: J. Humplik and G. Tkačik, “Probabilistic models for neural populations that naturally capture global coupling and criticality,” PLoS Computational Biology, vol. 13, no. 9. Public Library of Science, 2017. ista: Humplik J, Tkačik G. 2017. Probabilistic models for neural populations that naturally capture global coupling and criticality. PLoS Computational Biology. 13(9), e1005763. mla: Humplik, Jan, and Gašper Tkačik. “Probabilistic Models for Neural Populations That Naturally Capture Global Coupling and Criticality.” PLoS Computational Biology, vol. 13, no. 9, e1005763, Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005763. short: J. Humplik, G. Tkačik, PLoS Computational Biology 13 (2017). date_created: 2018-12-11T11:48:08Z date_published: 2017-09-19T00:00:00Z date_updated: 2021-01-12T08:12:21Z day: '19' ddc: - '530' - '571' department: - _id: GaTk doi: 10.1371/journal.pcbi.1005763 file: - access_level: open_access checksum: 81107096c19771c36ddbe6f0282a3acb content_type: application/pdf creator: system date_created: 2018-12-12T10:18:30Z date_updated: 2020-07-14T12:47:53Z file_id: '5352' file_name: IST-2017-884-v1+1_journal.pcbi.1005763.pdf file_size: 14167050 relation: main_file file_date_updated: 2020-07-14T12:47:53Z has_accepted_license: '1' intvolume: ' 13' issue: '9' language: - iso: eng month: '09' oa: 1 oa_version: Published Version project: - _id: 255008E4-B435-11E9-9278-68D0E5697425 grant_number: RGP0065/2012 name: Information processing and computation in fish groups - _id: 254D1A94-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P 25651-N26 name: Sensitivity to higher-order statistics in natural scenes publication: PLoS Computational Biology publication_identifier: issn: - 1553734X publication_status: published publisher: Public Library of Science publist_id: '6960' pubrep_id: '884' quality_controlled: '1' scopus_import: 1 status: public title: Probabilistic models for neural populations that naturally capture global coupling and 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: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 13 year: '2017' ... --- _id: '2257' abstract: - lang: eng text: 'Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such “K-pairwise” models—being systematic extensions of the previously used pairwise Ising models—provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population''s capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction.' acknowledgement: "\r\n\r\n\r\n\r\nThis work was funded by NSF grant IIS-0613435, NSF grant PHY-0957573, NSF grant CCF-0939370, NIH grant R01 EY14196, NIH grant P50 GM071508, the Fannie and John Hertz Foundation, the Swartz Foundation, the WM Keck Foundation, ANR Optima and the French State program “Investissements d'Avenir” [LIFESENSES: ANR-10-LABX-65], and the Austrian Research Foundation FWF P25651." article_number: e1003408 author: - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Olivier full_name: Marre, Olivier last_name: Marre - first_name: Dario full_name: Amodei, Dario last_name: Amodei - first_name: Elad full_name: Schneidman, Elad last_name: Schneidman - first_name: William full_name: Bialek, William last_name: Bialek - first_name: Michael full_name: Berry, Michael last_name: Berry citation: ama: Tkačik G, Marre O, Amodei D, Schneidman E, Bialek W, Berry M. Searching for collective behavior in a large network of sensory neurons. PLoS Computational Biology. 2014;10(1). doi:10.1371/journal.pcbi.1003408 apa: Tkačik, G., Marre, O., Amodei, D., Schneidman, E., Bialek, W., & Berry, M. (2014). Searching for collective behavior in a large network of sensory neurons. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1003408 chicago: Tkačik, Gašper, Olivier Marre, Dario Amodei, Elad Schneidman, William Bialek, and Michael Berry. “Searching for Collective Behavior in a Large Network of Sensory Neurons.” PLoS Computational Biology. Public Library of Science, 2014. https://doi.org/10.1371/journal.pcbi.1003408. ieee: G. Tkačik, O. Marre, D. Amodei, E. Schneidman, W. Bialek, and M. Berry, “Searching for collective behavior in a large network of sensory neurons,” PLoS Computational Biology, vol. 10, no. 1. Public Library of Science, 2014. ista: Tkačik G, Marre O, Amodei D, Schneidman E, Bialek W, Berry M. 2014. Searching for collective behavior in a large network of sensory neurons. PLoS Computational Biology. 10(1), e1003408. mla: Tkačik, Gašper, et al. “Searching for Collective Behavior in a Large Network of Sensory Neurons.” PLoS Computational Biology, vol. 10, no. 1, e1003408, Public Library of Science, 2014, doi:10.1371/journal.pcbi.1003408. short: G. Tkačik, O. Marre, D. Amodei, E. Schneidman, W. Bialek, M. Berry, PLoS Computational Biology 10 (2014). date_created: 2018-12-11T11:56:36Z date_published: 2014-01-02T00:00:00Z date_updated: 2024-02-21T13:46:14Z day: '02' ddc: - '570' department: - _id: GaTk doi: 10.1371/journal.pcbi.1003408 file: - access_level: open_access checksum: c720222c5e924a4acb17f23b9381a6ca content_type: application/pdf creator: system date_created: 2018-12-12T10:12:46Z date_updated: 2020-07-14T12:45:35Z file_id: '4965' file_name: IST-2016-436-v1+1_journal.pcbi.1003408.pdf file_size: 2194790 relation: main_file file_date_updated: 2020-07-14T12:45:35Z has_accepted_license: '1' intvolume: ' 10' issue: '1' language: - iso: eng main_file_link: - open_access: '1' url: http://repository.ist.ac.at/id/eprint/436 month: '01' oa: 1 oa_version: Published Version publication: PLoS Computational Biology publication_identifier: issn: - 1553734X publication_status: published publisher: Public Library of Science publist_id: '4689' pubrep_id: '436' quality_controlled: '1' related_material: record: - id: '5562' relation: popular_science status: public scopus_import: 1 status: public title: Searching for collective behavior in a large network of sensory neurons 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: 4435EBFC-F248-11E8-B48F-1D18A9856A87 volume: 10 year: '2014' ...