--- _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 license: https://creativecommons.org/licenses/by-nc-nd/4.0/ 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' ...