[{"publication":"Annual ACM Symposium on Parallelism in Algorithms and Architectures","citation":{"ista":"Brandt S, Keller B, Rybicki J, Suomela J, Uitto J. 2021. Efficient load-balancing through distributed token dropping. Annual ACM Symposium on Parallelism in Algorithms and Architectures. SPAA: Symposium on Parallelism in Algorithms and Architectures , 129–139.","apa":"Brandt, S., Keller, B., Rybicki, J., Suomela, J., & Uitto, J. (2021). Efficient load-balancing through distributed token dropping. In Annual ACM Symposium on Parallelism in Algorithms and Architectures (pp. 129–139). Virtual Event, United States. https://doi.org/10.1145/3409964.3461785","ieee":"S. Brandt, B. Keller, J. Rybicki, J. Suomela, and J. Uitto, “Efficient load-balancing through distributed token dropping,” in Annual ACM Symposium on Parallelism in Algorithms and Architectures, Virtual Event, United States, 2021, pp. 129–139.","ama":"Brandt S, Keller B, Rybicki J, Suomela J, Uitto J. Efficient load-balancing through distributed token dropping. In: Annual ACM Symposium on Parallelism in Algorithms and Architectures. ; 2021:129-139. doi:10.1145/3409964.3461785","chicago":"Brandt, Sebastian, Barbara Keller, Joel Rybicki, Jukka Suomela, and Jara Uitto. “Efficient Load-Balancing through Distributed Token Dropping.” In Annual ACM Symposium on Parallelism in Algorithms and Architectures, 129–39, 2021. https://doi.org/10.1145/3409964.3461785.","mla":"Brandt, Sebastian, et al. “Efficient Load-Balancing through Distributed Token Dropping.” Annual ACM Symposium on Parallelism in Algorithms and Architectures, 2021, pp. 129–39, doi:10.1145/3409964.3461785.","short":"S. Brandt, B. Keller, J. Rybicki, J. Suomela, J. Uitto, in:, Annual ACM Symposium on Parallelism in Algorithms and Architectures, 2021, pp. 129–139."},"page":"129-139","date_published":"2021-07-06T00:00:00Z","scopus_import":"1","day":"06","article_processing_charge":"No","_id":"9678","user_id":"D865714E-FA4E-11E9-B85B-F5C5E5697425","title":"Efficient load-balancing through distributed token dropping","status":"public","oa_version":"Preprint","type":"conference","abstract":[{"lang":"eng","text":"We introduce a new graph problem, the token dropping game, and we show how to solve it efficiently in a distributed setting. We use the token dropping game as a tool to design an efficient distributed algorithm for stable orientations and more generally for locally optimal semi-matchings. The prior work by Czygrinow et al. (DISC 2012) finds a stable orientation in O(Δ^5) rounds in graphs of maximum degree Δ, while we improve it to O(Δ^4) and also prove a lower bound of Ω(Δ). For the more general problem of locally optimal semi-matchings, the prior upper bound is O(S^5) and our new algorithm runs in O(C · S^4) rounds, which is an improvement for C = o(S); here C and S are the maximum degrees of customers and servers, respectively."}],"oa":1,"external_id":{"arxiv":["2005.07761"]},"main_file_link":[{"url":"https://arxiv.org/abs/2005.07761","open_access":"1"}],"quality_controlled":"1","project":[{"_id":"26A5D39A-B435-11E9-9278-68D0E5697425","grant_number":"840605","call_identifier":"H2020","name":"Coordination in constrained and natural distributed systems"}],"conference":{"location":" Virtual Event, United States","start_date":"2021-07-06","end_date":"2021-07-08","name":"SPAA: Symposium on Parallelism in Algorithms and Architectures "},"doi":"10.1145/3409964.3461785","language":[{"iso":"eng"}],"month":"07","publication_identifier":{"isbn":["9781450380706"]},"acknowledgement":"We thank Orr Fischer, Juho Hirvonen, and Tuomo Lempiäinen for valuable discussions. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 840605.","year":"2021","publication_status":"published","department":[{"_id":"DaAl"}],"author":[{"full_name":"Brandt, Sebastian","last_name":"Brandt","first_name":"Sebastian"},{"first_name":"Barbara","last_name":"Keller","full_name":"Keller, Barbara"},{"first_name":"Joel","last_name":"Rybicki","id":"334EFD2E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6432-6646","full_name":"Rybicki, Joel"},{"first_name":"Jukka","last_name":"Suomela","full_name":"Suomela, Jukka"},{"first_name":"Jara","last_name":"Uitto","full_name":"Uitto, Jara"}],"related_material":{"record":[{"relation":"earlier_version","status":"public","id":"15074"}]},"date_updated":"2024-03-05T07:13:12Z","date_created":"2021-07-18T22:01:22Z","ec_funded":1},{"publication_identifier":{"issn":["0178-4617"],"eissn":["1432-0541"]},"month":"12","doi":"10.1007/s00453-021-00905-9","conference":{"location":"Virtual, Online; Germany","start_date":"2020-07-08","end_date":"2020-07-11","name":"ICALP: International Colloquium on Automata, Languages, and Programming "},"language":[{"iso":"eng"}],"external_id":{"arxiv":["2003.09297"],"isi":["000734004600001"]},"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"oa":1,"project":[{"grant_number":"805223","_id":"268A44D6-B435-11E9-9278-68D0E5697425","name":"Elastic Coordination for Scalable Machine Learning","call_identifier":"H2020"},{"_id":"B67AFEDC-15C9-11EA-A837-991A96BB2854","name":"IST Austria Open Access Fund"}],"isi":1,"quality_controlled":"1","ec_funded":1,"file_date_updated":"2021-12-27T10:36:40Z","related_material":{"link":[{"relation":"earlier_version","url":"https://doi.org/10.4230/LIPIcs.ICALP.2020.7"}],"record":[{"status":"public","relation":"earlier_version","id":"15077"}]},"author":[{"id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-3650-940X","first_name":"Dan-Adrian","last_name":"Alistarh","full_name":"Alistarh, Dan-Adrian"},{"full_name":"Nadiradze, Giorgi","last_name":"Nadiradze","first_name":"Giorgi","orcid":"0000-0001-5634-0731","id":"3279A00C-F248-11E8-B48F-1D18A9856A87"},{"id":"bcc145fd-e77f-11ea-ae8b-80d661dbff67","last_name":"Sabour","first_name":"Amirmojtaba","full_name":"Sabour, Amirmojtaba"}],"date_updated":"2024-03-05T07:35:53Z","date_created":"2020-08-24T06:24:04Z","acknowledgement":"The authors sincerely thank Thomas Sauerwald and George Giakkoupis for insightful discussions, and Mohsen Ghaffari, Yuval Peres, and Udi Wieder for feedback on earlier versions of this draft. We also thank the ICALP anonymous reviewers for their very useful comments. Open access funding provided by Institute of Science and Technology (IST Austria). Funding was provided by European Research Council (Grant No. PR1042ERC01).","year":"2021","publisher":"Springer Nature","department":[{"_id":"DaAl"}],"publication_status":"published","has_accepted_license":"1","article_processing_charge":"Yes (via OA deal)","day":"24","scopus_import":"1","date_published":"2021-12-24T00:00:00Z","citation":{"short":"D.-A. Alistarh, G. Nadiradze, A. Sabour, Algorithmica (2021).","mla":"Alistarh, Dan-Adrian, et al. “Dynamic Averaging Load Balancing on Cycles.” Algorithmica, Springer Nature, 2021, doi:10.1007/s00453-021-00905-9.","chicago":"Alistarh, Dan-Adrian, Giorgi Nadiradze, and Amirmojtaba Sabour. “Dynamic Averaging Load Balancing on Cycles.” Algorithmica. Springer Nature, 2021. https://doi.org/10.1007/s00453-021-00905-9.","ama":"Alistarh D-A, Nadiradze G, Sabour A. Dynamic averaging load balancing on cycles. Algorithmica. 2021. doi:10.1007/s00453-021-00905-9","apa":"Alistarh, D.-A., Nadiradze, G., & Sabour, A. (2021). Dynamic averaging load balancing on cycles. Algorithmica. Virtual, Online; Germany: Springer Nature. https://doi.org/10.1007/s00453-021-00905-9","ieee":"D.-A. Alistarh, G. Nadiradze, and A. Sabour, “Dynamic averaging load balancing on cycles,” Algorithmica. Springer Nature, 2021.","ista":"Alistarh D-A, Nadiradze G, Sabour A. 2021. Dynamic averaging load balancing on cycles. Algorithmica."},"publication":"Algorithmica","article_type":"original","abstract":[{"lang":"eng","text":"We consider the following dynamic load-balancing process: given an underlying graph G with n nodes, in each step t≥ 0, one unit of load is created, and placed at a randomly chosen graph node. In the same step, the chosen node picks a random neighbor, and the two nodes balance their loads by averaging them. We are interested in the expected gap between the minimum and maximum loads at nodes as the process progresses, and its dependence on n and on the graph structure. Variants of the above graphical balanced allocation process have been studied previously by Peres, Talwar, and Wieder [Peres et al., 2015], and by Sauerwald and Sun [Sauerwald and Sun, 2015]. These authors left as open the question of characterizing the gap in the case of cycle graphs in the dynamic case, where weights are created during the algorithm’s execution. For this case, the only known upper bound is of 𝒪(n log n), following from a majorization argument due to [Peres et al., 2015], which analyzes a related graphical allocation process. In this paper, we provide an upper bound of 𝒪 (√n log n) on the expected gap of the above process for cycles of length n. We introduce a new potential analysis technique, which enables us to bound the difference in load between k-hop neighbors on the cycle, for any k ≤ n/2. We complement this with a \"gap covering\" argument, which bounds the maximum value of the gap by bounding its value across all possible subsets of a certain structure, and recursively bounding the gaps within each subset. We provide analytical and experimental evidence that our upper bound on the gap is tight up to a logarithmic factor. "}],"type":"journal_article","file":[{"file_name":"2021_Algorithmica_Alistarh.pdf","access_level":"open_access","content_type":"application/pdf","file_size":525950,"creator":"cchlebak","relation":"main_file","file_id":"10577","date_created":"2021-12-27T10:36:40Z","date_updated":"2021-12-27T10:36:40Z","checksum":"21169b25b0c8e17b21e12af22bff9870","success":1}],"oa_version":"Published Version","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","_id":"8286","ddc":["000"],"status":"public","title":"Dynamic averaging load balancing on cycles"},{"citation":{"apa":"Feliciangeli, D. (2021). The polaron at strong coupling. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:9733","ieee":"D. Feliciangeli, “The polaron at strong coupling,” Institute of Science and Technology Austria, 2021.","ista":"Feliciangeli D. 2021. The polaron at strong coupling. Institute of Science and Technology Austria.","ama":"Feliciangeli D. The polaron at strong coupling. 2021. doi:10.15479/at:ista:9733","chicago":"Feliciangeli, Dario. “The Polaron at Strong Coupling.” Institute of Science and Technology Austria, 2021. https://doi.org/10.15479/at:ista:9733.","short":"D. Feliciangeli, The Polaron at Strong Coupling, Institute of Science and Technology Austria, 2021.","mla":"Feliciangeli, Dario. The Polaron at Strong Coupling. Institute of Science and Technology Austria, 2021, doi:10.15479/at:ista:9733."},"page":"180","date_published":"2021-08-20T00:00:00Z","day":"20","article_processing_charge":"No","has_accepted_license":"1","_id":"9733","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","title":"The polaron at strong coupling","status":"public","ddc":["515","519","539"],"oa_version":"Published Version","file":[{"file_id":"9944","relation":"main_file","date_updated":"2021-09-06T09:28:56Z","date_created":"2021-08-19T14:03:48Z","checksum":"e88bb8ca43948abe060eb2d2fa719881","file_name":"Thesis_FeliciangeliA.pdf","access_level":"open_access","creator":"dfelicia","file_size":1958710,"content_type":"application/pdf"},{"access_level":"closed","file_name":"thesis.7z","content_type":"application/octet-stream","file_size":3771669,"creator":"dfelicia","relation":"source_file","file_id":"9945","checksum":"72810843abee83705853505b3f8348aa","date_created":"2021-08-19T14:06:35Z","date_updated":"2022-03-10T12:13:57Z"}],"type":"dissertation","alternative_title":["ISTA Thesis"],"abstract":[{"lang":"eng","text":"This thesis is the result of the research carried out by the author during his PhD at IST Austria between 2017 and 2021. It mainly focuses on the Fröhlich polaron model, specifically to its regime of strong coupling. This model, which is rigorously introduced and discussed in the introduction, has been of great interest in condensed matter physics and field theory for more than eighty years. It is used to describe an electron interacting with the atoms of a solid material (the strength of this interaction is modeled by the presence of a coupling constant α in the Hamiltonian of the system). The particular regime examined here, which is mathematically described by considering the limit α →∞, displays many interesting features related to the emergence of classical behavior, which allows for a simplified effective description of the system under analysis. The properties, the range of validity and a quantitative analysis of the precision of such classical approximations are the main object of the present work. We specify our investigation to the study of the ground state energy of the system, its dynamics and its effective mass. For each of these problems, we provide in the introduction an overview of the previously known results and a detailed account of the original contributions by the author."}],"tmp":{"short":"CC BY-ND (4.0)","image":"/image/cc_by_nd.png","name":"Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nd/4.0/legalcode"},"oa":1,"project":[{"_id":"256E75B8-B435-11E9-9278-68D0E5697425","grant_number":"716117","call_identifier":"H2020","name":"Optimal Transport and Stochastic Dynamics"},{"grant_number":"694227","_id":"25C6DC12-B435-11E9-9278-68D0E5697425","name":"Analysis of quantum many-body systems","call_identifier":"H2020"},{"name":"Taming Complexity in Partial Differential Systems","_id":"fc31cba2-9c52-11eb-aca3-ff467d239cd2","grant_number":"F6504"}],"doi":"10.15479/at:ista:9733","degree_awarded":"PhD","supervisor":[{"full_name":"Seiringer, Robert","id":"4AFD0470-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6781-0521","first_name":"Robert","last_name":"Seiringer"},{"last_name":"Maas","first_name":"Jan","orcid":"0000-0002-0845-1338","id":"4C5696CE-F248-11E8-B48F-1D18A9856A87","full_name":"Maas, Jan"}],"language":[{"iso":"eng"}],"month":"08","publication_identifier":{"issn":["2663-337X"]},"year":"2021","publication_status":"published","publisher":"Institute of Science and Technology Austria","department":[{"_id":"GradSch"},{"_id":"RoSe"},{"_id":"JaMa"}],"author":[{"last_name":"Feliciangeli","first_name":"Dario","orcid":"0000-0003-0754-8530","id":"41A639AA-F248-11E8-B48F-1D18A9856A87","full_name":"Feliciangeli, Dario"}],"related_material":{"record":[{"relation":"part_of_dissertation","status":"public","id":"9787"},{"id":"9792","relation":"part_of_dissertation","status":"public"},{"status":"public","relation":"part_of_dissertation","id":"9225"},{"status":"public","relation":"part_of_dissertation","id":"9781"},{"relation":"part_of_dissertation","status":"public","id":"9791"}]},"date_created":"2021-07-27T15:48:30Z","date_updated":"2024-03-06T12:30:44Z","file_date_updated":"2022-03-10T12:13:57Z","ec_funded":1},{"file_date_updated":"2021-06-23T07:09:41Z","year":"2021","publication_status":"published","publisher":"Journal of Machine Learning Research","department":[{"_id":"DaAl"}],"author":[{"full_name":"Ramezani-Kebrya, Ali","first_name":"Ali","last_name":"Ramezani-Kebrya"},{"full_name":"Faghri, Fartash","last_name":"Faghri","first_name":"Fartash"},{"full_name":"Markov, Ilya","last_name":"Markov","first_name":"Ilya"},{"id":"2980135A-F248-11E8-B48F-1D18A9856A87","first_name":"Vitalii","last_name":"Aksenov","full_name":"Aksenov, Vitalii"},{"orcid":"0000-0003-3650-940X","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","last_name":"Alistarh","first_name":"Dan-Adrian","full_name":"Alistarh, Dan-Adrian"},{"last_name":"Roy","first_name":"Daniel M.","full_name":"Roy, Daniel M."}],"date_updated":"2024-03-06T12:22:07Z","date_created":"2021-06-20T22:01:33Z","volume":22,"month":"04","publication_identifier":{"issn":["15324435"],"eissn":["15337928"]},"main_file_link":[{"url":"https://www.jmlr.org/papers/v22/20-255.html","open_access":"1"}],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"external_id":{"arxiv":["1908.06077"]},"oa":1,"quality_controlled":"1","language":[{"iso":"eng"}],"type":"journal_article","abstract":[{"lang":"eng","text":"As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed to perform parallel model training. One popular communication-compression method for data-parallel SGD is QSGD (Alistarh et al., 2017), which quantizes and encodes gradients to reduce communication costs. The baseline variant of QSGD provides strong theoretical guarantees, however, for practical purposes, the authors proposed a heuristic variant which we call QSGDinf, which demonstrated impressive empirical gains for distributed training of large neural networks. In this paper, we build on this work to propose a new gradient quantization scheme, and show that it has both stronger theoretical guarantees than QSGD, and matches and exceeds the empirical performance of the QSGDinf heuristic and of other compression methods."}],"issue":"114","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"9571","title":"NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization","ddc":["000"],"status":"public","intvolume":" 22","file":[{"relation":"main_file","file_id":"9595","date_updated":"2021-06-23T07:09:41Z","date_created":"2021-06-23T07:09:41Z","checksum":"6428aa8bcb67768b6949c99b55d5281d","success":1,"file_name":"2021_JournalOfMachineLearningResearch_Ramezani-Kebrya.pdf","access_level":"open_access","content_type":"application/pdf","file_size":11237154,"creator":"asandaue"}],"oa_version":"Published Version","scopus_import":"1","day":"01","article_processing_charge":"No","has_accepted_license":"1","publication":"Journal of Machine Learning Research","citation":{"short":"A. Ramezani-Kebrya, F. Faghri, I. Markov, V. Aksenov, D.-A. Alistarh, D.M. Roy, Journal of Machine Learning Research 22 (2021) 1−43.","mla":"Ramezani-Kebrya, Ali, et al. “NUQSGD: Provably Communication-Efficient Data-Parallel SGD via Nonuniform Quantization.” Journal of Machine Learning Research, vol. 22, no. 114, Journal of Machine Learning Research, 2021, p. 1−43.","chicago":"Ramezani-Kebrya, Ali, Fartash Faghri, Ilya Markov, Vitalii Aksenov, Dan-Adrian Alistarh, and Daniel M. Roy. “NUQSGD: Provably Communication-Efficient Data-Parallel SGD via Nonuniform Quantization.” Journal of Machine Learning Research. Journal of Machine Learning Research, 2021.","ama":"Ramezani-Kebrya A, Faghri F, Markov I, Aksenov V, Alistarh D-A, Roy DM. NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization. Journal of Machine Learning Research. 2021;22(114):1−43.","apa":"Ramezani-Kebrya, A., Faghri, F., Markov, I., Aksenov, V., Alistarh, D.-A., & Roy, D. M. (2021). NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization. Journal of Machine Learning Research. Journal of Machine Learning Research.","ieee":"A. Ramezani-Kebrya, F. Faghri, I. Markov, V. Aksenov, D.-A. Alistarh, and D. M. Roy, “NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization,” Journal of Machine Learning Research, vol. 22, no. 114. Journal of Machine Learning Research, p. 1−43, 2021.","ista":"Ramezani-Kebrya A, Faghri F, Markov I, Aksenov V, Alistarh D-A, Roy DM. 2021. NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization. Journal of Machine Learning Research. 22(114), 1−43."},"article_type":"original","page":"1−43","date_published":"2021-04-01T00:00:00Z"},{"citation":{"ama":"Takeo YH, Shuster SA, Jiang L, et al. GluD2- and Cbln1-mediated competitive synaptogenesis shapes the dendritic arbors of cerebellar Purkinje cells. Neuron. 2021;109(4):P629-644.E8. doi:10.1016/j.neuron.2020.11.028","ista":"Takeo YH, Shuster SA, Jiang L, Hu M, Luginbuhl DJ, Rülicke T, Contreras X, Hippenmeyer S, Wagner MJ, Ganguli S, Luo L. 2021. GluD2- and Cbln1-mediated competitive synaptogenesis shapes the dendritic arbors of cerebellar Purkinje cells. Neuron. 109(4), P629–644.E8.","apa":"Takeo, Y. H., Shuster, S. A., Jiang, L., Hu, M., Luginbuhl, D. J., Rülicke, T., … Luo, L. (2021). GluD2- and Cbln1-mediated competitive synaptogenesis shapes the dendritic arbors of cerebellar Purkinje cells. Neuron. Elsevier. https://doi.org/10.1016/j.neuron.2020.11.028","ieee":"Y. H. Takeo et al., “GluD2- and Cbln1-mediated competitive synaptogenesis shapes the dendritic arbors of cerebellar Purkinje cells,” Neuron, vol. 109, no. 4. Elsevier, p. P629–644.E8, 2021.","mla":"Takeo, Yukari H., et al. “GluD2- and Cbln1-Mediated Competitive Synaptogenesis Shapes the Dendritic Arbors of Cerebellar Purkinje Cells.” Neuron, vol. 109, no. 4, Elsevier, 2021, p. P629–644.E8, doi:10.1016/j.neuron.2020.11.028.","short":"Y.H. Takeo, S.A. Shuster, L. Jiang, M. Hu, D.J. Luginbuhl, T. Rülicke, X. Contreras, S. Hippenmeyer, M.J. Wagner, S. Ganguli, L. Luo, Neuron 109 (2021) P629–644.E8.","chicago":"Takeo, Yukari H., S. Andrew Shuster, Linnie Jiang, Miley Hu, David J. Luginbuhl, Thomas Rülicke, Ximena Contreras, et al. “GluD2- and Cbln1-Mediated Competitive Synaptogenesis Shapes the Dendritic Arbors of Cerebellar Purkinje Cells.” Neuron. Elsevier, 2021. https://doi.org/10.1016/j.neuron.2020.11.028."},"publication":"Neuron","page":"P629-644.E8","article_type":"original","date_published":"2021-02-17T00:00:00Z","scopus_import":"1","article_processing_charge":"No","day":"17","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"8544","intvolume":" 109","status":"public","title":"GluD2- and Cbln1-mediated competitive synaptogenesis shapes the dendritic arbors of cerebellar Purkinje cells","oa_version":"Preprint","type":"journal_article","issue":"4","abstract":[{"text":"The synaptotrophic hypothesis posits that synapse formation stabilizes dendritic branches, yet this hypothesis has not been causally tested in vivo in the mammalian brain. Presynaptic ligand cerebellin-1 (Cbln1) and postsynaptic receptor GluD2 mediate synaptogenesis between granule cells and Purkinje cells in the molecular layer of the cerebellar cortex. Here we show that sparse but not global knockout of GluD2 causes under-elaboration of Purkinje cell dendrites in the deep molecular layer and overelaboration in the superficial molecular layer. Developmental, overexpression, structure-function, and genetic epistasis analyses indicate that dendrite morphogenesis defects result from competitive synaptogenesis in a Cbln1/GluD2-dependent manner. A generative model of dendritic growth based on competitive synaptogenesis largely recapitulates GluD2 sparse and global knockout phenotypes. Our results support the synaptotrophic hypothesis at initial stages of dendrite development, suggest a second mode in which cumulative synapse formation inhibits further dendrite growth, and highlight the importance of competition in dendrite morphogenesis.","lang":"eng"}],"main_file_link":[{"url":"https://doi.org/10.1101/2020.06.14.151258","open_access":"1"}],"oa":1,"project":[{"name":"Principles of Neural Stem Cell Lineage Progression in Cerebral Cortex Development","call_identifier":"H2020","grant_number":"725780","_id":"260018B0-B435-11E9-9278-68D0E5697425"}],"quality_controlled":"1","doi":"10.1016/j.neuron.2020.11.028","language":[{"iso":"eng"}],"publication_identifier":{"eissn":["1097-4199"]},"month":"02","year":"2021","acknowledgement":"We thank M. Mishina for GluD2fl frozen embryos, T.C. Südhof and J.I. Morgan for Cbln1fl mice, L. Anderson for help in generating the MADM alleles, W. Joo for a previously unpublished construct, M. Yuzaki, K. Shen, J. Ding, and members of the Luo lab, including J.M. Kebschull, H. Li, J. Li, T. Li, C.M. McLaughlin, D. Pederick, J. Ren, D.C. Wang and C. Xu for discussions and critiques of the manuscript, and M. Yuzaki for supporting Y.H.T. during the final phase of this project. Y.H.T. was supported by a JSPS fellowship; S.A.S. was supported by a Stanford Graduate Fellowship and an NSF Predoctoral Fellowship; L.J. is supported by a Stanford Graduate Fellowship and an NSF Predoctoral Fellowship; M.J.W. is supported by a Burroughs Wellcome Fund CASI Award. This work was supported by an NIH grant (R01-NS050538) to L.L.; the European Research Council (ERC) under the European Union's Horizon 2020 research and innovations programme (No. 725780 LinPro) to S.H.; and Simons and James S. McDonnell Foundations and an NSF CAREER award to S.G.; L.L. is an HHMI investigator.","publisher":"Elsevier","department":[{"_id":"SiHi"}],"publication_status":"published","author":[{"full_name":"Takeo, Yukari H.","first_name":"Yukari H.","last_name":"Takeo"},{"first_name":"S. Andrew","last_name":"Shuster","full_name":"Shuster, S. Andrew"},{"full_name":"Jiang, Linnie","first_name":"Linnie","last_name":"Jiang"},{"first_name":"Miley","last_name":"Hu","full_name":"Hu, Miley"},{"full_name":"Luginbuhl, David J.","last_name":"Luginbuhl","first_name":"David J."},{"first_name":"Thomas","last_name":"Rülicke","full_name":"Rülicke, Thomas"},{"full_name":"Contreras, Ximena","id":"475990FE-F248-11E8-B48F-1D18A9856A87","first_name":"Ximena","last_name":"Contreras"},{"full_name":"Hippenmeyer, Simon","first_name":"Simon","last_name":"Hippenmeyer","id":"37B36620-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-2279-1061"},{"full_name":"Wagner, Mark J.","last_name":"Wagner","first_name":"Mark J."},{"last_name":"Ganguli","first_name":"Surya","full_name":"Ganguli, Surya"},{"full_name":"Luo, Liqun","first_name":"Liqun","last_name":"Luo"}],"volume":109,"date_updated":"2024-03-06T12:12:48Z","date_created":"2020-09-21T11:59:47Z","ec_funded":1}]