[{"issue":"11","abstract":[{"text":"Oriens-lacunosum moleculare (O-LM) interneurons in the CA1 region of the hippocampus play a key role in feedback inhibition and in the control of network activity. However, how these cells are efficiently activated in the network remains unclear. To address this question, I performed recordings from CA1 pyramidal neuron axons, the presynaptic fibers that provide feedback innervation of these interneurons. Two forms of axonal action potential (AP) modulation were identified. First, repetitive stimulation resulted in activity-dependent AP broadening. Broadening showed fast onset, with marked changes in AP shape following a single AP. Second, tonic depolarization in CA1 pyramidal neuron somata induced AP broadening in the axon, and depolarization-induced broadening summated with activity-dependent broadening. Outsideout patch recordings from CA1 pyramidal neuron axons revealed a high density of a-dendrotoxin (α-DTX)-sensitive, inactivating K+ channels, suggesting that K+ channel inactivation mechanistically contributes to AP broadening. To examine the functional consequences of axonal AP modulation for synaptic transmission, I performed paired recordings between synaptically connected CA1 pyramidal neurons and O-LM interneurons. CA1 pyramidal neuron-O-LM interneuron excitatory postsynaptic currents (EPSCs) showed facilitation during both repetitive stimulation and tonic depolarization of the presynaptic neuron. Both effects were mimicked and occluded by α-DTX, suggesting that they were mediated by K+ channel inactivation. Therefore, axonal AP modulation can greatly facilitate the activation of O-LM interneurons. In conclusion, modulation of AP shape in CA1 pyramidal neuron axons substantially enhances the efficacy of principal neuron-interneuron synapses, promoting the activation of O-LM interneurons in recurrent inhibitory microcircuits.","lang":"eng"}],"type":"journal_article","pubrep_id":"434","file":[{"access_level":"open_access","file_name":"IST-2016-434-v1+1_journal.pone.0113124.pdf","file_size":5179993,"content_type":"application/pdf","creator":"system","relation":"main_file","file_id":"5107","checksum":"85e4f4ea144f827272aaf376b2830564","date_created":"2018-12-12T10:14:52Z","date_updated":"2020-07-14T12:45:24Z"}],"oa_version":"Published Version","_id":"2002","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","intvolume":" 9","ddc":["570"],"status":"public","title":"Action potential modulation in CA1 pyramidal neuron axons facilitates OLM interneuron activation in recurrent inhibitory microcircuits of rat hippocampus","has_accepted_license":"1","day":"19","scopus_import":1,"date_published":"2014-11-19T00:00:00Z","citation":{"short":"S. Kim, PLoS One 9 (2014).","mla":"Kim, Sooyun. “Action Potential Modulation in CA1 Pyramidal Neuron Axons Facilitates OLM Interneuron Activation in Recurrent Inhibitory Microcircuits of Rat Hippocampus.” PLoS One, vol. 9, no. 11, 0113124, Public Library of Science, 2014, doi:10.1371/journal.pone.0113124.","chicago":"Kim, Sooyun. “Action Potential Modulation in CA1 Pyramidal Neuron Axons Facilitates OLM Interneuron Activation in Recurrent Inhibitory Microcircuits of Rat Hippocampus.” PLoS One. Public Library of Science, 2014. https://doi.org/10.1371/journal.pone.0113124.","ama":"Kim S. Action potential modulation in CA1 pyramidal neuron axons facilitates OLM interneuron activation in recurrent inhibitory microcircuits of rat hippocampus. PLoS One. 2014;9(11). doi:10.1371/journal.pone.0113124","ieee":"S. Kim, “Action potential modulation in CA1 pyramidal neuron axons facilitates OLM interneuron activation in recurrent inhibitory microcircuits of rat hippocampus,” PLoS One, vol. 9, no. 11. Public Library of Science, 2014.","apa":"Kim, S. (2014). Action potential modulation in CA1 pyramidal neuron axons facilitates OLM interneuron activation in recurrent inhibitory microcircuits of rat hippocampus. PLoS One. Public Library of Science. https://doi.org/10.1371/journal.pone.0113124","ista":"Kim S. 2014. Action potential modulation in CA1 pyramidal neuron axons facilitates OLM interneuron activation in recurrent inhibitory microcircuits of rat hippocampus. PLoS One. 9(11), 0113124."},"publication":"PLoS One","ec_funded":1,"publist_id":"5074","file_date_updated":"2020-07-14T12:45:24Z","license":"https://creativecommons.org/licenses/by-sa/4.0/","article_number":"0113124","author":[{"full_name":"Kim, Sooyun","id":"394AB1C8-F248-11E8-B48F-1D18A9856A87","first_name":"Sooyun","last_name":"Kim"}],"volume":9,"date_updated":"2021-01-12T06:54:39Z","date_created":"2018-12-11T11:55:09Z","year":"2014","department":[{"_id":"PeJo"}],"publisher":"Public Library of Science","publication_status":"published","month":"11","doi":"10.1371/journal.pone.0113124","language":[{"iso":"eng"}],"tmp":{"short":"CC BY-SA (4.0)","image":"/images/cc_by_sa.png","name":"Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode"},"oa":1,"project":[{"_id":"25C0F108-B435-11E9-9278-68D0E5697425","grant_number":"268548","name":"Nanophysiology of fast-spiking, parvalbumin-expressing GABAergic interneurons","call_identifier":"FP7"}],"quality_controlled":"1"},{"date_published":"2014-07-02T00:00:00Z","doi":"10.1016/j.neuron.2014.06.013","language":[{"iso":"eng"}],"publication":"Neuron","citation":{"ama":"O’Neill J, Csicsvari JL. Learning by example in the hippocampus. Neuron. 2014;83(1):8-10. doi:10.1016/j.neuron.2014.06.013","ieee":"J. O’Neill and J. L. Csicsvari, “Learning by example in the hippocampus,” Neuron, vol. 83, no. 1. Elsevier, pp. 8–10, 2014.","apa":"O’Neill, J., & Csicsvari, J. L. (2014). Learning by example in the hippocampus. Neuron. Elsevier. https://doi.org/10.1016/j.neuron.2014.06.013","ista":"O’Neill J, Csicsvari JL. 2014. Learning by example in the hippocampus. Neuron. 83(1), 8–10.","short":"J. O’Neill, J.L. Csicsvari, Neuron 83 (2014) 8–10.","mla":"O’Neill, Joseph, and Jozsef L. Csicsvari. “Learning by Example in the Hippocampus.” Neuron, vol. 83, no. 1, Elsevier, 2014, pp. 8–10, doi:10.1016/j.neuron.2014.06.013.","chicago":"O’Neill, Joseph, and Jozsef L Csicsvari. “Learning by Example in the Hippocampus.” Neuron. Elsevier, 2014. https://doi.org/10.1016/j.neuron.2014.06.013."},"quality_controlled":"1","page":"8 - 10","month":"07","day":"02","scopus_import":1,"author":[{"full_name":"O'Neill, Joseph","id":"426376DC-F248-11E8-B48F-1D18A9856A87","first_name":"Joseph","last_name":"O'Neill"},{"full_name":"Csicsvari, Jozsef L","id":"3FA14672-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-5193-4036","first_name":"Jozsef L","last_name":"Csicsvari"}],"date_created":"2018-12-11T11:55:09Z","date_updated":"2021-01-12T06:54:39Z","oa_version":"None","volume":83,"_id":"2003","year":"2014","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","title":"Learning by example in the hippocampus","publication_status":"published","status":"public","publisher":"Elsevier","department":[{"_id":"JoCs"}],"intvolume":" 83","abstract":[{"text":"Learning can be facilitated by previous knowledge when it is organized into relational representations forming schemas. In this issue of Neuron, McKenzie et al. (2014) demonstrate that the hippocampus rapidly forms interrelated, hierarchical memory representations to support schema-based learning.","lang":"eng"}],"issue":"1","publist_id":"5073","type":"journal_article"},{"main_file_link":[{"url":"http://arxiv.org/abs/1401.5193","open_access":"1"}],"citation":{"ama":"Yu F, Fienberg S, Slaković A, Uhler C. Scalable privacy-preserving data sharing methodology for genome-wide association studies. Journal of Biomedical Informatics. 2014;50:133-141. doi:10.1016/j.jbi.2014.01.008","apa":"Yu, F., Fienberg, S., Slaković, A., & Uhler, C. (2014). Scalable privacy-preserving data sharing methodology for genome-wide association studies. Journal of Biomedical Informatics. Elsevier. https://doi.org/10.1016/j.jbi.2014.01.008","ieee":"F. Yu, S. Fienberg, A. Slaković, and C. Uhler, “Scalable privacy-preserving data sharing methodology for genome-wide association studies,” Journal of Biomedical Informatics, vol. 50. Elsevier, pp. 133–141, 2014.","ista":"Yu F, Fienberg S, Slaković A, Uhler C. 2014. Scalable privacy-preserving data sharing methodology for genome-wide association studies. Journal of Biomedical Informatics. 50, 133–141.","short":"F. Yu, S. Fienberg, A. Slaković, C. Uhler, Journal of Biomedical Informatics 50 (2014) 133–141.","mla":"Yu, Fei, et al. “Scalable Privacy-Preserving Data Sharing Methodology for Genome-Wide Association Studies.” Journal of Biomedical Informatics, vol. 50, Elsevier, 2014, pp. 133–41, doi:10.1016/j.jbi.2014.01.008.","chicago":"Yu, Fei, Stephen Fienberg, Alexandra Slaković, and Caroline Uhler. “Scalable Privacy-Preserving Data Sharing Methodology for Genome-Wide Association Studies.” Journal of Biomedical Informatics. Elsevier, 2014. https://doi.org/10.1016/j.jbi.2014.01.008."},"oa":1,"publication":"Journal of Biomedical Informatics","page":"133 - 141","quality_controlled":"1","date_published":"2014-08-01T00:00:00Z","doi":"10.1016/j.jbi.2014.01.008","language":[{"iso":"eng"}],"scopus_import":1,"month":"08","day":"01","_id":"2011","year":"2014","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","acknowledgement":"This research was partially supported by NSF Awards EMSW21-RTG and BCS-0941518 to the Department of Statistics at Carnegie Mellon University, and by NSF Grant BCS-0941553 to the Department of Statistics at Pennsylvania State University. This work was also supported in part by the National Center for Research Resources, Grant UL1 RR033184, and is now at the National Center for Advancing Translational Sciences, Grant UL1 TR000127 to Pennsylvania State University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NSF and NIH.","publisher":"Elsevier","department":[{"_id":"CaUh"}],"intvolume":" 50","title":"Scalable privacy-preserving data sharing methodology for genome-wide association studies","status":"public","publication_status":"published","author":[{"last_name":"Yu","first_name":"Fei","full_name":"Yu, Fei"},{"full_name":"Fienberg, Stephen","last_name":"Fienberg","first_name":"Stephen"},{"full_name":"Slaković, Alexandra","last_name":"Slaković","first_name":"Alexandra"},{"last_name":"Uhler","first_name":"Caroline","orcid":"0000-0002-7008-0216","id":"49ADD78E-F248-11E8-B48F-1D18A9856A87","full_name":"Uhler, Caroline"}],"volume":50,"oa_version":"Submitted Version","date_updated":"2021-01-12T06:54:42Z","date_created":"2018-12-11T11:55:12Z","type":"journal_article","publist_id":"5065","abstract":[{"text":"The protection of privacy of individual-level information in genome-wide association study (GWAS) databases has been a major concern of researchers following the publication of “an attack” on GWAS data by Homer et al. (2008). Traditional statistical methods for confidentiality and privacy protection of statistical databases do not scale well to deal with GWAS data, especially in terms of guarantees regarding protection from linkage to external information. The more recent concept of differential privacy, introduced by the cryptographic community, is an approach that provides a rigorous definition of privacy with meaningful privacy guarantees in the presence of arbitrary external information, although the guarantees may come at a serious price in terms of data utility. Building on such notions, Uhler et al. (2013) proposed new methods to release aggregate GWAS data without compromising an individual’s privacy. We extend the methods developed in Uhler et al. (2013) for releasing differentially-private χ2χ2-statistics by allowing for arbitrary number of cases and controls, and for releasing differentially-private allelic test statistics. We also provide a new interpretation by assuming the controls’ data are known, which is a realistic assumption because some GWAS use publicly available data as controls. We assess the performance of the proposed methods through a risk-utility analysis on a real data set consisting of DNA samples collected by the Wellcome Trust Case Control Consortium and compare the methods with the differentially-private release mechanism proposed by Johnson and Shmatikov (2013).","lang":"eng"}]},{"_id":"2005","year":"2014","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","status":"public","publication_status":"published","title":"Turning heads to remember places","intvolume":" 17","publisher":"Nature Publishing Group","department":[{"_id":"JoCs"}],"author":[{"first_name":"David","last_name":"Dupret","full_name":"Dupret, David"},{"full_name":"Csicsvari, Jozsef L","first_name":"Jozsef L","last_name":"Csicsvari","id":"3FA14672-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-5193-4036"}],"date_updated":"2021-01-12T06:54:40Z","date_created":"2018-12-11T11:55:09Z","volume":17,"oa_version":"None","type":"journal_article","abstract":[{"text":"By eliciting a natural exploratory behavior in rats, head scanning, a study reveals that hippocampal place cells form new, stable firing fields in those locations where the behavior has just occurred.","lang":"eng"}],"issue":"5","publist_id":"5071","publication":"Nature Neuroscience","citation":{"short":"D. Dupret, J.L. Csicsvari, Nature Neuroscience 17 (2014) 643–644.","mla":"Dupret, David, and Jozsef L. Csicsvari. “Turning Heads to Remember Places.” Nature Neuroscience, vol. 17, no. 5, Nature Publishing Group, 2014, pp. 643–44, doi:10.1038/nn.3700.","chicago":"Dupret, David, and Jozsef L Csicsvari. “Turning Heads to Remember Places.” Nature Neuroscience. Nature Publishing Group, 2014. https://doi.org/10.1038/nn.3700.","ama":"Dupret D, Csicsvari JL. Turning heads to remember places. Nature Neuroscience. 2014;17(5):643-644. doi:10.1038/nn.3700","ieee":"D. Dupret and J. L. Csicsvari, “Turning heads to remember places,” Nature Neuroscience, vol. 17, no. 5. Nature Publishing Group, pp. 643–644, 2014.","apa":"Dupret, D., & Csicsvari, J. L. (2014). Turning heads to remember places. Nature Neuroscience. Nature Publishing Group. https://doi.org/10.1038/nn.3700","ista":"Dupret D, Csicsvari JL. 2014. Turning heads to remember places. Nature Neuroscience. 17(5), 643–644."},"quality_controlled":"1","page":"643 - 644","date_published":"2014-04-25T00:00:00Z","doi":"10.1038/nn.3700","language":[{"iso":"eng"}],"scopus_import":1,"day":"25","month":"04"},{"status":"public","title":"gIPFrm: Generalized iterative proportional fitting for relational models","publisher":"The Comprehensive R Archive Network","department":[{"_id":"CaUh"}],"year":"2014","_id":"2007","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2022-08-26T08:12:12Z","date_created":"2018-12-11T11:55:10Z","oa_version":"Published Version","author":[{"full_name":"Klimova, Anna","id":"31934120-F248-11E8-B48F-1D18A9856A87","last_name":"Klimova","first_name":"Anna"},{"last_name":"Rudas","first_name":"Tamás","full_name":"Rudas, Tamás"}],"type":"research_data_reference","abstract":[{"text":"Maximum likelihood estimation under relational models, with or without the overall effect. For more information see the reference manual","lang":"eng"}],"publist_id":"5069","citation":{"chicago":"Klimova, Anna, and Tamás Rudas. “GIPFrm: Generalized Iterative Proportional Fitting for Relational Models.” The Comprehensive R Archive Network, 2014.","short":"A. Klimova, T. Rudas, (2014).","mla":"Klimova, Anna, and Tamás Rudas. GIPFrm: Generalized Iterative Proportional Fitting for Relational Models. The Comprehensive R Archive Network, 2014.","ieee":"A. Klimova and T. Rudas, “gIPFrm: Generalized iterative proportional fitting for relational models.” The Comprehensive R Archive Network, 2014.","apa":"Klimova, A., & Rudas, T. (2014). gIPFrm: Generalized iterative proportional fitting for relational models. The Comprehensive R Archive Network.","ista":"Klimova A, Rudas T. 2014. gIPFrm: Generalized iterative proportional fitting for relational models, The Comprehensive R Archive Network.","ama":"Klimova A, Rudas T. gIPFrm: Generalized iterative proportional fitting for relational models. 2014."},"oa":1,"main_file_link":[{"url":"https://CRAN.R-project.org/package=gIPFrm ","open_access":"1"}],"date_published":"2014-03-20T00:00:00Z","month":"03","day":"20","article_processing_charge":"No"},{"author":[{"full_name":"Matsukawa, Hiroshi","last_name":"Matsukawa","first_name":"Hiroshi"},{"full_name":"Akiyoshi Nishimura, Sachiko","last_name":"Akiyoshi Nishimura","first_name":"Sachiko"},{"full_name":"Zhang, Qi","first_name":"Qi","last_name":"Zhang"},{"first_name":"Rafael","last_name":"Luján","full_name":"Luján, Rafael"},{"last_name":"Yamaguchi","first_name":"Kazuhiko","full_name":"Yamaguchi, Kazuhiko"},{"first_name":"Hiromichi","last_name":"Goto","full_name":"Goto, Hiromichi"},{"last_name":"Yaguchi","first_name":"Kunio","full_name":"Yaguchi, Kunio"},{"last_name":"Hashikawa","first_name":"Tsutomu","full_name":"Hashikawa, Tsutomu"},{"full_name":"Sano, Chie","last_name":"Sano","first_name":"Chie"},{"orcid":"0000-0001-8761-9444","id":"499F3ABC-F248-11E8-B48F-1D18A9856A87","last_name":"Shigemoto","first_name":"Ryuichi","full_name":"Shigemoto, Ryuichi"},{"full_name":"Nakashiba, Toshiaki","last_name":"Nakashiba","first_name":"Toshiaki"},{"full_name":"Itohara, Shigeyoshi","first_name":"Shigeyoshi","last_name":"Itohara"}],"volume":34,"date_created":"2018-12-11T11:55:14Z","date_updated":"2022-05-24T08:54:54Z","pmid":1,"acknowledgement":"This work was supported by “Funding Program for World-Leading Innovative R&D on Science and Technology (FIRST Program)” initiated by the Council for Science and Technology Policy.","year":"2014","publisher":"Society for Neuroscience","department":[{"_id":"RySh"}],"publication_status":"published","publist_id":"5054","file_date_updated":"2022-05-24T08:41:41Z","doi":"10.1523/JNEUROSCI.1141-14.2014","language":[{"iso":"eng"}],"external_id":{"pmid":["25411505"]},"oa":1,"quality_controlled":"1","publication_identifier":{"issn":["0270-6474"],"eissn":["1529-2401"]},"month":"11","oa_version":"Published Version","file":[{"content_type":"application/pdf","file_size":3963728,"creator":"dernst","access_level":"open_access","file_name":"2014_JournNeuroscience_Matsukawa.pdf","checksum":"6913e9bc26e9fc1c0441a739a4199229","success":1,"date_updated":"2022-05-24T08:41:41Z","date_created":"2022-05-24T08:41:41Z","relation":"main_file","file_id":"11410"}],"_id":"2018","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","intvolume":" 34","title":"Netrin-G/NGL complexes encode functional synaptic diversification","ddc":["570"],"status":"public","issue":"47","abstract":[{"lang":"eng","text":"Synaptic cell adhesion molecules are increasingly gaining attention for conferring specific properties to individual synapses. Netrin-G1 and netrin-G2 are trans-synaptic adhesion molecules that distribute on distinct axons, and their presence restricts the expression of their cognate receptors, NGL1 and NGL2, respectively, to specific subdendritic segments of target neurons. However, the neural circuits and functional roles of netrin-G isoform complexes remain unclear. Here, we use netrin-G-KO and NGL-KO mice to reveal that netrin-G1/NGL1 and netrin-G2/NGL2 interactions specify excitatory synapses in independent hippocampal pathways. In the hippocampal CA1 area, netrin-G1/NGL1 and netrin-G2/NGL2 were expressed in the temporoammonic and Schaffer collateral pathways, respectively. The lack of presynaptic netrin-Gs led to the dispersion of NGLs from postsynaptic membranes. In accord, netrin-G mutant synapses displayed opposing phenotypes in long-term and short-term plasticity through discrete biochemical pathways. The plasticity phenotypes in netrin-G-KOs were phenocopied in NGL-KOs, with a corresponding loss of netrin-Gs from presynaptic membranes. Our findings show that netrin-G/NGL interactions differentially control synaptic plasticity in distinct circuits via retrograde signaling mechanisms and explain how synaptic inputs are diversified to control neuronal activity."}],"type":"journal_article","date_published":"2014-11-19T00:00:00Z","citation":{"chicago":"Matsukawa, Hiroshi, Sachiko Akiyoshi Nishimura, Qi Zhang, Rafael Luján, Kazuhiko Yamaguchi, Hiromichi Goto, Kunio Yaguchi, et al. “Netrin-G/NGL Complexes Encode Functional Synaptic Diversification.” Journal of Neuroscience. Society for Neuroscience, 2014. https://doi.org/10.1523/JNEUROSCI.1141-14.2014.","mla":"Matsukawa, Hiroshi, et al. “Netrin-G/NGL Complexes Encode Functional Synaptic Diversification.” Journal of Neuroscience, vol. 34, no. 47, Society for Neuroscience, 2014, pp. 15779–92, doi:10.1523/JNEUROSCI.1141-14.2014.","short":"H. Matsukawa, S. Akiyoshi Nishimura, Q. Zhang, R. Luján, K. Yamaguchi, H. Goto, K. Yaguchi, T. Hashikawa, C. Sano, R. Shigemoto, T. Nakashiba, S. Itohara, Journal of Neuroscience 34 (2014) 15779–15792.","ista":"Matsukawa H, Akiyoshi Nishimura S, Zhang Q, Luján R, Yamaguchi K, Goto H, Yaguchi K, Hashikawa T, Sano C, Shigemoto R, Nakashiba T, Itohara S. 2014. Netrin-G/NGL complexes encode functional synaptic diversification. Journal of Neuroscience. 34(47), 15779–15792.","apa":"Matsukawa, H., Akiyoshi Nishimura, S., Zhang, Q., Luján, R., Yamaguchi, K., Goto, H., … Itohara, S. (2014). Netrin-G/NGL complexes encode functional synaptic diversification. Journal of Neuroscience. Society for Neuroscience. https://doi.org/10.1523/JNEUROSCI.1141-14.2014","ieee":"H. Matsukawa et al., “Netrin-G/NGL complexes encode functional synaptic diversification,” Journal of Neuroscience, vol. 34, no. 47. Society for Neuroscience, pp. 15779–15792, 2014.","ama":"Matsukawa H, Akiyoshi Nishimura S, Zhang Q, et al. Netrin-G/NGL complexes encode functional synaptic diversification. Journal of Neuroscience. 2014;34(47):15779-15792. doi:10.1523/JNEUROSCI.1141-14.2014"},"publication":"Journal of Neuroscience","page":"15779 - 15792","article_type":"original","has_accepted_license":"1","article_processing_charge":"No","day":"19","scopus_import":"1"},{"citation":{"chicago":"Erdös, László, and Dominik J Schröder. “Phase Transition in the Density of States of Quantum Spin Glasses.” Mathematical Physics, Analysis and Geometry. Springer, 2014. https://doi.org/10.1007/s11040-014-9164-3.","short":"L. Erdös, D.J. Schröder, Mathematical Physics, Analysis and Geometry 17 (2014) 441–464.","mla":"Erdös, László, and Dominik J. Schröder. “Phase Transition in the Density of States of Quantum Spin Glasses.” Mathematical Physics, Analysis and Geometry, vol. 17, no. 3–4, Springer, 2014, pp. 441–64, doi:10.1007/s11040-014-9164-3.","ieee":"L. Erdös and D. J. Schröder, “Phase transition in the density of states of quantum spin glasses,” Mathematical Physics, Analysis and Geometry, vol. 17, no. 3–4. Springer, pp. 441–464, 2014.","apa":"Erdös, L., & Schröder, D. J. (2014). Phase transition in the density of states of quantum spin glasses. Mathematical Physics, Analysis and Geometry. Springer. https://doi.org/10.1007/s11040-014-9164-3","ista":"Erdös L, Schröder DJ. 2014. Phase transition in the density of states of quantum spin glasses. Mathematical Physics, Analysis and Geometry. 17(3–4), 441–464.","ama":"Erdös L, Schröder DJ. Phase transition in the density of states of quantum spin glasses. Mathematical Physics, Analysis and Geometry. 2014;17(3-4):441-464. doi:10.1007/s11040-014-9164-3"},"publication":"Mathematical Physics, Analysis and Geometry","page":"441 - 464","date_published":"2014-12-17T00:00:00Z","scopus_import":1,"day":"17","_id":"2019","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","intvolume":" 17","title":"Phase transition in the density of states of quantum spin glasses","status":"public","oa_version":"Submitted Version","type":"journal_article","issue":"3-4","abstract":[{"lang":"eng","text":"We prove that the empirical density of states of quantum spin glasses on arbitrary graphs converges to a normal distribution as long as the maximal degree is negligible compared with the total number of edges. This extends the recent results of Keating et al. (2014) that were proved for graphs with bounded chromatic number and with symmetric coupling distribution. Furthermore, we generalise the result to arbitrary hypergraphs. We test the optimality of our condition on the maximal degree for p-uniform hypergraphs that correspond to p-spin glass Hamiltonians acting on n distinguishable spin- 1/2 particles. At the critical threshold p = n1/2 we find a sharp classical-quantum phase transition between the normal distribution and the Wigner semicircle law. The former is characteristic to classical systems with commuting variables, while the latter is a signature of noncommutative random matrix theory."}],"main_file_link":[{"url":"http://arxiv.org/abs/1407.1552","open_access":"1"}],"oa":1,"project":[{"grant_number":"338804","_id":"258DCDE6-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"Random matrices, universality and disordered quantum systems"}],"quality_controlled":"1","doi":"10.1007/s11040-014-9164-3","language":[{"iso":"eng"}],"month":"12","year":"2014","publisher":"Springer","department":[{"_id":"LaEr"}],"publication_status":"published","author":[{"full_name":"Erdös, László","id":"4DBD5372-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-5366-9603","first_name":"László","last_name":"Erdös"},{"full_name":"Schröder, Dominik J","first_name":"Dominik J","last_name":"Schröder"}],"volume":17,"date_updated":"2021-01-12T06:54:45Z","date_created":"2018-12-11T11:55:15Z","publist_id":"5053","ec_funded":1},{"oa_version":"Submitted Version","_id":"2013","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","intvolume":" 14","status":"public","title":"Hypersurfaces and their singularities in partial correlation testing","issue":"5","abstract":[{"text":"An asymptotic theory is developed for computing volumes of regions in the parameter space of a directed Gaussian graphical model that are obtained by bounding partial correlations. We study these volumes using the method of real log canonical thresholds from algebraic geometry. Our analysis involves the computation of the singular loci of correlation hypersurfaces. Statistical applications include the strong-faithfulness assumption for the PC algorithm and the quantification of confounder bias in causal inference. A detailed analysis is presented for trees, bow ties, tripartite graphs, and complete graphs.\r\n","lang":"eng"}],"type":"journal_article","date_published":"2014-10-10T00:00:00Z","citation":{"mla":"Lin, Shaowei, et al. “Hypersurfaces and Their Singularities in Partial Correlation Testing.” Foundations of Computational Mathematics, vol. 14, no. 5, Springer, 2014, pp. 1079–116, doi:10.1007/s10208-014-9205-0.","short":"S. Lin, C. Uhler, B. Sturmfels, P. Bühlmann, Foundations of Computational Mathematics 14 (2014) 1079–1116.","chicago":"Lin, Shaowei, Caroline Uhler, Bernd Sturmfels, and Peter Bühlmann. “Hypersurfaces and Their Singularities in Partial Correlation Testing.” Foundations of Computational Mathematics. Springer, 2014. https://doi.org/10.1007/s10208-014-9205-0.","ama":"Lin S, Uhler C, Sturmfels B, Bühlmann P. Hypersurfaces and their singularities in partial correlation testing. Foundations of Computational Mathematics. 2014;14(5):1079-1116. doi:10.1007/s10208-014-9205-0","ista":"Lin S, Uhler C, Sturmfels B, Bühlmann P. 2014. Hypersurfaces and their singularities in partial correlation testing. Foundations of Computational Mathematics. 14(5), 1079–1116.","apa":"Lin, S., Uhler, C., Sturmfels, B., & Bühlmann, P. (2014). Hypersurfaces and their singularities in partial correlation testing. Foundations of Computational Mathematics. Springer. https://doi.org/10.1007/s10208-014-9205-0","ieee":"S. Lin, C. Uhler, B. Sturmfels, and P. Bühlmann, “Hypersurfaces and their singularities in partial correlation testing,” Foundations of Computational Mathematics, vol. 14, no. 5. Springer, pp. 1079–1116, 2014."},"publication":"Foundations of Computational Mathematics","page":"1079 - 1116","day":"10","scopus_import":1,"author":[{"last_name":"Lin","first_name":"Shaowei","full_name":"Lin, Shaowei"},{"full_name":"Uhler, Caroline","id":"49ADD78E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-7008-0216","first_name":"Caroline","last_name":"Uhler"},{"full_name":"Sturmfels, Bernd","first_name":"Bernd","last_name":"Sturmfels"},{"full_name":"Bühlmann, Peter","first_name":"Peter","last_name":"Bühlmann"}],"volume":14,"date_updated":"2021-01-12T06:54:43Z","date_created":"2018-12-11T11:55:12Z","acknowledgement":"This work was supported in part by the US National Science Foundation (DMS-0968882) and the Defense Advanced Research Projects Agency (DARPA) Deep Learning program (FA8650-10-C-7020).","year":"2014","publisher":"Springer","department":[{"_id":"CaUh"}],"publication_status":"published","publist_id":"5063","doi":"10.1007/s10208-014-9205-0","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1209.0285"}],"oa":1,"quality_controlled":"1","month":"10"},{"type":"preprint","publist_id":"5058","abstract":[{"lang":"eng","text":" Gaussian graphical models have received considerable attention during the past four decades from the statistical and machine learning communities. In Bayesian treatments of this model, the G-Wishart distribution serves as the conjugate prior for inverse covariance matrices satisfying graphical constraints. While it is straightforward to posit the unnormalized densities, the normalizing constants of these distributions have been known only for graphs that are chordal, or decomposable. Up until now, it was unknown whether the normalizing constant for a general graph could be represented explicitly, and a considerable body of computational literature emerged that attempted to avoid this apparent intractability. We close this question by providing an explicit representation of the G-Wishart normalizing constant for general graphs."}],"extern":1,"acknowledgement":"A.L.'s research was supported by Statistics for Innovation sfi2 in Oslo.\nD.R.'s research was partially supported by the U.S. National Science Foun-dation grant DMS-1309808; and by a Romberg Guest Professorship at the Heidelberg University Graduate School for Mathematical and Computational Methods in the Sciences, funded by German Universities Excellence Initiative grant GSC 220/2.","_id":"2017","year":"2014","publisher":"ArXiv","title":" Exact formulas for the normalizing constants of Wishart distributions for graphical models","status":"public","publication_status":"published","author":[{"id":"49ADD78E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-7008-0216","first_name":"Caroline","last_name":"Uhler","full_name":"Caroline Uhler"},{"last_name":"Lenkoski","first_name":"Alex","full_name":"Lenkoski, Alex"},{"full_name":"Richards, Donald","first_name":"Donald","last_name":"Richards"}],"date_created":"2018-12-11T11:55:14Z","date_updated":"2021-01-12T06:54:44Z","month":"06","day":"18","main_file_link":[{"url":"http://arxiv.org/abs/1406.4901","open_access":"1"}],"citation":{"chicago":"Uhler, Caroline, Alex Lenkoski, and Donald Richards. “ Exact Formulas for the Normalizing Constants of Wishart Distributions for Graphical Models.” ArXiv. ArXiv, 2014.","short":"C. Uhler, A. Lenkoski, D. Richards, ArXiv (2014).","mla":"Uhler, Caroline, et al. “ Exact Formulas for the Normalizing Constants of Wishart Distributions for Graphical Models.” ArXiv, ArXiv, 2014.","ieee":"C. Uhler, A. Lenkoski, and D. Richards, “ Exact formulas for the normalizing constants of Wishart distributions for graphical models,” ArXiv. ArXiv, 2014.","apa":"Uhler, C., Lenkoski, A., & Richards, D. (2014). Exact formulas for the normalizing constants of Wishart distributions for graphical models. ArXiv. ArXiv.","ista":"Uhler C, Lenkoski A, Richards D. 2014. Exact formulas for the normalizing constants of Wishart distributions for graphical models. ArXiv, .","ama":"Uhler C, Lenkoski A, Richards D. Exact formulas for the normalizing constants of Wishart distributions for graphical models. ArXiv. 2014."},"oa":1,"publication":"ArXiv","quality_controlled":0,"date_published":"2014-06-18T00:00:00Z"},{"publist_id":"5050","ec_funded":1,"file_date_updated":"2020-07-14T12:45:25Z","author":[{"last_name":"Gao","first_name":"Peng","full_name":"Gao, Peng"},{"full_name":"Postiglione, Maria P","first_name":"Maria P","last_name":"Postiglione","id":"2C67902A-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Krieger","first_name":"Teresa","full_name":"Krieger, Teresa"},{"full_name":"Hernandez, Luisirene","first_name":"Luisirene","last_name":"Hernandez"},{"last_name":"Wang","first_name":"Chao","full_name":"Wang, Chao"},{"full_name":"Han, Zhi","first_name":"Zhi","last_name":"Han"},{"full_name":"Streicher, Carmen","first_name":"Carmen","last_name":"Streicher","id":"36BCB99C-F248-11E8-B48F-1D18A9856A87"},{"id":"41DB591E-F248-11E8-B48F-1D18A9856A87","last_name":"Papusheva","first_name":"Ekaterina","full_name":"Papusheva, Ekaterina"},{"first_name":"Ryan","last_name":"Insolera","full_name":"Insolera, Ryan"},{"full_name":"Chugh, Kritika","first_name":"Kritika","last_name":"Chugh"},{"first_name":"Oren","last_name":"Kodish","full_name":"Kodish, Oren"},{"last_name":"Huang","first_name":"Kun","full_name":"Huang, Kun"},{"full_name":"Simons, Benjamin","last_name":"Simons","first_name":"Benjamin"},{"full_name":"Luo, Liqun","first_name":"Liqun","last_name":"Luo"},{"first_name":"Simon","last_name":"Hippenmeyer","id":"37B36620-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-2279-1061","full_name":"Hippenmeyer, Simon"},{"first_name":"Song","last_name":"Shi","full_name":"Shi, Song"}],"volume":159,"date_created":"2018-12-11T11:55:16Z","date_updated":"2021-01-12T06:54:47Z","year":"2014","publisher":"Cell Press","department":[{"_id":"SiHi"},{"_id":"Bio"}],"publication_status":"published","month":"11","doi":"10.1016/j.cell.2014.10.027","language":[{"iso":"eng"}],"oa":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"},"project":[{"name":"Molecular Mechanisms of Cerebral Cortex Development","call_identifier":"FP7","_id":"25D61E48-B435-11E9-9278-68D0E5697425","grant_number":"618444"},{"name":"Quantitative Structure-Function Analysis of Cerebral Cortex Assembly at Clonal Level","_id":"25D7962E-B435-11E9-9278-68D0E5697425","grant_number":"RGP0053/2014"}],"quality_controlled":"1","issue":"4","abstract":[{"lang":"eng","text":"Radial glial progenitors (RGPs) are responsible for producing nearly all neocortical neurons. To gain insight into the patterns of RGP division and neuron production, we quantitatively analyzed excitatory neuron genesis in the mouse neocortex using Mosaic Analysis with Double Markers, which provides single-cell resolution of progenitor division patterns and potential in vivo. We found that RGPs progress through a coherent program in which their proliferative potential diminishes in a predictable manner. Upon entry into the neurogenic phase, individual RGPs produce ∼8–9 neurons distributed in both deep and superficial layers, indicating a unitary output in neuronal production. Removal of OTX1, a transcription factor transiently expressed in RGPs, results in both deep- and superficial-layer neuron loss and a reduction in neuronal unit size. Moreover, ∼1/6 of neurogenic RGPs proceed to produce glia. These results suggest that progenitor behavior and histogenesis in the mammalian neocortex conform to a remarkably orderly and deterministic program."}],"type":"journal_article","pubrep_id":"423","file":[{"access_level":"open_access","file_name":"IST-2016-423-v1+1_1-s2.0-S0092867414013154-main.pdf","creator":"system","file_size":4435787,"content_type":"application/pdf","file_id":"4709","relation":"main_file","checksum":"6c5de8329bb2ffa71cba9fda750f14ce","date_created":"2018-12-12T10:08:47Z","date_updated":"2020-07-14T12:45:25Z"}],"oa_version":"Published Version","_id":"2022","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","intvolume":" 159","status":"public","ddc":["570"],"title":"Deterministic progenitor behavior and unitary production of neurons in the neocortex","has_accepted_license":"1","day":"06","scopus_import":1,"date_published":"2014-11-06T00:00:00Z","citation":{"mla":"Gao, Peng, et al. “Deterministic Progenitor Behavior and Unitary Production of Neurons in the Neocortex.” Cell, vol. 159, no. 4, Cell Press, 2014, pp. 775–88, doi:10.1016/j.cell.2014.10.027.","short":"P. Gao, M.P. Postiglione, T. Krieger, L. Hernandez, C. Wang, Z. Han, C. Streicher, E. Papusheva, R. Insolera, K. Chugh, O. Kodish, K. Huang, B. Simons, L. Luo, S. Hippenmeyer, S. Shi, Cell 159 (2014) 775–788.","chicago":"Gao, Peng, Maria P Postiglione, Teresa Krieger, Luisirene Hernandez, Chao Wang, Zhi Han, Carmen Streicher, et al. “Deterministic Progenitor Behavior and Unitary Production of Neurons in the Neocortex.” Cell. Cell Press, 2014. https://doi.org/10.1016/j.cell.2014.10.027.","ama":"Gao P, Postiglione MP, Krieger T, et al. Deterministic progenitor behavior and unitary production of neurons in the neocortex. Cell. 2014;159(4):775-788. doi:10.1016/j.cell.2014.10.027","ista":"Gao P, Postiglione MP, Krieger T, Hernandez L, Wang C, Han Z, Streicher C, Papusheva E, Insolera R, Chugh K, Kodish O, Huang K, Simons B, Luo L, Hippenmeyer S, Shi S. 2014. Deterministic progenitor behavior and unitary production of neurons in the neocortex. Cell. 159(4), 775–788.","apa":"Gao, P., Postiglione, M. P., Krieger, T., Hernandez, L., Wang, C., Han, Z., … Shi, S. (2014). Deterministic progenitor behavior and unitary production of neurons in the neocortex. Cell. Cell Press. https://doi.org/10.1016/j.cell.2014.10.027","ieee":"P. Gao et al., “Deterministic progenitor behavior and unitary production of neurons in the neocortex,” Cell, vol. 159, no. 4. Cell Press, pp. 775–788, 2014."},"publication":"Cell","page":"775 - 788"}]