{"issue":"1","citation":{"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,” <i>PLoS Computational Biology</i>, vol. 10, no. 1. Public Library of Science, 2014.","apa":"Tkačik, G., Marre, O., Amodei, D., Schneidman, E., Bialek, W., &#38; Berry, M. (2014). Searching for collective behavior in a large network of sensory neurons. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1003408\">https://doi.org/10.1371/journal.pcbi.1003408</a>","mla":"Tkačik, Gašper, et al. “Searching for Collective Behavior in a Large Network of Sensory Neurons.” <i>PLoS Computational Biology</i>, vol. 10, no. 1, e1003408, Public Library of Science, 2014, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1003408\">10.1371/journal.pcbi.1003408</a>.","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.","short":"G. Tkačik, O. Marre, D. Amodei, E. Schneidman, W. Bialek, M. Berry, PLoS Computational Biology 10 (2014).","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.” <i>PLoS Computational Biology</i>. Public Library of Science, 2014. <a href=\"https://doi.org/10.1371/journal.pcbi.1003408\">https://doi.org/10.1371/journal.pcbi.1003408</a>.","ama":"Tkačik G, Marre O, Amodei D, Schneidman E, Bialek W, Berry M. Searching for collective behavior in a large network of sensory neurons. <i>PLoS Computational Biology</i>. 2014;10(1). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1003408\">10.1371/journal.pcbi.1003408</a>"},"file":[{"file_size":2194790,"relation":"main_file","access_level":"open_access","date_created":"2018-12-12T10:12:46Z","creator":"system","checksum":"c720222c5e924a4acb17f23b9381a6ca","file_id":"4965","file_name":"IST-2016-436-v1+1_journal.pcbi.1003408.pdf","content_type":"application/pdf","date_updated":"2020-07-14T12:45:35Z"}],"intvolume":"        10","scopus_import":1,"date_created":"2018-12-11T11:56:36Z","author":[{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik","first_name":"Gasper"},{"first_name":"Olivier","last_name":"Marre","full_name":"Marre, Olivier"},{"full_name":"Amodei, Dario","last_name":"Amodei","first_name":"Dario"},{"first_name":"Elad","last_name":"Schneidman","full_name":"Schneidman, Elad"},{"full_name":"Bialek, William","last_name":"Bialek","first_name":"William"},{"full_name":"Berry, Michael","last_name":"Berry","first_name":"Michael"}],"pubrep_id":"436","title":"Searching for collective behavior in a large network of sensory neurons","day":"02","ddc":["570"],"main_file_link":[{"open_access":"1","url":"http://repository.ist.ac.at/id/eprint/436"}],"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.","doi":"10.1371/journal.pcbi.1003408","publication_identifier":{"issn":["1553734X"]},"month":"01","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."}],"publication":"PLoS Computational Biology","volume":10,"has_accepted_license":"1","oa_version":"Published Version","status":"public","_id":"2257","publist_id":"4689","date_published":"2014-01-02T00:00:00Z","file_date_updated":"2020-07-14T12:45:35Z","related_material":{"record":[{"id":"5562","relation":"popular_science","status":"public"}]},"user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","language":[{"iso":"eng"}],"quality_controlled":"1","oa":1,"publisher":"Public Library of Science","date_updated":"2024-02-21T13:46:14Z","department":[{"_id":"GaTk"}],"publication_status":"published","article_number":"e1003408","type":"journal_article","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)"},"year":"2014"}