{"title":"Stimulus-dependent maximum entropy models of neural population codes","quality_controlled":"1","status":"public","publisher":"Public Library of Science","scopus_import":1,"oa_version":"Published Version","oa":1,"date_created":"2018-12-11T12:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"GaTk"}],"abstract":[{"text":"Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. For large populations, direct sampling of these distributions is impossible, and so we must rely on constructing appropriate models. We show here that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. We introduce the stimulus-dependent maximum entropy (SDME) model—a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. We find that the SDME model gives a more accurate account of single cell responses and in particular significantly outperforms uncoupled models in reproducing the distributions of population codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like average surprise and information transmission in a neural population.","lang":"eng"}],"publication_status":"published","date_published":"2013-03-01T00:00:00Z","issue":"3","month":"03","date_updated":"2021-01-12T07:00:20Z","tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"type":"journal_article","file":[{"file_name":"IST-2013-120-v1+1_journal.pcbi.1002922.pdf","access_level":"open_access","creator":"system","content_type":"application/pdf","relation":"main_file","checksum":"5a30876c193209fa05b26db71845dd16","file_size":1548120,"date_created":"2018-12-12T10:14:45Z","file_id":"5099","date_updated":"2020-07-14T12:45:52Z"}],"day":"01","ddc":["570"],"file_date_updated":"2020-07-14T12:45:52Z","year":"2013","intvolume":"         9","publist_id":"3926","volume":9,"_id":"2863","article_number":"e1002922","pubrep_id":"120","language":[{"iso":"eng"}],"citation":{"short":"E. Granot Atedgi, G. Tkačik, R. Segev, E. Schneidman, PLoS Computational Biology 9 (2013).","ama":"Granot Atedgi E, Tkačik G, Segev R, Schneidman E. Stimulus-dependent maximum entropy models of neural population codes. <i>PLoS Computational Biology</i>. 2013;9(3). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1002922\">10.1371/journal.pcbi.1002922</a>","apa":"Granot Atedgi, E., Tkačik, G., Segev, R., &#38; Schneidman, E. (2013). Stimulus-dependent maximum entropy models of neural population codes. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1002922\">https://doi.org/10.1371/journal.pcbi.1002922</a>","ista":"Granot Atedgi E, Tkačik G, Segev R, Schneidman E. 2013. Stimulus-dependent maximum entropy models of neural population codes. PLoS Computational Biology. 9(3), e1002922.","chicago":"Granot Atedgi, Einat, Gašper Tkačik, Ronen Segev, and Elad Schneidman. “Stimulus-Dependent Maximum Entropy Models of Neural Population Codes.” <i>PLoS Computational Biology</i>. Public Library of Science, 2013. <a href=\"https://doi.org/10.1371/journal.pcbi.1002922\">https://doi.org/10.1371/journal.pcbi.1002922</a>.","ieee":"E. Granot Atedgi, G. Tkačik, R. Segev, and E. Schneidman, “Stimulus-dependent maximum entropy models of neural population codes,” <i>PLoS Computational Biology</i>, vol. 9, no. 3. Public Library of Science, 2013.","mla":"Granot Atedgi, Einat, et al. “Stimulus-Dependent Maximum Entropy Models of Neural Population Codes.” <i>PLoS Computational Biology</i>, vol. 9, no. 3, e1002922, Public Library of Science, 2013, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1002922\">10.1371/journal.pcbi.1002922</a>."},"author":[{"first_name":"Einat","last_name":"Granot Atedgi","full_name":"Granot Atedgi, Einat"},{"orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper","last_name":"Tkacik","first_name":"Gasper"},{"full_name":"Segev, Ronen","last_name":"Segev","first_name":"Ronen"},{"last_name":"Schneidman","first_name":"Elad","full_name":"Schneidman, Elad"}],"has_accepted_license":"1","publication":"PLoS Computational Biology","doi":"10.1371/journal.pcbi.1002922"}