--- _id: '2863' abstract: - lang: eng 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. article_number: e1002922 author: - first_name: Einat full_name: Granot Atedgi, Einat last_name: Granot Atedgi - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Ronen full_name: Segev, Ronen last_name: Segev - first_name: Elad full_name: Schneidman, Elad last_name: Schneidman citation: ama: Granot Atedgi E, Tkačik G, Segev R, Schneidman E. Stimulus-dependent maximum entropy models of neural population codes. PLoS Computational Biology. 2013;9(3). doi:10.1371/journal.pcbi.1002922 apa: Granot Atedgi, E., Tkačik, G., Segev, R., & Schneidman, E. (2013). Stimulus-dependent maximum entropy models of neural population codes. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1002922 chicago: Granot Atedgi, Einat, Gašper Tkačik, Ronen Segev, and Elad Schneidman. “Stimulus-Dependent Maximum Entropy Models of Neural Population Codes.” PLoS Computational Biology. Public Library of Science, 2013. https://doi.org/10.1371/journal.pcbi.1002922. ieee: E. Granot Atedgi, G. Tkačik, R. Segev, and E. Schneidman, “Stimulus-dependent maximum entropy models of neural population codes,” PLoS Computational Biology, vol. 9, no. 3. Public Library of Science, 2013. 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. mla: Granot Atedgi, Einat, et al. “Stimulus-Dependent Maximum Entropy Models of Neural Population Codes.” PLoS Computational Biology, vol. 9, no. 3, e1002922, Public Library of Science, 2013, doi:10.1371/journal.pcbi.1002922. short: E. Granot Atedgi, G. Tkačik, R. Segev, E. Schneidman, PLoS Computational Biology 9 (2013). date_created: 2018-12-11T12:00:00Z date_published: 2013-03-01T00:00:00Z date_updated: 2021-01-12T07:00:20Z day: '01' ddc: - '570' department: - _id: GaTk doi: 10.1371/journal.pcbi.1002922 file: - access_level: open_access checksum: 5a30876c193209fa05b26db71845dd16 content_type: application/pdf creator: system date_created: 2018-12-12T10:14:45Z date_updated: 2020-07-14T12:45:52Z file_id: '5099' file_name: IST-2013-120-v1+1_journal.pcbi.1002922.pdf file_size: 1548120 relation: main_file file_date_updated: 2020-07-14T12:45:52Z has_accepted_license: '1' intvolume: ' 9' issue: '3' language: - iso: eng month: '03' oa: 1 oa_version: Published Version publication: PLoS Computational Biology publication_status: published publisher: Public Library of Science publist_id: '3926' pubrep_id: '120' quality_controlled: '1' scopus_import: 1 status: public title: Stimulus-dependent maximum entropy models of neural 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: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 9 year: '2013' ...