[{"month":"05","article_number":"e1006057","oa_version":"Published Version","project":[{"_id":"25CBA828-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"Human Brain Project Specific Grant Agreement 1 (HBP SGA 1)","grant_number":"720270"},{"_id":"254D1A94-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"P 25651-N26","name":"Sensitivity to higher-order statistics in natural scenes"}],"publication":"PLoS Computational Biology","has_accepted_license":"1","language":[{"iso":"eng"}],"oa":1,"date_published":"2018-05-10T00:00:00Z","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","status":"public","related_material":{"record":[{"id":"5584","relation":"research_data","status":"public"}],"link":[{"url":"https://ist.ac.at/en/news/video-of-moving-discs-reconstructed-from-rat-retinal-neuron-signals/","relation":"press_release","description":"News on IST Homepage"}]},"file":[{"date_created":"2019-02-13T11:07:15Z","checksum":"3026f94d235219e15514505fdbadf34e","file_size":3460786,"date_updated":"2020-07-14T12:45:53Z","content_type":"application/pdf","file_name":"2018_Plos_Botella_Soler.pdf","relation":"main_file","access_level":"open_access","file_id":"5974","creator":"dernst"}],"title":"Nonlinear decoding of a complex movie from the mammalian retina","intvolume":"        14","publication_status":"published","date_created":"2018-12-11T11:45:39Z","article_processing_charge":"Yes","department":[{"_id":"GaTk"}],"author":[{"last_name":"Botella Soler","first_name":"Vicent","full_name":"Botella Soler, Vicent","orcid":"0000-0002-8790-1914","id":"421234E8-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Deny","first_name":"Stephane","full_name":"Deny, Stephane"},{"full_name":"Martius, Georg S","last_name":"Martius","first_name":"Georg S"},{"full_name":"Marre, Olivier","last_name":"Marre","first_name":"Olivier"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik","first_name":"Gasper"}],"issue":"5","_id":"292","scopus_import":"1","article_type":"original","publisher":"Public Library of Science","file_date_updated":"2020-07-14T12:45:53Z","ec_funded":1,"quality_controlled":"1","abstract":[{"lang":"eng","text":"Retina is a paradigmatic system for studying sensory encoding: the transformation of light into spiking activity of ganglion cells. The inverse problem, where stimulus is reconstructed from spikes, has received less attention, especially for complex stimuli that should be reconstructed “pixel-by-pixel”. We recorded around a hundred neurons from a dense patch in a rat retina and decoded movies of multiple small randomly-moving discs. We constructed nonlinear (kernelized and neural network) decoders that improved significantly over linear results. An important contribution to this was the ability of nonlinear decoders to reliably separate between neural responses driven by locally fluctuating light signals, and responses at locally constant light driven by spontaneous-like activity. This improvement crucially depended on the precise, non-Poisson temporal structure of individual spike trains, which originated in the spike-history dependence of neural responses. We propose a general principle by which downstream circuitry could discriminate between spontaneous and stimulus-driven activity based solely on higher-order statistical structure in the incoming spike trains."}],"doi":"10.1371/journal.pcbi.1006057","day":"10","isi":1,"external_id":{"isi":["000434012100002"]},"date_updated":"2024-02-21T13:45:25Z","year":"2018","citation":{"apa":"Botella Soler, V., Deny, S., Martius, G. S., Marre, O., &#38; Tkačik, G. (2018). Nonlinear decoding of a complex movie from the mammalian retina. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1006057\">https://doi.org/10.1371/journal.pcbi.1006057</a>","ama":"Botella Soler V, Deny S, Martius GS, Marre O, Tkačik G. Nonlinear decoding of a complex movie from the mammalian retina. <i>PLoS Computational Biology</i>. 2018;14(5). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1006057\">10.1371/journal.pcbi.1006057</a>","chicago":"Botella Soler, Vicente, Stephane Deny, Georg S Martius, Olivier Marre, and Gašper Tkačik. “Nonlinear Decoding of a Complex Movie from the Mammalian Retina.” <i>PLoS Computational Biology</i>. Public Library of Science, 2018. <a href=\"https://doi.org/10.1371/journal.pcbi.1006057\">https://doi.org/10.1371/journal.pcbi.1006057</a>.","ieee":"V. Botella Soler, S. Deny, G. S. Martius, O. Marre, and G. Tkačik, “Nonlinear decoding of a complex movie from the mammalian retina,” <i>PLoS Computational Biology</i>, vol. 14, no. 5. Public Library of Science, 2018.","mla":"Botella Soler, Vicente, et al. “Nonlinear Decoding of a Complex Movie from the Mammalian Retina.” <i>PLoS Computational Biology</i>, vol. 14, no. 5, e1006057, Public Library of Science, 2018, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1006057\">10.1371/journal.pcbi.1006057</a>.","short":"V. Botella Soler, S. Deny, G.S. Martius, O. Marre, G. Tkačik, PLoS Computational Biology 14 (2018).","ista":"Botella Soler V, Deny S, Martius GS, Marre O, Tkačik G. 2018. Nonlinear decoding of a complex movie from the mammalian retina. PLoS Computational Biology. 14(5), e1006057."},"ddc":["570"],"volume":14},{"publist_id":"7273","oa":1,"date_published":"2018-01-02T00:00:00Z","type":"journal_article","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","status":"public","main_file_link":[{"url":"https://doi.org/10.1101/152660 ","open_access":"1"}],"month":"01","oa_version":"Submitted Version","project":[{"call_identifier":"FWF","_id":"254D1A94-B435-11E9-9278-68D0E5697425","grant_number":"P 25651-N26","name":"Sensitivity to higher-order statistics in natural scenes"}],"publication":"PNAS","language":[{"iso":"eng"}],"abstract":[{"text":"A central goal in theoretical neuroscience is to predict the response properties of sensory neurons from first principles. To this end, “efficient coding” posits that sensory neurons encode maximal information about their inputs given internal constraints. There exist, however, many variants of efficient coding (e.g., redundancy reduction, different formulations of predictive coding, robust coding, sparse coding, etc.), differing in their regimes of applicability, in the relevance of signals to be encoded, and in the choice of constraints. It is unclear how these types of efficient coding relate or what is expected when different coding objectives are combined. Here we present a unified framework that encompasses previously proposed efficient coding models and extends to unique regimes. We show that optimizing neural responses to encode predictive information can lead them to either correlate or decorrelate their inputs, depending on the stimulus statistics; in contrast, at low noise, efficiently encoding the past always predicts decorrelation. Later, we investigate coding of naturalistic movies and show that qualitatively different types of visual motion tuning and levels of response sparsity are predicted, depending on whether the objective is to recover the past or predict the future. Our approach promises a way to explain the observed diversity of sensory neural responses, as due to multiple functional goals and constraints fulfilled by different cell types and/or circuits.","lang":"eng"}],"doi":"10.1073/pnas.1711114115","day":"02","isi":1,"external_id":{"isi":["000419128700049"]},"date_updated":"2023-09-19T10:16:35Z","citation":{"short":"M.J. Chalk, O. Marre, G. Tkačik, PNAS 115 (2018) 186–191.","mla":"Chalk, Matthew J., et al. “Toward a Unified Theory of Efficient, Predictive, and Sparse Coding.” <i>PNAS</i>, vol. 115, no. 1, National Academy of Sciences, 2018, pp. 186–91, doi:<a href=\"https://doi.org/10.1073/pnas.1711114115\">10.1073/pnas.1711114115</a>.","ista":"Chalk MJ, Marre O, Tkačik G. 2018. Toward a unified theory of efficient, predictive, and sparse coding. PNAS. 115(1), 186–191.","ama":"Chalk MJ, Marre O, Tkačik G. Toward a unified theory of efficient, predictive, and sparse coding. <i>PNAS</i>. 2018;115(1):186-191. doi:<a href=\"https://doi.org/10.1073/pnas.1711114115\">10.1073/pnas.1711114115</a>","apa":"Chalk, M. J., Marre, O., &#38; Tkačik, G. (2018). Toward a unified theory of efficient, predictive, and sparse coding. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1711114115\">https://doi.org/10.1073/pnas.1711114115</a>","ieee":"M. J. Chalk, O. Marre, and G. Tkačik, “Toward a unified theory of efficient, predictive, and sparse coding,” <i>PNAS</i>, vol. 115, no. 1. National Academy of Sciences, pp. 186–191, 2018.","chicago":"Chalk, Matthew J, Olivier Marre, and Gašper Tkačik. “Toward a Unified Theory of Efficient, Predictive, and Sparse Coding.” <i>PNAS</i>. National Academy of Sciences, 2018. <a href=\"https://doi.org/10.1073/pnas.1711114115\">https://doi.org/10.1073/pnas.1711114115</a>."},"year":"2018","volume":115,"title":"Toward a unified theory of efficient, predictive, and sparse coding","intvolume":"       115","publication_status":"published","date_created":"2018-12-11T11:47:04Z","department":[{"_id":"GaTk"}],"article_processing_charge":"No","author":[{"orcid":"0000-0001-7782-4436","full_name":"Chalk, Matthew J","first_name":"Matthew J","last_name":"Chalk","id":"2BAAC544-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Marre","first_name":"Olivier","full_name":"Marre, Olivier"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","last_name":"Tkacik","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper"}],"issue":"1","_id":"543","scopus_import":"1","publisher":"National Academy of Sciences","page":"186 - 191","quality_controlled":"1"},{"publisher":"Institute of Science and Technology Austria","file_date_updated":"2020-07-14T12:47:07Z","keyword":["retina","decoding","regression","neural networks","complex stimulus"],"month":"03","title":"Nonlinear decoding of a complex movie from the mammalian retina","oa_version":"Published Version","department":[{"_id":"ChLa"},{"_id":"GaTk"}],"project":[{"name":"Sensitivity to higher-order statistics in natural scenes","grant_number":"P 25651-N26","_id":"254D1A94-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"}],"article_processing_charge":"No","date_created":"2018-12-12T12:31:39Z","author":[{"full_name":"Deny, Stephane","first_name":"Stephane","last_name":"Deny"},{"last_name":"Marre","first_name":"Olivier","full_name":"Marre, Olivier"},{"full_name":"Botella-Soler, Vicente","first_name":"Vicente","last_name":"Botella-Soler"},{"last_name":"Martius","first_name":"Georg S","full_name":"Martius, Georg S","id":"3A276B68-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"datarep_id":"98","_id":"5584","has_accepted_license":"1","related_material":{"record":[{"status":"public","id":"292","relation":"used_in_publication"}]},"ddc":["570"],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","file":[{"date_updated":"2020-07-14T12:47:07Z","content_type":"application/octet-stream","file_name":"IST-2018-98-v1+1_BBalls_area2_tile2_20x20.mat","date_created":"2018-12-12T13:02:24Z","checksum":"6808748837b9afbbbabc2a356ca2b88a","file_size":1142543971,"file_id":"5590","creator":"system","relation":"main_file","access_level":"open_access"},{"file_id":"5591","creator":"system","access_level":"open_access","relation":"main_file","date_updated":"2020-07-14T12:47:07Z","content_type":"application/pdf","file_name":"IST-2018-98-v1+2_ExperimentStructure.pdf","date_created":"2018-12-12T13:02:25Z","file_size":702336,"checksum":"d6d6cd07743038fe3a12352983fcf9dd"},{"date_updated":"2020-07-14T12:47:07Z","content_type":"application/octet-stream","file_name":"IST-2018-98-v1+3_GoodLocations_area2_20x20.mat","date_created":"2018-12-12T13:02:26Z","file_size":432,"checksum":"0c9cfb4dab35bb3dc25a04395600b1c8","file_id":"5592","creator":"system","access_level":"open_access","relation":"main_file"},{"date_updated":"2020-07-14T12:47:07Z","file_name":"IST-2018-98-v1+4_README.txt","content_type":"text/plain","date_created":"2018-12-12T13:02:26Z","checksum":"2a83b011012e21e934b4596285b1a183","file_size":986,"file_id":"5593","creator":"system","relation":"main_file","access_level":"open_access"}],"abstract":[{"lang":"eng","text":"This package contains data for the publication \"Nonlinear decoding of a complex movie from the mammalian retina\" by Deny S. et al, PLOS Comput Biol (2018). \r\n\r\nThe data consists of\r\n(i) 91 spike sorted, isolated rat retinal ganglion cells that pass stability and quality criteria, recorded on the multi-electrode array, in response to the presentation of the complex movie with many randomly moving dark discs. The responses are represented as 648000 x 91 binary matrix, where the first index indicates the timebin of duration 12.5 ms, and the second index the neural identity. The matrix entry is 0/1 if the neuron didn't/did spike in the particular time bin.\r\n(ii) README file and a graphical illustration of the structure of the experiment, specifying how the 648000 timebins are split into epochs where 1, 2, 4, or 10 discs  were displayed, and which stimulus segments are exact repeats or unique ball trajectories.\r\n(iii) a 648000 x 400 matrix of luminance traces for each of the 20 x 20 positions (\"sites\") in the movie frame, with time that is locked to the recorded raster. The luminance traces are produced as described in the manuscript by filtering the raw disc movie with a small gaussian spatial kernel. "}],"oa":1,"doi":"10.15479/AT:ISTA:98","day":"29","date_published":"2018-03-29T00:00:00Z","type":"research_data","date_updated":"2024-02-21T13:45:26Z","tmp":{"legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","short":"CC0 (1.0)","name":"Creative Commons Public Domain Dedication (CC0 1.0)","image":"/images/cc_0.png"},"citation":{"short":"S. Deny, O. Marre, V. Botella-Soler, G.S. Martius, G. Tkačik, (2018).","mla":"Deny, Stephane, et al. <i>Nonlinear Decoding of a Complex Movie from the Mammalian Retina</i>. Institute of Science and Technology Austria, 2018, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:98\">10.15479/AT:ISTA:98</a>.","ista":"Deny S, Marre O, Botella-Soler V, Martius GS, Tkačik G. 2018. Nonlinear decoding of a complex movie from the mammalian retina, Institute of Science and Technology Austria, <a href=\"https://doi.org/10.15479/AT:ISTA:98\">10.15479/AT:ISTA:98</a>.","ama":"Deny S, Marre O, Botella-Soler V, Martius GS, Tkačik G. Nonlinear decoding of a complex movie from the mammalian retina. 2018. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:98\">10.15479/AT:ISTA:98</a>","apa":"Deny, S., Marre, O., Botella-Soler, V., Martius, G. S., &#38; Tkačik, G. (2018). Nonlinear decoding of a complex movie from the mammalian retina. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:98\">https://doi.org/10.15479/AT:ISTA:98</a>","chicago":"Deny, Stephane, Olivier Marre, Vicente Botella-Soler, Georg S Martius, and Gašper Tkačik. “Nonlinear Decoding of a Complex Movie from the Mammalian Retina.” Institute of Science and Technology Austria, 2018. <a href=\"https://doi.org/10.15479/AT:ISTA:98\">https://doi.org/10.15479/AT:ISTA:98</a>.","ieee":"S. Deny, O. Marre, V. Botella-Soler, G. S. Martius, and G. Tkačik, “Nonlinear decoding of a complex movie from the mammalian retina.” Institute of Science and Technology Austria, 2018."},"year":"2018"},{"file_date_updated":"2018-12-12T10:16:06Z","quality_controlled":"1","ec_funded":1,"publisher":"Nature Publishing Group","author":[{"first_name":"Stephane","last_name":"Deny","full_name":"Deny, Stephane"},{"full_name":"Ferrari, Ulisse","last_name":"Ferrari","first_name":"Ulisse"},{"full_name":"Mace, Emilie","first_name":"Emilie","last_name":"Mace"},{"full_name":"Yger, Pierre","first_name":"Pierre","last_name":"Yger"},{"full_name":"Caplette, Romain","first_name":"Romain","last_name":"Caplette"},{"last_name":"Picaud","first_name":"Serge","full_name":"Picaud, Serge"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","last_name":"Tkacik","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper"},{"last_name":"Marre","first_name":"Olivier","full_name":"Marre, Olivier"}],"issue":"1","_id":"1104","scopus_import":"1","pubrep_id":"921","title":"Multiplexed computations in retinal ganglion cells of a single type","intvolume":"         8","publication_status":"published","department":[{"_id":"GaTk"}],"date_created":"2018-12-11T11:50:10Z","article_processing_charge":"No","ddc":["571"],"volume":8,"isi":1,"external_id":{"isi":["000417241200004"]},"date_updated":"2023-09-20T11:41:19Z","year":"2017","citation":{"ama":"Deny S, Ferrari U, Mace E, et al. Multiplexed computations in retinal ganglion cells of a single type. <i>Nature Communications</i>. 2017;8(1). doi:<a href=\"https://doi.org/10.1038/s41467-017-02159-y\">10.1038/s41467-017-02159-y</a>","apa":"Deny, S., Ferrari, U., Mace, E., Yger, P., Caplette, R., Picaud, S., … Marre, O. (2017). Multiplexed computations in retinal ganglion cells of a single type. <i>Nature Communications</i>. Nature Publishing Group. <a href=\"https://doi.org/10.1038/s41467-017-02159-y\">https://doi.org/10.1038/s41467-017-02159-y</a>","chicago":"Deny, Stephane, Ulisse Ferrari, Emilie Mace, Pierre Yger, Romain Caplette, Serge Picaud, Gašper Tkačik, and Olivier Marre. “Multiplexed Computations in Retinal Ganglion Cells of a Single Type.” <i>Nature Communications</i>. Nature Publishing Group, 2017. <a href=\"https://doi.org/10.1038/s41467-017-02159-y\">https://doi.org/10.1038/s41467-017-02159-y</a>.","ieee":"S. Deny <i>et al.</i>, “Multiplexed computations in retinal ganglion cells of a single type,” <i>Nature Communications</i>, vol. 8, no. 1. Nature Publishing Group, 2017.","mla":"Deny, Stephane, et al. “Multiplexed Computations in Retinal Ganglion Cells of a Single Type.” <i>Nature Communications</i>, vol. 8, no. 1, 1964, Nature Publishing Group, 2017, doi:<a href=\"https://doi.org/10.1038/s41467-017-02159-y\">10.1038/s41467-017-02159-y</a>.","short":"S. Deny, U. Ferrari, E. Mace, P. Yger, R. Caplette, S. Picaud, G. Tkačik, O. Marre, Nature Communications 8 (2017).","ista":"Deny S, Ferrari U, Mace E, Yger P, Caplette R, Picaud S, Tkačik G, Marre O. 2017. Multiplexed computations in retinal ganglion cells of a single type. Nature Communications. 8(1), 1964."},"abstract":[{"lang":"eng","text":"In the early visual system, cells of the same type perform the same computation in different places of the visual field. How these cells code together a complex visual scene is unclear. A common assumption is that cells of a single-type extract a single-stimulus feature to form a feature map, but this has rarely been observed directly. Using large-scale recordings in the rat retina, we show that a homogeneous population of fast OFF ganglion cells simultaneously encodes two radically different features of a visual scene. Cells close to a moving object code quasilinearly for its position, while distant cells remain largely invariant to the object's position and, instead, respond nonlinearly to changes in the object's speed. We develop a quantitative model that accounts for this effect and identify a disinhibitory circuit that mediates it. Ganglion cells of a single type thus do not code for one, but two features simultaneously. This richer, flexible neural map might also be present in other sensory systems."}],"doi":"10.1038/s41467-017-02159-y","day":"06","language":[{"iso":"eng"}],"publication":"Nature Communications","has_accepted_license":"1","month":"12","article_number":"1964","oa_version":"Published Version","project":[{"call_identifier":"FP7","_id":"25CD3DD2-B435-11E9-9278-68D0E5697425","name":"Localization of ion channels and receptors by two and three-dimensional immunoelectron microscopic approaches","grant_number":"604102"},{"call_identifier":"FWF","_id":"254D1A94-B435-11E9-9278-68D0E5697425","grant_number":"P 25651-N26","name":"Sensitivity to higher-order statistics in natural scenes"}],"status":"public","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","file":[{"creator":"system","file_id":"5191","access_level":"open_access","relation":"main_file","content_type":"application/pdf","file_name":"IST-2018-921-v1+1_s41467-017-02159-y.pdf","date_updated":"2018-12-12T10:16:06Z","file_size":2872887,"date_created":"2018-12-12T10:16:06Z"}],"date_published":"2017-12-06T00:00:00Z","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"oa":1,"publist_id":"6266","publication_identifier":{"issn":["20411723"]}},{"date_updated":"2021-01-12T08:12:21Z","citation":{"mla":"Humplik, Jan, and Gašper Tkačik. “Probabilistic Models for Neural Populations That Naturally Capture Global Coupling and Criticality.” <i>PLoS Computational Biology</i>, vol. 13, no. 9, e1005763, Public Library of Science, 2017, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005763\">10.1371/journal.pcbi.1005763</a>.","short":"J. Humplik, G. Tkačik, PLoS Computational Biology 13 (2017).","ista":"Humplik J, Tkačik G. 2017. Probabilistic models for neural populations that naturally capture global coupling and criticality. PLoS Computational Biology. 13(9), e1005763.","apa":"Humplik, J., &#38; Tkačik, G. (2017). Probabilistic models for neural populations that naturally capture global coupling and criticality. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1005763\">https://doi.org/10.1371/journal.pcbi.1005763</a>","ama":"Humplik J, Tkačik G. Probabilistic models for neural populations that naturally capture global coupling and criticality. <i>PLoS Computational Biology</i>. 2017;13(9). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005763\">10.1371/journal.pcbi.1005763</a>","ieee":"J. Humplik and G. Tkačik, “Probabilistic models for neural populations that naturally capture global coupling and criticality,” <i>PLoS Computational Biology</i>, vol. 13, no. 9. Public Library of Science, 2017.","chicago":"Humplik, Jan, and Gašper Tkačik. “Probabilistic Models for Neural Populations That Naturally Capture Global Coupling and Criticality.” <i>PLoS Computational Biology</i>. Public Library of Science, 2017. <a href=\"https://doi.org/10.1371/journal.pcbi.1005763\">https://doi.org/10.1371/journal.pcbi.1005763</a>."},"year":"2017","abstract":[{"lang":"eng","text":"Advances in multi-unit recordings pave the way for statistical modeling of activity patterns in large neural populations. Recent studies have shown that the summed activity of all neurons strongly shapes the population response. A separate recent finding has been that neural populations also exhibit criticality, an anomalously large dynamic range for the probabilities of different population activity patterns. Motivated by these two observations, we introduce a class of probabilistic models which takes into account the prior knowledge that the neural population could be globally coupled and close to critical. These models consist of an energy function which parametrizes interactions between small groups of neurons, and an arbitrary positive, strictly increasing, and twice differentiable function which maps the energy of a population pattern to its probability. We show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an accurate description of the activity of retinal ganglion cells which outperforms previous models based on the summed activity of neurons; 2) prior knowledge that the population is critical translates to prior expectations about the shape of the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous latent variable globally coupling the system whose distribution we can infer from data. Our method is independent of the underlying system’s state space; hence, it can be applied to other systems such as natural scenes or amino acid sequences of proteins which are also known to exhibit criticality."}],"doi":"10.1371/journal.pcbi.1005763","day":"19","ddc":["530","571"],"volume":13,"author":[{"id":"2E9627A8-F248-11E8-B48F-1D18A9856A87","full_name":"Humplik, Jan","last_name":"Humplik","first_name":"Jan"},{"first_name":"Gasper","last_name":"Tkacik","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"issue":"9","_id":"720","scopus_import":1,"pubrep_id":"884","title":"Probabilistic models for neural populations that naturally capture global coupling and criticality","intvolume":"        13","publication_status":"published","department":[{"_id":"GaTk"}],"article_processing_charge":"Yes","date_created":"2018-12-11T11:48:08Z","file_date_updated":"2020-07-14T12:47:53Z","quality_controlled":"1","publisher":"Public Library of Science","date_published":"2017-09-19T00:00:00Z","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"oa":1,"publist_id":"6960","publication_identifier":{"issn":["1553734X"]},"status":"public","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","file":[{"file_id":"5352","creator":"system","access_level":"open_access","relation":"main_file","date_updated":"2020-07-14T12:47:53Z","content_type":"application/pdf","file_name":"IST-2017-884-v1+1_journal.pcbi.1005763.pdf","date_created":"2018-12-12T10:18:30Z","file_size":14167050,"checksum":"81107096c19771c36ddbe6f0282a3acb"}],"publication":"PLoS Computational Biology","has_accepted_license":"1","month":"09","article_number":"e1005763","oa_version":"Published Version","project":[{"name":"Information processing and computation in fish groups","grant_number":"RGP0065/2012","_id":"255008E4-B435-11E9-9278-68D0E5697425"},{"call_identifier":"FWF","_id":"254D1A94-B435-11E9-9278-68D0E5697425","name":"Sensitivity to higher-order statistics in natural scenes","grant_number":"P 25651-N26"}],"language":[{"iso":"eng"}]},{"oa_version":"Published Version","project":[{"call_identifier":"FWF","_id":"254D1A94-B435-11E9-9278-68D0E5697425","grant_number":"P 25651-N26","name":"Sensitivity to higher-order statistics in natural scenes"}],"month":"11","article_number":"e1005855","publication":"PLoS Computational Biology","has_accepted_license":"1","language":[{"iso":"eng"}],"oa":1,"publist_id":"6153","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"date_published":"2016-11-17T00:00:00Z","type":"journal_article","file":[{"date_updated":"2020-07-14T12:44:38Z","content_type":"application/pdf","file_name":"2016_PLOS_Prentice.pdf","date_created":"2019-01-25T10:35:00Z","file_size":4492021,"checksum":"47b08cbd4dbf32b25ba161f5f4b262cc","file_id":"5884","creator":"kschuh","relation":"main_file","access_level":"open_access"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","related_material":{"record":[{"id":"9709","relation":"research_data","status":"public"}]},"status":"public","publication_status":"published","department":[{"_id":"GaTk"}],"date_created":"2018-12-11T11:50:40Z","title":"Error-robust modes of the retinal population code","intvolume":"        12","_id":"1197","scopus_import":1,"author":[{"full_name":"Prentice, Jason","last_name":"Prentice","first_name":"Jason"},{"full_name":"Marre, Olivier","last_name":"Marre","first_name":"Olivier"},{"full_name":"Ioffe, Mark","first_name":"Mark","last_name":"Ioffe"},{"first_name":"Adrianna","last_name":"Loback","full_name":"Loback, Adrianna"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik","first_name":"Gasper"},{"last_name":"Berry","first_name":"Michael","full_name":"Berry, Michael"}],"issue":"11","publisher":"Public Library of Science","quality_controlled":"1","file_date_updated":"2020-07-14T12:44:38Z","doi":"10.1371/journal.pcbi.1005148","day":"17","abstract":[{"text":"Across the nervous system, certain population spiking patterns are observed far more frequently than others. A hypothesis about this structure is that these collective activity patterns function as population codewords–collective modes–carrying information distinct from that of any single cell. We investigate this phenomenon in recordings of ∼150 retinal ganglion cells, the retina’s output. We develop a novel statistical model that decomposes the population response into modes; it predicts the distribution of spiking activity in the ganglion cell population with high accuracy. We found that the modes represent localized features of the visual stimulus that are distinct from the features represented by single neurons. Modes form clusters of activity states that are readily discriminated from one another. When we repeated the same visual stimulus, we found that the same mode was robustly elicited. These results suggest that retinal ganglion cells’ collective signaling is endowed with a form of error-correcting code–a principle that may hold in brain areas beyond retina.","lang":"eng"}],"date_updated":"2023-02-23T14:05:40Z","citation":{"short":"J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, M. Berry, PLoS Computational Biology 12 (2016).","mla":"Prentice, Jason, et al. “Error-Robust Modes of the Retinal Population Code.” <i>PLoS Computational Biology</i>, vol. 12, no. 11, e1005855, Public Library of Science, 2016, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005148\">10.1371/journal.pcbi.1005148</a>.","ista":"Prentice J, Marre O, Ioffe M, Loback A, Tkačik G, Berry M. 2016. Error-robust modes of the retinal population code. PLoS Computational Biology. 12(11), e1005855.","apa":"Prentice, J., Marre, O., Ioffe, M., Loback, A., Tkačik, G., &#38; Berry, M. (2016). Error-robust modes of the retinal population code. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1005148\">https://doi.org/10.1371/journal.pcbi.1005148</a>","ama":"Prentice J, Marre O, Ioffe M, Loback A, Tkačik G, Berry M. Error-robust modes of the retinal population code. <i>PLoS Computational Biology</i>. 2016;12(11). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005148\">10.1371/journal.pcbi.1005148</a>","chicago":"Prentice, Jason, Olivier Marre, Mark Ioffe, Adrianna Loback, Gašper Tkačik, and Michael Berry. “Error-Robust Modes of the Retinal Population Code.” <i>PLoS Computational Biology</i>. Public Library of Science, 2016. <a href=\"https://doi.org/10.1371/journal.pcbi.1005148\">https://doi.org/10.1371/journal.pcbi.1005148</a>.","ieee":"J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, and M. Berry, “Error-robust modes of the retinal population code,” <i>PLoS Computational Biology</i>, vol. 12, no. 11. Public Library of Science, 2016."},"year":"2016","volume":12,"acknowledgement":"JSP was supported by a C.V. Starr Fellowship from the Starr Foundation (http://www.starrfoundation.org/). GT was supported by Austrian Research Foundation (https://www.fwf.ac.at/en/) grant FWF P25651. MJB received support from National Eye Institute (https://nei.nih.gov/) grant EY 14196 and from the National Science Foundation grant 1504977. The authors thank Cristina Savin and Vicent Botella-Soler for helpful comments on the manuscript.","ddc":["570"]},{"year":"2016","citation":{"ieee":"G. Tkačik and W. Bialek, “Information processing in living systems,” <i>Annual Review of Condensed Matter Physics</i>, vol. 7. Annual Reviews, pp. 89–117, 2016.","chicago":"Tkačik, Gašper, and William Bialek. “Information Processing in Living Systems.” <i>Annual Review of Condensed Matter Physics</i>. Annual Reviews, 2016. <a href=\"https://doi.org/10.1146/annurev-conmatphys-031214-014803\">https://doi.org/10.1146/annurev-conmatphys-031214-014803</a>.","apa":"Tkačik, G., &#38; Bialek, W. (2016). Information processing in living systems. <i>Annual Review of Condensed Matter Physics</i>. Annual Reviews. <a href=\"https://doi.org/10.1146/annurev-conmatphys-031214-014803\">https://doi.org/10.1146/annurev-conmatphys-031214-014803</a>","ama":"Tkačik G, Bialek W. Information processing in living systems. <i>Annual Review of Condensed Matter Physics</i>. 2016;7:89-117. doi:<a href=\"https://doi.org/10.1146/annurev-conmatphys-031214-014803\">10.1146/annurev-conmatphys-031214-014803</a>","ista":"Tkačik G, Bialek W. 2016. Information processing in living systems. Annual Review of Condensed Matter Physics. 7, 89–117.","mla":"Tkačik, Gašper, and William Bialek. “Information Processing in Living Systems.” <i>Annual Review of Condensed Matter Physics</i>, vol. 7, Annual Reviews, 2016, pp. 89–117, doi:<a href=\"https://doi.org/10.1146/annurev-conmatphys-031214-014803\">10.1146/annurev-conmatphys-031214-014803</a>.","short":"G. Tkačik, W. Bialek, Annual Review of Condensed Matter Physics 7 (2016) 89–117."},"date_updated":"2021-01-12T06:49:23Z","abstract":[{"lang":"eng","text":"Life depends as much on the flow of information as on the flow of energy. Here we review the many efforts to make this intuition precise. Starting with the building blocks of information theory, we explore examples where it has been possible to measure, directly, the flow of information in biological networks, or more generally where information-theoretic ideas have been used to guide the analysis of experiments. Systems of interest range from single molecules (the sequence diversity in families of proteins) to groups of organisms (the distribution of velocities in flocks of birds), and all scales in between. Many of these analyses are motivated by the idea that biological systems may have evolved to optimize the gathering and representation of information, and we review the experimental evidence for this optimization, again across a wide range of scales."}],"day":"10","doi":"10.1146/annurev-conmatphys-031214-014803","acknowledgement":"Our work was supported in part by the US\r\nNational Science Foundation (PHY–1305525 and CCF–\r\n0939370), by the Austrian Science Foundation (FWF\r\nP25651), by the Human Frontiers Science Program, and\r\nby the Simons and Swartz Foundations.","volume":7,"author":[{"last_name":"Tkacik","first_name":"Gasper","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Bialek","first_name":"William","full_name":"Bialek, William"}],"scopus_import":1,"_id":"1248","intvolume":"         7","title":"Information processing in living systems","date_created":"2018-12-11T11:50:56Z","department":[{"_id":"GaTk"}],"publication_status":"published","quality_controlled":"1","page":"89 - 117","publisher":"Annual Reviews","type":"journal_article","date_published":"2016-03-10T00:00:00Z","oa":1,"publist_id":"6080","status":"public","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1412.8752"}],"publication":"Annual Review of Condensed Matter Physics","month":"03","project":[{"call_identifier":"FWF","_id":"254D1A94-B435-11E9-9278-68D0E5697425","grant_number":"P 25651-N26","name":"Sensitivity to higher-order statistics in natural scenes"}],"oa_version":"Preprint","language":[{"iso":"eng"}]},{"publication_status":"published","department":[{"_id":"GaTk"}],"date_created":"2018-12-11T11:53:31Z","title":"High accuracy decoding of dynamical motion from a large retinal population","pubrep_id":"455","intvolume":"        11","_id":"1697","scopus_import":1,"author":[{"full_name":"Marre, Olivier","last_name":"Marre","first_name":"Olivier"},{"id":"421234E8-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8790-1914","full_name":"Botella Soler, Vicente","first_name":"Vicente","last_name":"Botella Soler"},{"full_name":"Simmons, Kristina","last_name":"Simmons","first_name":"Kristina"},{"first_name":"Thierry","last_name":"Mora","full_name":"Mora, Thierry"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik","first_name":"Gasper"},{"first_name":"Michael","last_name":"Berry","full_name":"Berry, Michael"}],"issue":"7","publisher":"Public Library of Science","quality_controlled":"1","file_date_updated":"2020-07-14T12:45:12Z","doi":"10.1371/journal.pcbi.1004304","day":"01","abstract":[{"lang":"eng","text":"Motion tracking is a challenge the visual system has to solve by reading out the retinal population. It is still unclear how the information from different neurons can be combined together to estimate the position of an object. Here we recorded a large population of ganglion cells in a dense patch of salamander and guinea pig retinas while displaying a bar moving diffusively. We show that the bar’s position can be reconstructed from retinal activity with a precision in the hyperacuity regime using a linear decoder acting on 100+ cells. We then took advantage of this unprecedented precision to explore the spatial structure of the retina’s population code. The classical view would have suggested that the firing rates of the cells form a moving hill of activity tracking the bar’s position. Instead, we found that most ganglion cells in the salamander fired sparsely and idiosyncratically, so that their neural image did not track the bar. Furthermore, ganglion cell activity spanned an area much larger than predicted by their receptive fields, with cells coding for motion far in their surround. As a result, population redundancy was high, and we could find multiple, disjoint subsets of neurons that encoded the trajectory with high precision. This organization allows for diverse collections of ganglion cells to represent high-accuracy motion information in a form easily read out by downstream neural circuits."}],"date_updated":"2021-01-12T06:52:35Z","citation":{"ista":"Marre O, Botella Soler V, Simmons K, Mora T, Tkačik G, Berry M. 2015. High accuracy decoding of dynamical motion from a large retinal population. PLoS Computational Biology. 11(7), e1004304.","mla":"Marre, Olivier, et al. “High Accuracy Decoding of Dynamical Motion from a Large Retinal Population.” <i>PLoS Computational Biology</i>, vol. 11, no. 7, e1004304, Public Library of Science, 2015, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1004304\">10.1371/journal.pcbi.1004304</a>.","short":"O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkačik, M. Berry, PLoS Computational Biology 11 (2015).","chicago":"Marre, Olivier, Vicente Botella Soler, Kristina Simmons, Thierry Mora, Gašper Tkačik, and Michael Berry. “High Accuracy Decoding of Dynamical Motion from a Large Retinal Population.” <i>PLoS Computational Biology</i>. Public Library of Science, 2015. <a href=\"https://doi.org/10.1371/journal.pcbi.1004304\">https://doi.org/10.1371/journal.pcbi.1004304</a>.","ieee":"O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkačik, and M. Berry, “High accuracy decoding of dynamical motion from a large retinal population,” <i>PLoS Computational Biology</i>, vol. 11, no. 7. Public Library of Science, 2015.","apa":"Marre, O., Botella Soler, V., Simmons, K., Mora, T., Tkačik, G., &#38; Berry, M. (2015). High accuracy decoding of dynamical motion from a large retinal population. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1004304\">https://doi.org/10.1371/journal.pcbi.1004304</a>","ama":"Marre O, Botella Soler V, Simmons K, Mora T, Tkačik G, Berry M. High accuracy decoding of dynamical motion from a large retinal population. <i>PLoS Computational Biology</i>. 2015;11(7). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1004304\">10.1371/journal.pcbi.1004304</a>"},"year":"2015","acknowledgement":"This work was supported by grants EY 014196 and EY 017934 to MJB, ANR OPTIMA, the French State program Investissements d’Avenir managed by the Agence Nationale de la Recherche [LIFESENSES: ANR-10-LABX-65], and by a EC grant from the Human Brain Project (CLAP) to OM, the Austrian Research Foundation FWF P25651 to VBS and GT. VBS is partially supported by contracts MEC, Spain (Grant No. AYA2010- 22111-C03-02, Grant No. AYA2013-48623-C2-2 and FEDER Funds).","volume":11,"ddc":["570"],"oa_version":"Published Version","project":[{"grant_number":"P 25651-N26","name":"Sensitivity to higher-order statistics in natural scenes","_id":"254D1A94-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"}],"month":"07","article_number":"e1004304","publication":"PLoS Computational Biology","has_accepted_license":"1","language":[{"iso":"eng"}],"oa":1,"publist_id":"5447","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"date_published":"2015-07-01T00:00:00Z","type":"journal_article","file":[{"relation":"main_file","access_level":"open_access","creator":"system","file_id":"5212","file_size":4673930,"checksum":"472b979f3f1cffb37b3e503f085115ca","date_created":"2018-12-12T10:16:25Z","file_name":"IST-2016-455-v1+1_journal.pcbi.1004304.pdf","content_type":"application/pdf","date_updated":"2020-07-14T12:45:12Z"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public"},{"acknowledgement":"Research was supported in part by National Science Foundation Grants PHY-1305525, PHY-1451171, and CCF-0939370, by National Institutes of Health Grant R01 EY14196, and by Austrian Science Foundation Grant FWF P25651. Additional support was provided by the\r\nFannie and John Hertz Foundation, by the Swartz Foundation, by the W. M. Keck Foundation, and by the Simons Foundation.","volume":112,"external_id":{"pmid":["26330611"]},"citation":{"mla":"Tkačik, Gašper, et al. “Thermodynamics and Signatures of Criticality in a Network of Neurons.” <i>PNAS</i>, vol. 112, no. 37, National Academy of Sciences, 2015, pp. 11508–13, doi:<a href=\"https://doi.org/10.1073/pnas.1514188112\">10.1073/pnas.1514188112</a>.","short":"G. Tkačik, T. Mora, O. Marre, D. Amodei, S. Palmer, M. Berry Ii, W. Bialek, PNAS 112 (2015) 11508–11513.","ista":"Tkačik G, Mora T, Marre O, Amodei D, Palmer S, Berry Ii M, Bialek W. 2015. Thermodynamics and signatures of criticality in a network of neurons. PNAS. 112(37), 11508–11513.","ama":"Tkačik G, Mora T, Marre O, et al. Thermodynamics and signatures of criticality in a network of neurons. <i>PNAS</i>. 2015;112(37):11508-11513. doi:<a href=\"https://doi.org/10.1073/pnas.1514188112\">10.1073/pnas.1514188112</a>","apa":"Tkačik, G., Mora, T., Marre, O., Amodei, D., Palmer, S., Berry Ii, M., &#38; Bialek, W. (2015). Thermodynamics and signatures of criticality in a network of neurons. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1514188112\">https://doi.org/10.1073/pnas.1514188112</a>","chicago":"Tkačik, Gašper, Thierry Mora, Olivier Marre, Dario Amodei, Stephanie Palmer, Michael Berry Ii, and William Bialek. “Thermodynamics and Signatures of Criticality in a Network of Neurons.” <i>PNAS</i>. National Academy of Sciences, 2015. <a href=\"https://doi.org/10.1073/pnas.1514188112\">https://doi.org/10.1073/pnas.1514188112</a>.","ieee":"G. Tkačik <i>et al.</i>, “Thermodynamics and signatures of criticality in a network of neurons,” <i>PNAS</i>, vol. 112, no. 37. National Academy of Sciences, pp. 11508–11513, 2015."},"year":"2015","date_updated":"2021-01-12T06:52:37Z","abstract":[{"lang":"eng","text":"The activity of a neural network is defined by patterns of spiking and silence from the individual neurons. Because spikes are (relatively) sparse, patterns of activity with increasing numbers of spikes are less probable, but, with more spikes, the number of possible patterns increases. This tradeoff between probability and numerosity is mathematically equivalent to the relationship between entropy and energy in statistical physics. We construct this relationship for populations of up to N = 160 neurons in a small patch of the vertebrate retina, using a combination of direct and model-based analyses of experiments on the response of this network to naturalistic movies. We see signs of a thermodynamic limit, where the entropy per neuron approaches a smooth function of the energy per neuron as N increases. The form of this function corresponds to the distribution of activity being poised near an unusual kind of critical point. We suggest further tests of criticality, and give a brief discussion of its functional significance. "}],"day":"15","doi":"10.1073/pnas.1514188112","quality_controlled":"1","page":"11508 - 11513","publisher":"National Academy of Sciences","issue":"37","author":[{"orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","first_name":"Gasper","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Mora, Thierry","first_name":"Thierry","last_name":"Mora"},{"full_name":"Marre, Olivier","first_name":"Olivier","last_name":"Marre"},{"full_name":"Amodei, Dario","last_name":"Amodei","first_name":"Dario"},{"full_name":"Palmer, Stephanie","last_name":"Palmer","first_name":"Stephanie"},{"full_name":"Berry Ii, Michael","first_name":"Michael","last_name":"Berry Ii"},{"last_name":"Bialek","first_name":"William","full_name":"Bialek, William"}],"scopus_import":1,"_id":"1701","pmid":1,"intvolume":"       112","title":"Thermodynamics and signatures of criticality in a network of neurons","date_created":"2018-12-11T11:53:33Z","department":[{"_id":"GaTk"}],"publication_status":"published","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4577210/","open_access":"1"}],"type":"journal_article","date_published":"2015-09-15T00:00:00Z","publist_id":"5440","oa":1,"language":[{"iso":"eng"}],"publication":"PNAS","month":"09","project":[{"call_identifier":"FWF","_id":"254D1A94-B435-11E9-9278-68D0E5697425","name":"Sensitivity to higher-order statistics in natural scenes","grant_number":"P 25651-N26"}],"oa_version":"Submitted Version"},{"ddc":["570"],"abstract":[{"lang":"eng","text":"Information processing in the sensory periphery is shaped by natural stimulus statistics. In the periphery, a transmission bottleneck constrains performance; thus efficient coding implies that natural signal components with a predictably wider range should be compressed. In a different regime—when sampling limitations constrain performance—efficient coding implies that more resources should be allocated to informative features that are more variable. We propose that this regime is relevant for sensory cortex when it extracts complex features from limited numbers of sensory samples. To test this prediction, we use central visual processing as a model: we show that visual sensitivity for local multi-point spatial correlations, described by dozens of independently-measured parameters, can be quantitatively predicted from the structure of natural images. This suggests that efficient coding applies centrally, where it extends to higher-order sensory features and operates in a regime in which sensitivity increases with feature variability."}],"doi":"10.7554/eLife.03722","day":"14","date_updated":"2021-01-12T06:53:50Z","year":"2014","citation":{"ista":"Hermundstad A, Briguglio J, Conte M, Victor J, Balasubramanian V, Tkačik G. 2014. Variance predicts salience in central sensory processing. eLife. (November), e03722.","short":"A. Hermundstad, J. Briguglio, M. Conte, J. Victor, V. Balasubramanian, G. Tkačik, ELife (2014).","mla":"Hermundstad, Ann, et al. “Variance Predicts Salience in Central Sensory Processing.” <i>ELife</i>, no. November, e03722, eLife Sciences Publications, 2014, doi:<a href=\"https://doi.org/10.7554/eLife.03722\">10.7554/eLife.03722</a>.","ieee":"A. Hermundstad, J. Briguglio, M. Conte, J. Victor, V. Balasubramanian, and G. Tkačik, “Variance predicts salience in central sensory processing,” <i>eLife</i>, no. November. eLife Sciences Publications, 2014.","chicago":"Hermundstad, Ann, John Briguglio, Mary Conte, Jonathan Victor, Vijay Balasubramanian, and Gašper Tkačik. “Variance Predicts Salience in Central Sensory Processing.” <i>ELife</i>. eLife Sciences Publications, 2014. <a href=\"https://doi.org/10.7554/eLife.03722\">https://doi.org/10.7554/eLife.03722</a>.","ama":"Hermundstad A, Briguglio J, Conte M, Victor J, Balasubramanian V, Tkačik G. Variance predicts salience in central sensory processing. <i>eLife</i>. 2014;(November). doi:<a href=\"https://doi.org/10.7554/eLife.03722\">10.7554/eLife.03722</a>","apa":"Hermundstad, A., Briguglio, J., Conte, M., Victor, J., Balasubramanian, V., &#38; Tkačik, G. (2014). 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