{"date_published":"2015-07-01T00:00:00Z","citation":{"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>","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.","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>.","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>","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.","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)."},"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"intvolume":"        11","project":[{"grant_number":"P 25651-N26","_id":"254D1A94-B435-11E9-9278-68D0E5697425","name":"Sensitivity to higher-order statistics in natural scenes","call_identifier":"FWF"}],"quality_controlled":"1","title":"High accuracy decoding of dynamical motion from a large retinal population","year":"2015","pubrep_id":"455","month":"07","date_updated":"2021-01-12T06:52:35Z","publist_id":"5447","oa_version":"Published Version","author":[{"first_name":"Olivier","last_name":"Marre","full_name":"Marre, Olivier"},{"full_name":"Botella Soler, Vicente","id":"421234E8-F248-11E8-B48F-1D18A9856A87","first_name":"Vicente","orcid":"0000-0002-8790-1914","last_name":"Botella Soler"},{"last_name":"Simmons","first_name":"Kristina","full_name":"Simmons, Kristina"},{"full_name":"Mora, Thierry","first_name":"Thierry","last_name":"Mora"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper","last_name":"Tkacik","first_name":"Gasper","orcid":"0000-0002-6699-1455"},{"first_name":"Michael","last_name":"Berry","full_name":"Berry, Michael"}],"status":"public","license":"https://creativecommons.org/licenses/by/4.0/","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).","article_number":"e1004304","ddc":["570"],"issue":"7","has_accepted_license":"1","doi":"10.1371/journal.pcbi.1004304","type":"journal_article","file":[{"relation":"main_file","file_size":4673930,"access_level":"open_access","checksum":"472b979f3f1cffb37b3e503f085115ca","date_created":"2018-12-12T10:16:25Z","content_type":"application/pdf","date_updated":"2020-07-14T12:45:12Z","creator":"system","file_id":"5212","file_name":"IST-2016-455-v1+1_journal.pcbi.1004304.pdf"}],"department":[{"_id":"GaTk"}],"publisher":"Public Library of Science","day":"01","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"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.","lang":"eng"}],"volume":11,"file_date_updated":"2020-07-14T12:45:12Z","date_created":"2018-12-11T11:53:31Z","publication":"PLoS Computational Biology","language":[{"iso":"eng"}],"publication_status":"published","_id":"1697","scopus_import":1,"oa":1}