{"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","has_accepted_license":"1","article_processing_charge":"No","article_type":"original","oa_version":"Published Version","date_created":"2020-04-12T22:00:40Z","file_date_updated":"2020-07-14T12:48:01Z","date_published":"2020-03-13T00:00:00Z","month":"03","type":"journal_article","publication_identifier":{"eissn":["16625188"]},"ddc":["570"],"title":"Clustering of neural activity: A design principle for population codes","status":"public","department":[{"_id":"GaTk"}],"author":[{"first_name":"Michael J.","last_name":"Berry","full_name":"Berry, Michael J."},{"first_name":"Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","full_name":"Tkačik, Gašper","last_name":"Tkačik"}],"intvolume":"        14","citation":{"chicago":"Berry, Michael J., and Gašper Tkačik. “Clustering of Neural Activity: A Design Principle for Population Codes.” <i>Frontiers in Computational Neuroscience</i>. Frontiers, 2020. <a href=\"https://doi.org/10.3389/fncom.2020.00020\">https://doi.org/10.3389/fncom.2020.00020</a>.","ieee":"M. J. Berry and G. Tkačik, “Clustering of neural activity: A design principle for population codes,” <i>Frontiers in Computational Neuroscience</i>, vol. 14. Frontiers, 2020.","mla":"Berry, Michael J., and Gašper Tkačik. “Clustering of Neural Activity: A Design Principle for Population Codes.” <i>Frontiers in Computational Neuroscience</i>, vol. 14, 20, Frontiers, 2020, doi:<a href=\"https://doi.org/10.3389/fncom.2020.00020\">10.3389/fncom.2020.00020</a>.","apa":"Berry, M. J., &#38; Tkačik, G. (2020). Clustering of neural activity: A design principle for population codes. <i>Frontiers in Computational Neuroscience</i>. Frontiers. <a href=\"https://doi.org/10.3389/fncom.2020.00020\">https://doi.org/10.3389/fncom.2020.00020</a>","ista":"Berry MJ, Tkačik G. 2020. Clustering of neural activity: A design principle for population codes. Frontiers in Computational Neuroscience. 14, 20.","short":"M.J. Berry, G. Tkačik, Frontiers in Computational Neuroscience 14 (2020).","ama":"Berry MJ, Tkačik G. Clustering of neural activity: A design principle for population codes. <i>Frontiers in Computational Neuroscience</i>. 2020;14. doi:<a href=\"https://doi.org/10.3389/fncom.2020.00020\">10.3389/fncom.2020.00020</a>"},"isi":1,"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)"},"abstract":[{"lang":"eng","text":"We propose that correlations among neurons are generically strong enough to organize neural activity patterns into a discrete set of clusters, which can each be viewed as a population codeword. Our reasoning starts with the analysis of retinal ganglion cell data using maximum entropy models, showing that the population is robustly in a frustrated, marginally sub-critical, or glassy, state. This leads to an argument that neural populations in many other brain areas might share this structure. Next, we use latent variable models to show that this glassy state possesses well-defined clusters of neural activity. Clusters have three appealing properties: (i) clusters exhibit error correction, i.e., they are reproducibly elicited by the same stimulus despite variability at the level of constituent neurons; (ii) clusters encode qualitatively different visual features than their constituent neurons; and (iii) clusters can be learned by downstream neural circuits in an unsupervised fashion. We hypothesize that these properties give rise to a “learnable” neural code which the cortical hierarchy uses to extract increasingly complex features without supervision or reinforcement."}],"pmid":1,"scopus_import":"1","quality_controlled":"1","oa":1,"language":[{"iso":"eng"}],"publisher":"Frontiers","year":"2020","volume":14,"day":"13","external_id":{"pmid":["32231528"],"isi":["000525543200001"]},"publication_status":"published","license":"https://creativecommons.org/licenses/by/4.0/","file":[{"date_created":"2020-04-14T12:20:39Z","content_type":"application/pdf","file_size":4082937,"relation":"main_file","file_id":"7659","checksum":"2b1da23823eae9cedbb42d701945b61e","date_updated":"2020-07-14T12:48:01Z","access_level":"open_access","file_name":"2020_Frontiers_Berry.pdf","creator":"dernst"}],"doi":"10.3389/fncom.2020.00020","publication":"Frontiers in Computational Neuroscience","_id":"7656","article_number":"20","date_updated":"2023-08-18T10:30:11Z"}