{"external_id":{"isi":["000416196400016"]},"author":[{"full_name":"Savin, Cristina","first_name":"Cristina","last_name":"Savin","id":"3933349E-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper"}],"date_updated":"2023-09-28T11:32:22Z","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","year":"2017","_id":"730","doi":"10.1016/j.conb.2017.08.001","quality_controlled":"1","isi":1,"publication_status":"published","volume":46,"scopus_import":"1","month":"10","publication":"Current Opinion in Neurobiology","citation":{"short":"C. Savin, G. Tkačik, Current Opinion in Neurobiology 46 (2017) 120–126.","ieee":"C. Savin and G. Tkačik, “Maximum entropy models as a tool for building precise neural controls,” <i>Current Opinion in Neurobiology</i>, vol. 46. Elsevier, pp. 120–126, 2017.","ama":"Savin C, Tkačik G. Maximum entropy models as a tool for building precise neural controls. <i>Current Opinion in Neurobiology</i>. 2017;46:120-126. doi:<a href=\"https://doi.org/10.1016/j.conb.2017.08.001\">10.1016/j.conb.2017.08.001</a>","chicago":"Savin, Cristina, and Gašper Tkačik. “Maximum Entropy Models as a Tool for Building Precise Neural Controls.” <i>Current Opinion in Neurobiology</i>. Elsevier, 2017. <a href=\"https://doi.org/10.1016/j.conb.2017.08.001\">https://doi.org/10.1016/j.conb.2017.08.001</a>.","apa":"Savin, C., &#38; Tkačik, G. (2017). Maximum entropy models as a tool for building precise neural controls. <i>Current Opinion in Neurobiology</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.conb.2017.08.001\">https://doi.org/10.1016/j.conb.2017.08.001</a>","mla":"Savin, Cristina, and Gašper Tkačik. “Maximum Entropy Models as a Tool for Building Precise Neural Controls.” <i>Current Opinion in Neurobiology</i>, vol. 46, Elsevier, 2017, pp. 120–26, doi:<a href=\"https://doi.org/10.1016/j.conb.2017.08.001\">10.1016/j.conb.2017.08.001</a>.","ista":"Savin C, Tkačik G. 2017. Maximum entropy models as a tool for building precise neural controls. Current Opinion in Neurobiology. 46, 120–126."},"status":"public","publication_identifier":{"issn":["09594388"]},"date_created":"2018-12-11T11:48:11Z","language":[{"iso":"eng"}],"article_processing_charge":"No","title":"Maximum entropy models as a tool for building precise neural controls","page":"120 - 126","day":"01","project":[{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"intvolume":"        46","type":"journal_article","publist_id":"6943","publisher":"Elsevier","department":[{"_id":"GaTk"}],"ec_funded":1,"abstract":[{"lang":"eng","text":"Neural responses are highly structured, with population activity restricted to a small subset of the astronomical range of possible activity patterns. Characterizing these statistical regularities is important for understanding circuit computation, but challenging in practice. Here we review recent approaches based on the maximum entropy principle used for quantifying collective behavior in neural activity. We highlight recent models that capture population-level statistics of neural data, yielding insights into the organization of the neural code and its biological substrate. Furthermore, the MaxEnt framework provides a general recipe for constructing surrogate ensembles that preserve aspects of the data, but are otherwise maximally unstructured. This idea can be used to generate a hierarchy of controls against which rigorous statistical tests are possible."}],"date_published":"2017-10-01T00:00:00Z","oa_version":"None"}