--- res: bibo_abstract: - 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.@eng bibo_authorlist: - foaf_Person: foaf_givenName: Cristina foaf_name: Savin, Cristina foaf_surname: Savin foaf_workInfoHomepage: http://www.librecat.org/personId=3933349E-F248-11E8-B48F-1D18A9856A87 - foaf_Person: foaf_givenName: Gasper foaf_name: Tkacik, Gasper foaf_surname: Tkacik foaf_workInfoHomepage: http://www.librecat.org/personId=3D494DCA-F248-11E8-B48F-1D18A9856A87 orcid: 0000-0002-6699-1455 bibo_doi: 10.1016/j.conb.2017.08.001 bibo_volume: 46 dct_date: 2017^xs_gYear dct_identifier: - UT:000416196400016 dct_isPartOf: - http://id.crossref.org/issn/09594388 dct_language: eng dct_publisher: Elsevier@ dct_title: Maximum entropy models as a tool for building precise neural controls@ ...