---
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/0959-4388
  dct_language: eng
  dct_publisher: Elsevier@
  dct_title: Maximum entropy models as a tool for building precise neural controls@
...