---
_id: '730'
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.
article_processing_charge: No
author:
- first_name: Cristina
  full_name: Savin, Cristina
  id: 3933349E-F248-11E8-B48F-1D18A9856A87
  last_name: Savin
- first_name: Gasper
  full_name: Tkacik, Gasper
  id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
  last_name: Tkacik
  orcid: 0000-0002-6699-1455
citation:
  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>
  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>
  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>.
  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.
  ista: Savin C, Tkačik G. 2017. Maximum entropy models as a tool for building precise
    neural controls. Current Opinion in Neurobiology. 46, 120–126.
  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>.
  short: C. Savin, G. Tkačik, Current Opinion in Neurobiology 46 (2017) 120–126.
date_created: 2018-12-11T11:48:11Z
date_published: 2017-10-01T00:00:00Z
date_updated: 2023-09-28T11:32:22Z
day: '01'
department:
- _id: GaTk
doi: 10.1016/j.conb.2017.08.001
ec_funded: 1
external_id:
  isi:
  - '000416196400016'
intvolume: '        46'
isi: 1
language:
- iso: eng
month: '10'
oa_version: None
page: 120 - 126
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
  call_identifier: FP7
  grant_number: '291734'
  name: International IST Postdoc Fellowship Programme
publication: Current Opinion in Neurobiology
publication_identifier:
  issn:
  - '09594388'
publication_status: published
publisher: Elsevier
publist_id: '6943'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Maximum entropy models as a tool for building precise neural controls
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 46
year: '2017'
...