---
res:
  bibo_abstract:
  - The number of possible activity patterns in a population of neurons grows exponentially
    with the size of the population. Typical experiments explore only a tiny fraction
    of the large space of possible activity patterns in the case of populations with
    more than 10 or 20 neurons. It is thus impossible, in this undersampled regime,
    to estimate the probabilities with which most of the activity patterns occur.
    As a result, the corresponding entropy - which is a measure of the computational
    power of the neural population - cannot be estimated directly. We propose a simple
    scheme for estimating the entropy in the undersampled regime, which bounds its
    value from both below and above. The lower bound is the usual 'naive' entropy
    of the experimental frequencies. The upper bound results from a hybrid approximation
    of the entropy which makes use of the naive estimate, a maximum entropy fit, and
    a coverage adjustment. We apply our simple scheme to artificial data, in order
    to check their accuracy; we also compare its performance to those of several previously
    defined entropy estimators. We then apply it to actual measurements of neural
    activity in populations with up to 100 cells. Finally, we discuss the similarities
    and differences between the proposed simple estimation scheme and various earlier
    methods. © 2013 IOP Publishing Ltd and SISSA Medialab srl.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Michael
      foaf_name: Berry, Michael
      foaf_surname: Berry
  - 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
  - foaf_Person:
      foaf_givenName: Julien
      foaf_name: Dubuis, Julien
      foaf_surname: Dubuis
  - foaf_Person:
      foaf_givenName: Olivier
      foaf_name: Marre, Olivier
      foaf_surname: Marre
  - foaf_Person:
      foaf_givenName: Ravá
      foaf_name: Da Silveira, Ravá
      foaf_surname: Da Silveira
  bibo_doi: 10.1088/1742-5468/2013/03/P03015
  bibo_issue: '3'
  bibo_volume: 2013
  dct_date: 2013^xs_gYear
  dct_identifier:
  - UT:000316056900015
  dct_language: eng
  dct_publisher: IOP Publishing@
  dct_title: A simple method for estimating the entropy of neural activity@
...