---
res:
  bibo_abstract:
  - Neuronal responses to complex stimuli and tasks can encompass a wide range of
    time scales. Understanding these responses requires measures that characterize
    how the information on these response patterns are represented across multiple
    temporal resolutions. In this paper we propose a metric – which we call multiscale
    relevance (MSR) – to capture the dynamical variability of the activity of single
    neurons across different time scales. The MSR is a non-parametric, fully featureless
    indicator in that it uses only the time stamps of the firing activity without
    resorting to any a priori covariate or invoking any specific structure in the
    tuning curve for neural activity. When applied to neural data from the mEC and
    from the ADn and PoS regions of freely-behaving rodents, we found that neurons
    having low MSR tend to have low mutual information and low firing sparsity across
    the correlates that are believed to be encoded by the region of the brain where
    the recordings were made. In addition, neurons with high MSR contain significant
    information on spatial navigation and allow to decode spatial position or head
    direction as efficiently as those neurons whose firing activity has high mutual
    information with the covariate to be decoded and significantly better than the
    set of neurons with high local variations in their interspike intervals. Given
    these results, we propose that the MSR can be used as a measure to rank and select
    neurons for their information content without the need to appeal to any a priori
    covariate.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Ryan J
      foaf_name: Cubero, Ryan J
      foaf_surname: Cubero
      foaf_workInfoHomepage: http://www.librecat.org/personId=850B2E12-9CD4-11E9-837F-E719E6697425
    orcid: 0000-0003-0002-1867
  - foaf_Person:
      foaf_givenName: Matteo
      foaf_name: Marsili, Matteo
      foaf_surname: Marsili
  - foaf_Person:
      foaf_givenName: Yasser
      foaf_name: Roudi, Yasser
      foaf_surname: Roudi
  bibo_doi: 10.1007/s10827-020-00740-x
  bibo_volume: 48
  dct_date: 2020^xs_gYear
  dct_identifier:
  - UT:000515321800006
  dct_isPartOf:
  - http://id.crossref.org/issn/0929-5313
  - http://id.crossref.org/issn/1573-6873
  dct_language: eng
  dct_publisher: Springer Nature@
  dct_subject:
  - Time series analysis
  - Multiple time scale analysis
  - Spike train data
  - Information theory
  - Bayesian decoding
  dct_title: Multiscale relevance and informative encoding in neuronal spike trains@
...