---
_id: '7369'
abstract:
- lang: eng
  text: 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.
acknowledgement: This research was supported by the Kavli Foundation and the Centre
  of Excellence scheme of the Research Council of Norway (Centre for Neural Computation).
  RJC is currently receiving funding from the European Union’s Horizon 2020 research
  and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 754411.
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Ryan J
  full_name: Cubero, Ryan J
  id: 850B2E12-9CD4-11E9-837F-E719E6697425
  last_name: Cubero
  orcid: 0000-0003-0002-1867
- first_name: Matteo
  full_name: Marsili, Matteo
  last_name: Marsili
- first_name: Yasser
  full_name: Roudi, Yasser
  last_name: Roudi
citation:
  ama: Cubero RJ, Marsili M, Roudi Y. Multiscale relevance and informative encoding
    in neuronal spike trains. <i>Journal of Computational Neuroscience</i>. 2020;48:85-102.
    doi:<a href="https://doi.org/10.1007/s10827-020-00740-x">10.1007/s10827-020-00740-x</a>
  apa: Cubero, R. J., Marsili, M., &#38; Roudi, Y. (2020). Multiscale relevance and
    informative encoding in neuronal spike trains. <i>Journal of Computational Neuroscience</i>.
    Springer Nature. <a href="https://doi.org/10.1007/s10827-020-00740-x">https://doi.org/10.1007/s10827-020-00740-x</a>
  chicago: Cubero, Ryan J, Matteo Marsili, and Yasser Roudi. “Multiscale Relevance
    and Informative Encoding in Neuronal Spike Trains.” <i>Journal of Computational
    Neuroscience</i>. Springer Nature, 2020. <a href="https://doi.org/10.1007/s10827-020-00740-x">https://doi.org/10.1007/s10827-020-00740-x</a>.
  ieee: R. J. Cubero, M. Marsili, and Y. Roudi, “Multiscale relevance and informative
    encoding in neuronal spike trains,” <i>Journal of Computational Neuroscience</i>,
    vol. 48. Springer Nature, pp. 85–102, 2020.
  ista: Cubero RJ, Marsili M, Roudi Y. 2020. Multiscale relevance and informative
    encoding in neuronal spike trains. Journal of Computational Neuroscience. 48,
    85–102.
  mla: Cubero, Ryan J., et al. “Multiscale Relevance and Informative Encoding in Neuronal
    Spike Trains.” <i>Journal of Computational Neuroscience</i>, vol. 48, Springer
    Nature, 2020, pp. 85–102, doi:<a href="https://doi.org/10.1007/s10827-020-00740-x">10.1007/s10827-020-00740-x</a>.
  short: R.J. Cubero, M. Marsili, Y. Roudi, Journal of Computational Neuroscience
    48 (2020) 85–102.
date_created: 2020-01-28T10:34:00Z
date_published: 2020-02-01T00:00:00Z
date_updated: 2023-08-17T14:35:22Z
day: '01'
ddc:
- '004'
- '519'
- '570'
department:
- _id: SaSi
doi: 10.1007/s10827-020-00740-x
ec_funded: 1
external_id:
  isi:
  - '000515321800006'
file:
- access_level: open_access
  checksum: 036e9451d6cd0c190ad25791bf82393b
  content_type: application/pdf
  creator: rcubero
  date_created: 2020-01-28T09:31:09Z
  date_updated: 2020-07-14T12:47:56Z
  file_id: '7380'
  file_name: 10827_2020_740_MOESM1_ESM.pdf
  file_size: 1941355
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  content_type: application/pdf
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  relation: main_file
file_date_updated: 2020-07-14T12:47:56Z
has_accepted_license: '1'
intvolume: '        48'
isi: 1
keyword:
- Time series analysis
- Multiple time scale analysis
- Spike train data
- Information theory
- Bayesian decoding
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '02'
oa: 1
oa_version: Published Version
page: 85-102
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
  call_identifier: H2020
  grant_number: '754411'
  name: ISTplus - Postdoctoral Fellowships
publication: Journal of Computational Neuroscience
publication_identifier:
  eissn:
  - 1573-6873
  issn:
  - 0929-5313
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Multiscale relevance and informative encoding in neuronal spike trains
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 48
year: '2020'
...