---
_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. Journal of Computational Neuroscience. 2020;48:85-102.
doi:10.1007/s10827-020-00740-x
apa: Cubero, R. J., Marsili, M., & Roudi, Y. (2020). Multiscale relevance and
informative encoding in neuronal spike trains. Journal of Computational Neuroscience.
Springer Nature. https://doi.org/10.1007/s10827-020-00740-x
chicago: Cubero, Ryan J, Matteo Marsili, and Yasser Roudi. “Multiscale Relevance
and Informative Encoding in Neuronal Spike Trains.” Journal of Computational
Neuroscience. Springer Nature, 2020. https://doi.org/10.1007/s10827-020-00740-x.
ieee: R. J. Cubero, M. Marsili, and Y. Roudi, “Multiscale relevance and informative
encoding in neuronal spike trains,” Journal of Computational Neuroscience,
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.” Journal of Computational Neuroscience, vol. 48, Springer
Nature, 2020, pp. 85–102, doi:10.1007/s10827-020-00740-x.
short: R.J. Cubero, M. Marsili, Y. Roudi, Journal of Computational Neuroscience
48 (2020) 85–102.
corr_author: '1'
date_created: 2020-01-28T10:34:00Z
date_published: 2020-02-01T00:00:00Z
date_updated: 2025-04-14T07:44:02Z
day: '01'
ddc:
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department:
- _id: SaSi
doi: 10.1007/s10827-020-00740-x
ec_funded: 1
external_id:
isi:
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intvolume: ' 48'
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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:
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publisher: Springer Nature
quality_controlled: '1'
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title: Multiscale relevance and informative encoding in neuronal spike trains
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...
---
_id: '5584'
abstract:
- lang: eng
text: "This package contains data for the publication \"Nonlinear decoding of a
complex movie from the mammalian retina\" by Deny S. et al, PLOS Comput Biol (2018).
\r\n\r\nThe data consists of\r\n(i) 91 spike sorted, isolated rat retinal ganglion
cells that pass stability and quality criteria, recorded on the multi-electrode
array, in response to the presentation of the complex movie with many randomly
moving dark discs. The responses are represented as 648000 x 91 binary matrix,
where the first index indicates the timebin of duration 12.5 ms, and the second
index the neural identity. The matrix entry is 0/1 if the neuron didn't/did spike
in the particular time bin.\r\n(ii) README file and a graphical illustration of
the structure of the experiment, specifying how the 648000 timebins are split
into epochs where 1, 2, 4, or 10 discs were displayed, and which stimulus segments
are exact repeats or unique ball trajectories.\r\n(iii) a 648000 x 400 matrix
of luminance traces for each of the 20 x 20 positions (\"sites\") in the movie
frame, with time that is locked to the recorded raster. The luminance traces are
produced as described in the manuscript by filtering the raw disc movie with a
small gaussian spatial kernel. "
article_processing_charge: No
author:
- first_name: Stephane
full_name: Deny, Stephane
last_name: Deny
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Vicente
full_name: Botella-Soler, Vicente
last_name: Botella-Soler
- first_name: Georg S
full_name: Martius, Georg S
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last_name: Martius
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Deny S, Marre O, Botella-Soler V, Martius GS, Tkačik G. Nonlinear decoding
of a complex movie from the mammalian retina. 2018. doi:10.15479/AT:ISTA:98
apa: Deny, S., Marre, O., Botella-Soler, V., Martius, G. S., & Tkačik, G. (2018).
Nonlinear decoding of a complex movie from the mammalian retina. Institute of
Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:98
chicago: Deny, Stephane, Olivier Marre, Vicente Botella-Soler, Georg S Martius,
and Gašper Tkačik. “Nonlinear Decoding of a Complex Movie from the Mammalian Retina.”
Institute of Science and Technology Austria, 2018. https://doi.org/10.15479/AT:ISTA:98.
ieee: S. Deny, O. Marre, V. Botella-Soler, G. S. Martius, and G. Tkačik, “Nonlinear
decoding of a complex movie from the mammalian retina.” Institute of Science and
Technology Austria, 2018.
ista: Deny S, Marre O, Botella-Soler V, Martius GS, Tkačik G. 2018. Nonlinear decoding
of a complex movie from the mammalian retina, Institute of Science and Technology
Austria, 10.15479/AT:ISTA:98.
mla: Deny, Stephane, et al. Nonlinear Decoding of a Complex Movie from the Mammalian
Retina. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:98.
short: S. Deny, O. Marre, V. Botella-Soler, G.S. Martius, G. Tkačik, (2018).
datarep_id: '98'
date_created: 2018-12-12T12:31:39Z
date_published: 2018-03-29T00:00:00Z
date_updated: 2025-04-15T08:18:24Z
day: '29'
ddc:
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department:
- _id: ChLa
- _id: GaTk
doi: 10.15479/AT:ISTA:98
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creator: system
date_created: 2018-12-12T13:02:24Z
date_updated: 2020-07-14T12:47:07Z
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date_updated: 2020-07-14T12:47:07Z
file_id: '5591'
file_name: IST-2018-98-v1+2_ExperimentStructure.pdf
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date_created: 2018-12-12T13:02:26Z
date_updated: 2020-07-14T12:47:07Z
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file_name: IST-2018-98-v1+3_GoodLocations_area2_20x20.mat
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date_updated: 2020-07-14T12:47:07Z
file_id: '5593'
file_name: IST-2018-98-v1+4_README.txt
file_size: 986
relation: main_file
file_date_updated: 2020-07-14T12:47:07Z
has_accepted_license: '1'
keyword:
- retina
- decoding
- regression
- neural networks
- complex stimulus
license: https://creativecommons.org/publicdomain/zero/1.0/
month: '03'
oa: 1
oa_version: Published Version
project:
- _id: 254D1A94-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P 25651-N26
name: Sensitivity to higher-order statistics in natural scenes
publisher: Institute of Science and Technology Austria
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title: Nonlinear decoding of a complex movie from the mammalian retina
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type: research_data
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...