--- _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. 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 relation: supplementary_material - access_level: open_access checksum: 4dd8b1fd4b54486f79d82ac7b2a412b2 content_type: application/pdf creator: rcubero date_created: 2020-01-28T09:31:09Z date_updated: 2020-07-14T12:47:56Z file_id: '7381' file_name: Cubero2020_Article_MultiscaleRelevanceAndInformat.pdf file_size: 3257880 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' ... --- _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 id: 3A276B68-F248-11E8-B48F-1D18A9856A87 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: 2024-02-21T13:45:26Z day: '29' ddc: - '570' department: - _id: ChLa - _id: GaTk doi: 10.15479/AT:ISTA:98 file: - access_level: open_access checksum: 6808748837b9afbbbabc2a356ca2b88a content_type: application/octet-stream creator: system date_created: 2018-12-12T13:02:24Z date_updated: 2020-07-14T12:47:07Z file_id: '5590' file_name: IST-2018-98-v1+1_BBalls_area2_tile2_20x20.mat file_size: 1142543971 relation: main_file - access_level: open_access checksum: d6d6cd07743038fe3a12352983fcf9dd content_type: application/pdf creator: system date_created: 2018-12-12T13:02:25Z date_updated: 2020-07-14T12:47:07Z file_id: '5591' file_name: IST-2018-98-v1+2_ExperimentStructure.pdf file_size: 702336 relation: main_file - access_level: open_access checksum: 0c9cfb4dab35bb3dc25a04395600b1c8 content_type: application/octet-stream creator: system date_created: 2018-12-12T13:02:26Z date_updated: 2020-07-14T12:47:07Z file_id: '5592' file_name: IST-2018-98-v1+3_GoodLocations_area2_20x20.mat file_size: 432 relation: main_file - access_level: open_access checksum: 2a83b011012e21e934b4596285b1a183 content_type: text/plain creator: system date_created: 2018-12-12T13:02:26Z 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 related_material: record: - id: '292' relation: used_in_publication status: public status: public title: Nonlinear decoding of a complex movie from the mammalian retina tmp: image: /images/cc_0.png legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode name: Creative Commons Public Domain Dedication (CC0 1.0) short: CC0 (1.0) type: research_data user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2018' ...