{"month":"02","ec_funded":1,"year":"2020","publication_identifier":{"eissn":["1573-6873"],"issn":["0929-5313"]},"title":"Multiscale relevance and informative encoding in neuronal spike trains","keyword":["Time series analysis","Multiple time scale analysis","Spike train data","Information theory","Bayesian decoding"],"quality_controlled":"1","project":[{"call_identifier":"H2020","name":"ISTplus - Postdoctoral Fellowships","_id":"260C2330-B435-11E9-9278-68D0E5697425","grant_number":"754411"}],"intvolume":"        48","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"citation":{"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>.","ista":"Cubero RJ, Marsili M, Roudi Y. 2020. Multiscale relevance and informative encoding in neuronal spike trains. Journal of Computational Neuroscience. 48, 85–102.","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>","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.","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>","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."},"date_published":"2020-02-01T00:00:00Z","has_accepted_license":"1","page":"85-102","doi":"10.1007/s10827-020-00740-x","article_type":"original","type":"journal_article","ddc":["004","519","570"],"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.","status":"public","external_id":{"isi":["000515321800006"]},"author":[{"last_name":"Cubero","first_name":"Ryan J","orcid":"0000-0003-0002-1867","id":"850B2E12-9CD4-11E9-837F-E719E6697425","full_name":"Cubero, Ryan J"},{"full_name":"Marsili, Matteo","last_name":"Marsili","first_name":"Matteo"},{"full_name":"Roudi, Yasser","last_name":"Roudi","first_name":"Yasser"}],"oa_version":"Published Version","date_updated":"2023-08-17T14:35:22Z","isi":1,"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","article_processing_charge":"Yes (via OA deal)","day":"01","publisher":"Springer Nature","department":[{"_id":"SaSi"}],"file":[{"relation":"supplementary_material","file_size":1941355,"date_created":"2020-01-28T09:31:09Z","checksum":"036e9451d6cd0c190ad25791bf82393b","access_level":"open_access","creator":"rcubero","file_id":"7380","file_name":"10827_2020_740_MOESM1_ESM.pdf","date_updated":"2020-07-14T12:47:56Z","content_type":"application/pdf"},{"file_size":3257880,"relation":"main_file","date_updated":"2020-07-14T12:47:56Z","content_type":"application/pdf","file_id":"7381","creator":"rcubero","file_name":"Cubero2020_Article_MultiscaleRelevanceAndInformat.pdf","checksum":"4dd8b1fd4b54486f79d82ac7b2a412b2","access_level":"open_access","date_created":"2020-01-28T09:31:09Z"}],"_id":"7369","oa":1,"scopus_import":"1","publication_status":"published","publication":"Journal of Computational Neuroscience","language":[{"iso":"eng"}],"date_created":"2020-01-28T10:34:00Z","file_date_updated":"2020-07-14T12:47:56Z","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."}],"volume":48}