[{"publication_identifier":{"eissn":["19326203"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2023-10-18T08:17:42Z","scopus_import":"1","external_id":{"isi":["000641474900072"],"pmid":["33857170"]},"has_accepted_license":"1","oa_version":"Published Version","year":"2021","article_type":"original","oa":1,"publication_status":"published","volume":16,"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)"},"file_date_updated":"2021-05-04T13:22:19Z","issue":"4","article_processing_charge":"No","_id":"9362","abstract":[{"text":"A central goal in systems neuroscience is to understand the functions performed by neural circuits. Previous top-down models addressed this question by comparing the behaviour of an ideal model circuit, optimised to perform a given function, with neural recordings. However, this requires guessing in advance what function is being performed, which may not be possible for many neural systems. To address this, we propose an inverse reinforcement learning (RL) framework for inferring the function performed by a neural network from data. We assume that the responses of each neuron in a network are optimised so as to drive the network towards ‘rewarded’ states, that are desirable for performing a given function. We then show how one can use inverse RL to infer the reward function optimised by the network from observing its responses. This inferred reward function can be used to predict how the neural network should adapt its dynamics to perform the same function when the external environment or network structure changes. This could lead to theoretical predictions about how neural network dynamics adapt to deal with cell death and/or varying sensory stimulus statistics.","lang":"eng"}],"date_published":"2021-04-15T00:00:00Z","file":[{"success":1,"date_created":"2021-05-04T13:22:19Z","relation":"main_file","file_id":"9371","content_type":"application/pdf","access_level":"open_access","date_updated":"2021-05-04T13:22:19Z","checksum":"c52da133850307d2031f552d998f00e8","file_size":2768282,"creator":"kschuh","file_name":"2021_pone_Chalk.pdf"}],"article_number":"e0248940","acknowledgement":"The authors would like to thank Ulisse Ferrari for useful discussions and feedback.","pmid":1,"doi":"10.1371/journal.pone.0248940","ddc":["570"],"language":[{"iso":"eng"}],"title":"Inferring the function performed by a recurrent neural network","citation":{"ista":"Chalk MJ, Tkačik G, Marre O. 2021. Inferring the function performed by a recurrent neural network. PLoS ONE. 16(4), e0248940.","ieee":"M. J. Chalk, G. Tkačik, and O. Marre, “Inferring the function performed by a recurrent neural network,” <i>PLoS ONE</i>, vol. 16, no. 4. Public Library of Science, 2021.","chicago":"Chalk, Matthew J, Gašper Tkačik, and Olivier Marre. “Inferring the Function Performed by a Recurrent Neural Network.” <i>PLoS ONE</i>. Public Library of Science, 2021. <a href=\"https://doi.org/10.1371/journal.pone.0248940\">https://doi.org/10.1371/journal.pone.0248940</a>.","mla":"Chalk, Matthew J., et al. “Inferring the Function Performed by a Recurrent Neural Network.” <i>PLoS ONE</i>, vol. 16, no. 4, e0248940, Public Library of Science, 2021, doi:<a href=\"https://doi.org/10.1371/journal.pone.0248940\">10.1371/journal.pone.0248940</a>.","short":"M.J. Chalk, G. Tkačik, O. Marre, PLoS ONE 16 (2021).","apa":"Chalk, M. J., Tkačik, G., &#38; Marre, O. (2021). Inferring the function performed by a recurrent neural network. <i>PLoS ONE</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pone.0248940\">https://doi.org/10.1371/journal.pone.0248940</a>","ama":"Chalk MJ, Tkačik G, Marre O. Inferring the function performed by a recurrent neural network. <i>PLoS ONE</i>. 2021;16(4). doi:<a href=\"https://doi.org/10.1371/journal.pone.0248940\">10.1371/journal.pone.0248940</a>"},"type":"journal_article","author":[{"orcid":"0000-0001-7782-4436","id":"2BAAC544-F248-11E8-B48F-1D18A9856A87","last_name":"Chalk","full_name":"Chalk, Matthew J","first_name":"Matthew J"},{"orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","full_name":"Tkačik, Gašper","last_name":"Tkačik"},{"first_name":"Olivier","full_name":"Marre, Olivier","last_name":"Marre"}],"day":"15","isi":1,"publisher":"Public Library of Science","intvolume":"        16","status":"public","department":[{"_id":"GaTk"}],"quality_controlled":"1","publication":"PLoS ONE","date_created":"2021-05-02T22:01:28Z","month":"04"},{"author":[{"last_name":"Graff","full_name":"Graff, Grzegorz","first_name":"Grzegorz"},{"last_name":"Graff","first_name":"Beata","full_name":"Graff, Beata"},{"last_name":"Pilarczyk","first_name":"Pawel","full_name":"Pilarczyk, Pawel","id":"3768D56A-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Jablonski","full_name":"Jablonski, Grzegorz","first_name":"Grzegorz","orcid":"0000-0002-3536-9866","id":"4483EF78-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Dariusz","full_name":"Gąsecki, Dariusz","last_name":"Gąsecki"},{"full_name":"Narkiewicz, Krzysztof","first_name":"Krzysztof","last_name":"Narkiewicz"}],"type":"journal_article","day":"01","title":"Persistent homology as a new method of the assessment of heart rate variability","citation":{"apa":"Graff, G., Graff, B., Pilarczyk, P., Jablonski, G., Gąsecki, D., &#38; Narkiewicz, K. (2021). Persistent homology as a new method of the assessment of heart rate variability. <i>PLoS ONE</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pone.0253851\">https://doi.org/10.1371/journal.pone.0253851</a>","ama":"Graff G, Graff B, Pilarczyk P, Jablonski G, Gąsecki D, Narkiewicz K. Persistent homology as a new method of the assessment of heart rate variability. <i>PLoS ONE</i>. 2021;16(7). doi:<a href=\"https://doi.org/10.1371/journal.pone.0253851\">10.1371/journal.pone.0253851</a>","short":"G. Graff, B. Graff, P. Pilarczyk, G. Jablonski, D. Gąsecki, K. Narkiewicz, PLoS ONE 16 (2021).","mla":"Graff, Grzegorz, et al. “Persistent Homology as a New Method of the Assessment of Heart Rate Variability.” <i>PLoS ONE</i>, vol. 16, no. 7, e0253851, Public Library of Science, 2021, doi:<a href=\"https://doi.org/10.1371/journal.pone.0253851\">10.1371/journal.pone.0253851</a>.","ieee":"G. Graff, B. Graff, P. Pilarczyk, G. Jablonski, D. Gąsecki, and K. Narkiewicz, “Persistent homology as a new method of the assessment of heart rate variability,” <i>PLoS ONE</i>, vol. 16, no. 7. Public Library of Science, 2021.","ista":"Graff G, Graff B, Pilarczyk P, Jablonski G, Gąsecki D, Narkiewicz K. 2021. Persistent homology as a new method of the assessment of heart rate variability. PLoS ONE. 16(7), e0253851.","chicago":"Graff, Grzegorz, Beata Graff, Pawel Pilarczyk, Grzegorz Jablonski, Dariusz Gąsecki, and Krzysztof Narkiewicz. “Persistent Homology as a New Method of the Assessment of Heart Rate Variability.” <i>PLoS ONE</i>. Public Library of Science, 2021. <a href=\"https://doi.org/10.1371/journal.pone.0253851\">https://doi.org/10.1371/journal.pone.0253851</a>."},"doi":"10.1371/journal.pone.0253851","ddc":["006"],"language":[{"iso":"eng"}],"acknowledgement":"We express our gratitude to the anonymous referees who provided constructive comments that helped us improve the quality of the paper.","pmid":1,"date_created":"2021-08-08T22:01:28Z","month":"07","status":"public","intvolume":"        16","quality_controlled":"1","department":[{"_id":"HeEd"}],"publication":"PLoS ONE","isi":1,"publisher":"Public Library of Science","oa_version":"Published Version","year":"2021","has_accepted_license":"1","article_type":"original","publication_identifier":{"eissn":["19326203"]},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","date_updated":"2023-08-10T14:21:42Z","scopus_import":"1","external_id":{"pmid":["34292957"],"isi":["000678124900050"]},"date_published":"2021-07-01T00:00:00Z","_id":"9821","abstract":[{"text":"Heart rate variability (hrv) is a physiological phenomenon of the variation in the length of the time interval between consecutive heartbeats. In many cases it could be an indicator of the development of pathological states. The classical approach to the analysis of hrv includes time domain methods and frequency domain methods. However, attempts are still being made to define new and more effective hrv assessment tools. Persistent homology is a novel data analysis tool developed in the recent decades that is rooted at algebraic topology. The Topological Data Analysis (TDA) approach focuses on examining the shape of the data in terms of connectedness and holes, and has recently proved to be very effective in various fields of research. In this paper we propose the use of persistent homology to the hrv analysis. We recall selected topological descriptors used in the literature and we introduce some new topological descriptors that reflect the specificity of hrv, and we discuss their relation to the standard hrv measures. In particular, we show that this novel approach provides a collection of indices that might be at least as useful as the classical parameters in differentiating between series of beat-to-beat intervals (RR-intervals) in healthy subjects and patients suffering from a stroke episode.","lang":"eng"}],"file":[{"content_type":"application/pdf","file_id":"9832","relation":"main_file","date_created":"2021-08-09T09:25:41Z","success":1,"file_name":"2021_PLoSONE_Graff.pdf","file_size":2706919,"creator":"asandaue","checksum":"0277aa155d5db1febd2cb384768bba5f","date_updated":"2021-08-09T09:25:41Z","access_level":"open_access"}],"article_number":"e0253851","issue":"7","article_processing_charge":"Yes","volume":16,"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)"},"file_date_updated":"2021-08-09T09:25:41Z","oa":1,"publication_status":"published"}]