{"related_material":{"link":[{"url":"https://ist.ac.at/en/news/can-evolution-be-predicted/","description":"News on IST Homepage","relation":"press_release"}],"record":[{"status":"public","relation":"dissertation_contains","id":"15020"}]},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","project":[{"grant_number":"754411","name":"ISTplus - Postdoctoral Fellowships","call_identifier":"H2020","_id":"260C2330-B435-11E9-9278-68D0E5697425"}],"quality_controlled":"1","language":[{"iso":"eng"}],"oa":1,"external_id":{"isi":["000637809600006"]},"date_published":"2021-04-07T00:00:00Z","year":"2021","publisher":"Cell Press","publication_status":"published","department":[{"_id":"GaTk"}],"date_updated":"2024-03-06T14:22:51Z","article_processing_charge":"No","ec_funded":1,"type":"journal_article","page":"1227-1241.e5","intvolume":"       109","scopus_import":"1","author":[{"last_name":"Mlynarski","full_name":"Mlynarski, Wiktor F","id":"358A453A-F248-11E8-B48F-1D18A9856A87","first_name":"Wiktor F"},{"first_name":"Michal","id":"4171253A-F248-11E8-B48F-1D18A9856A87","full_name":"Hledik, Michal","last_name":"Hledik"},{"first_name":"Thomas R","orcid":"0000-0002-1287-3779","last_name":"Sokolowski","full_name":"Sokolowski, Thomas R","id":"3E999752-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Gašper","orcid":"0000-0002-6699-1455","last_name":"Tkačik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper"}],"date_created":"2020-02-28T11:00:12Z","day":"07","title":"Statistical analysis and optimality of neural systems","issue":"7","citation":{"ama":"Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. Statistical analysis and optimality of neural systems. <i>Neuron</i>. 2021;109(7):1227-1241.e5. doi:<a href=\"https://doi.org/10.1016/j.neuron.2021.01.020\">10.1016/j.neuron.2021.01.020</a>","chicago":"Mlynarski, Wiktor F, Michal Hledik, Thomas R Sokolowski, and Gašper Tkačik. “Statistical Analysis and Optimality of Neural Systems.” <i>Neuron</i>. Cell Press, 2021. <a href=\"https://doi.org/10.1016/j.neuron.2021.01.020\">https://doi.org/10.1016/j.neuron.2021.01.020</a>.","ista":"Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. 2021. Statistical analysis and optimality of neural systems. Neuron. 109(7), 1227–1241.e5.","short":"W.F. Mlynarski, M. Hledik, T.R. Sokolowski, G. Tkačik, Neuron 109 (2021) 1227–1241.e5.","mla":"Mlynarski, Wiktor F., et al. “Statistical Analysis and Optimality of Neural Systems.” <i>Neuron</i>, vol. 109, no. 7, Cell Press, 2021, p. 1227–1241.e5, doi:<a href=\"https://doi.org/10.1016/j.neuron.2021.01.020\">10.1016/j.neuron.2021.01.020</a>.","apa":"Mlynarski, W. F., Hledik, M., Sokolowski, T. R., &#38; Tkačik, G. (2021). Statistical analysis and optimality of neural systems. <i>Neuron</i>. Cell Press. <a href=\"https://doi.org/10.1016/j.neuron.2021.01.020\">https://doi.org/10.1016/j.neuron.2021.01.020</a>","ieee":"W. F. Mlynarski, M. Hledik, T. R. Sokolowski, and G. Tkačik, “Statistical analysis and optimality of neural systems,” <i>Neuron</i>, vol. 109, no. 7. Cell Press, p. 1227–1241.e5, 2021."},"volume":109,"publication":"Neuron","_id":"7553","oa_version":"Preprint","status":"public","acknowledgement":"The authors thank Dario Ringach for providing the V1 receptive fields and Olivier Marre for providing the retinal receptive fields. W.M. was funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 754411. M.H. was funded in part by Human Frontiers Science grant no. HFSP RGP0032/2018.","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1101/848374"}],"isi":1,"month":"04","doi":"10.1016/j.neuron.2021.01.020","abstract":[{"text":"Normative theories and statistical inference provide complementary approaches for the study of biological systems. A normative theory postulates that organisms have adapted to efficiently solve essential tasks, and proceeds to mathematically work out testable consequences of such optimality; parameters that maximize the hypothesized organismal function can be derived ab initio, without reference to experimental data. In contrast, statistical inference focuses on efficient utilization of data to learn model parameters, without reference to any a priori notion of biological function, utility, or fitness. Traditionally, these two approaches were developed independently and applied separately. Here we unify them in a coherent Bayesian framework that embeds a normative theory into a family of maximum-entropy “optimization priors.” This family defines a smooth interpolation between a data-rich inference regime (characteristic of “bottom-up” statistical models), and a data-limited ab inito prediction regime (characteristic of “top-down” normative theory). We demonstrate the applicability of our framework using data from the visual cortex, and argue that the flexibility it affords is essential to address a number of fundamental challenges relating to inference and prediction in complex, high-dimensional biological problems.","lang":"eng"}]}