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However, the applicability of BAL to experiments is limited as it requires performing high-dimensional integrations and optimizations in real time. Current methods are either too time consuming, or only applicable to specific models. Here, we propose an Efficient Sampling-Based Bayesian Active Learning (ESB-BAL) framework, which is efficient enough to be used in real-time biological experiments. We apply our method to the problem of estimating the parameters of a chemical synapse from the postsynaptic responses to evoked presynaptic action potentials. Using synthetic data and synaptic whole-cell patch-clamp recordings, we show that our method can improve the precision of model-based inferences, thereby paving the way towards more systematic and efficient experimental designs in physiology.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1011342","type":"journal-article","created":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T17:28:32Z","timestamp":1692638912000},"page":"e1011342","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Efficient sampling-based Bayesian Active Learning for synaptic characterization"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8433-3491","authenticated-orcid":true,"given":"Camille","family":"Gontier","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simone Carlo","family":"Surace","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6151-2363","authenticated-orcid":true,"given":"Igor","family":"Delvendahl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"M\u00fcller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Pascal","family":"Pfister","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2023,8,21]]},"reference":[{"issue":"2","key":"pcbi.1011342.ref001","doi-asserted-by":"crossref","DOI":"10.1523\/ENEURO.0113-15.2016","article-title":"Quantifying repetitive transmission at chemical synapses: a generative-model approach","volume":"3","author":"A Barri","year":"2016","journal-title":"Eneuro"},{"key":"pcbi.1011342.ref002","first-page":"116","article-title":"Bayesian inference of synaptic quantal parameters from correlated vesicle release","volume":"10","author":"AD Bird","year":"2016","journal-title":"Frontiers in computational neuroscience"},{"issue":"44","key":"pcbi.1011342.ref003","doi-asserted-by":"crossref","first-page":"E10313","DOI":"10.1073\/pnas.1800755115","article-title":"Comparing continual task learning in minds and machines","volume":"115","author":"T Flesch","year":"2018","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"6","key":"pcbi.1011342.ref004","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1088\/0957-0233\/9\/6\/003","article-title":"Optimal experiment design","volume":"9","author":"AF Emery","year":"1998","journal-title":"Measurement Science and Technology"},{"issue":"1","key":"pcbi.1011342.ref005","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1111\/1467-9868.00225","article-title":"Maximum entropy sampling and optimal Bayesian experimental design","volume":"62","author":"P Sebastiani","year":"2000","journal-title":"Journal of the Royal Statistical Society: Series B (Statistical Methodology)"},{"issue":"1","key":"pcbi.1011342.ref006","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1111\/insr.12107","article-title":"A review of modern computational algorithms for Bayesian optimal design","volume":"84","author":"EG Ryan","year":"2016","journal-title":"International Statistical Review"},{"issue":"3","key":"pcbi.1011342.ref007","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1162\/neco.2008.08-07-594","article-title":"Sequential optimal design of neurophysiology experiments","volume":"21","author":"J Lewi","year":"2009","journal-title":"Neural computation"},{"key":"pcbi.1011342.ref008","unstructured":"Park M, Horwitz G, Pillow JW. 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