{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/flexible-statistical-inference-for","title":"Flexible statistical inference for mechanistic models of neural dynamics","arxiv_id":"1711.01861","date":"2017-11-06","proceeding":"NeurIPS 2017 12","authors":["Jan-Matthis Lueckmann","Pedro J. Goncalves","Giacomo Bassetto","Kaan Öcal","Marcel Nonnenmacher","Jakob H. Macke"],"abstract":"Mechanistic models of single-neuron dynamics have been extensively studied in\ncomputational neuroscience. However, identifying which models can\nquantitatively reproduce empirically measured data has been challenging. We\npropose to overcome this limitation by using likelihood-free inference\napproaches (also known as Approximate Bayesian Computation, ABC) to perform\nfull Bayesian inference on single-neuron models. Our approach builds on recent\nadvances in ABC by learning a neural network which maps features of the\nobserved data to the posterior distribution over parameters. We learn a\nBayesian mixture-density network approximating the posterior over multiple\nrounds of adaptively chosen simulations. Furthermore, we propose an efficient\napproach for handling missing features and parameter settings for which the\nsimulator fails, as well as a strategy for automatically learning relevant\nfeatures using recurrent neural networks. On synthetic data, our approach\nefficiently estimates posterior distributions and recovers ground-truth\nparameters. On in-vitro recordings of membrane voltages, we recover\nmultivariate posteriors over biophysical parameters, which yield\nmodel-predicted voltage traces that accurately match empirical data. Our\napproach will enable neuroscientists to perform Bayesian inference on complex\nneuron models without having to design model-specific algorithms, closing the\ngap between mechanistic and statistical approaches to single-neuron modelling.","url_abs":"http://arxiv.org/abs/1711.01861v1","url_pdf":"http://arxiv.org/pdf/1711.01861v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"flexible-statistical-inference-for","repo_url":"https://github.com/mackelab/delfi","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.01861","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}