{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Saray S"],"funding":["European Social Fund","Horizon 2020 Framework Programme","Nemzeti Kutatási Fejlesztési és Innovációs Hivatal"],"pagination":["e1008114"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7875359"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["17(1)"],"pubmed_abstract":["Anatomically and biophysically detailed data-driven neuronal models have become widely used tools for understanding and predicting the behavior and function of neurons. Due to the increasing availability of experimental data from anatomical and electrophysiological measurements as well as the growing number of computational and software tools that enable accurate neuronal modeling, there are now a large number of different models of many cell types available in the literature. These models were usually built to capture a few important or interesting properties of the given neuron type, and it is often unknown how they would behave outside their original context. In addition, there is currently no simple way of quantitatively comparing different models regarding how closely they match speci"],"journal":["PLoS computational biology"],"pubmed_title":["HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data."],"pmcid":["PMC7875359"],"funding_grant_id":["785907","ÚNKP-19-3-III","720270","EFOP-3.6.3-VEKOP- 16-2017-00002"],"pubmed_authors":["Kali S","Vitale P","Lupascu CA","Migliore R","Rossert CA","Saray S","Romani A","Appukuttan S","Freund TF","Van Geit W","Davison AP","Muller E","Bologna LL"],"additional_accession":[]},"is_claimable":false,"name":"HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data.","description":"Anatomically and biophysically detailed data-driven neuronal models have become widely used tools for understanding and predicting the behavior and function of neurons. Due to the increasing availability of experimental data from anatomical and electrophysiological measurements as well as the growing number of computational and software tools that enable accurate neuronal modeling, there are now a large number of different models of many cell types available in the literature. These models were usually built to capture a few important or interesting properties of the given neuron type, and it is often unknown how they would behave outside their original context. In addition, there is currently no simple way of quantitatively comparing different models regarding how closely they match speci","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Jan","modification":"2025-04-18T15:59:05.086Z","creation":"2021-02-23T08:04:00Z"},"accession":"S-EPMC7875359","cross_references":{"pubmed":["33513130"],"doi":["10.1371/journal.pcbi.1008114"]}}