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Owing to the rapid development of simulation software and the accumulation of quantitative data of different neuronal types, it is possible to predict both computational and dynamical properties of local microcircuits in a \u2018bottom-up\u2019 manner. Simulated data from these models can be compared with experiments and \u2018top-down\u2019 modelling approaches, successively bridging the scales. Here we describe an open source pipeline, using the software Snudda, for predicting microcircuit connectivity and for setting up simulations using the NEURON simulation environment in a reproducible way. We also illustrate how to further \u2018curate\u2019 data on single neuron morphologies acquired from public databases. This model building pipeline was used to set up a first version of a full-scale cellular level model of mouse dorsal striatum. Model components from that work are here used to illustrate the different steps that are needed when modelling subcortical nuclei, such as the basal ganglia.<\/jats:p>","DOI":"10.1007\/s12021-021-09531-w","type":"journal-article","created":{"date-parts":[[2021,7,19]],"date-time":"2021-07-19T14:02:42Z","timestamp":1626703362000},"page":"685-701","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda"],"prefix":"10.1007","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9302-0750","authenticated-orcid":false,"given":"J. J. 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