{"entity": "researcher", "timestamp": "2026-08-20T21:08:25.684Z", "family": "Kozlov", "given": "Alexander", "initials": "A", "orcid": "0000-0003-3994-0799", "affiliations": ["Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, SE-10044, Stockholm, Sweden.", "Department of Neuroscience, Karolinska Institute, SE-17172, Stockholm, Sweden."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/bcd020bb912c4ff59b2cd86e28ac8d5c.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/bcd020bb912c4ff59b2cd86e28ac8d5c"}}, "publications": [{"entity": "publication", "iuid": "b3c5a34d9afc4b5c8febfa44e3feae9d", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/b3c5a34d9afc4b5c8febfa44e3feae9d.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/b3c5a34d9afc4b5c8febfa44e3feae9d"}}, "title": "Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda.", "authors": [{"family": "Hjorth", "given": "J J Johannes", "initials": "JJJ", "orcid": "0000-0002-9302-0750", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ec9300bb976d4038a766d908fcd0ce86.json"}}, {"family": "Hellgren Kotaleski", "given": "Jeanette", "initials": "J", "orcid": "0000-0002-0550-0739", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a920517bd1c142878f03bee05e843b62.json"}}, {"family": "Kozlov", "given": "Alexander", "initials": "A", "orcid": "0000-0003-3994-0799", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bcd020bb912c4ff59b2cd86e28ac8d5c.json"}}], "type": "journal article", "published": "2021-10-00", "journal": {"title": "Neuroinformatics", "issn": "1559-0089", "volume": "19", "issue": "4", "pages": "685-701", "issn-l": null}, "abstract": "Simulation of large-scale networks of neurons is an important approach to understanding and interpreting experimental data from healthy and diseased brains. 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 'bottom-up' manner. Simulated data from these models can be compared with experiments and 'top-down' 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 'curate' 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.", "doi": "10.1007/s12021-021-09531-w", "pmid": "34282528", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC8566446"}, {"db": "pii", "key": "10.1007/s12021-021-09531-w"}], "notes": [], "created": "2026-08-20T06:40:25.552Z", "modified": "2026-08-20T06:40:25.705Z"}, {"entity": "publication", "iuid": "93f150f40d0145e4bc4fc2c746510f4b", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/93f150f40d0145e4bc4fc2c746510f4b.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/93f150f40d0145e4bc4fc2c746510f4b"}}, "title": "Predicting synaptic connectivity for large-scale microcircuit simulations using Snudda", "authors": [{"family": "Hjorth", "given": "J J Johannes", "initials": "JJJ", "orcid": "0000-0002-9302-0750", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ec9300bb976d4038a766d908fcd0ce86.json"}}, {"family": "Kotaleski", "given": "Jeanette Hellgren", "initials": "JH", "orcid": "0000-0002-0550-0739", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a920517bd1c142878f03bee05e843b62.json"}}, {"family": "Kozlov", "given": "Alexander", "initials": "A", "orcid": "0000-0003-3994-0799", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bcd020bb912c4ff59b2cd86e28ac8d5c.json"}}], "type": "posted-content", "published": "2021-04-15", "journal": {"issn-l": null}, "abstract": null, "doi": "10.1101/2021.04.15.439985", "pmid": null, "labels": [], "xrefs": [], "notes": [], "created": "2026-08-20T10:03:59.797Z", "modified": "2026-08-20T10:03:59.860Z"}, {"entity": "publication", "iuid": "84f57e464ed24216aaed200f94fc312f", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/84f57e464ed24216aaed200f94fc312f.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/84f57e464ed24216aaed200f94fc312f"}}, "title": "The microcircuits of striatum in silico.", "authors": [{"family": "Hjorth", "given": "J J Johannes", "initials": "JJJ"}, {"family": "Kozlov", "given": "Alexander", "initials": "A", "orcid": "0000-0003-3994-0799", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bcd020bb912c4ff59b2cd86e28ac8d5c.json"}}, {"family": "Carannante", "given": "Ilaria", "initials": "I"}, {"family": "Frost Nyl\u00e9n", "given": "Johanna", "initials": "J", "orcid": "0000-0001-6863-8893", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2b679645c02544e9947f45d55cfeb813.json"}}, {"family": "Lindroos", "given": "Robert", "initials": "R"}, {"family": "Johansson", "given": "Yvonne", "initials": "Y"}, {"family": "Tokarska", "given": "Anna", "initials": "A"}, {"family": "Dorst", "given": "Matthijs C", "initials": "MC", "orcid": "0000-0002-1162-1481", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1fbde7c18d384d33a123afd3bb9380ba.json"}}, {"family": "Suryanarayana", "given": "Shreyas M", "initials": "SM", "orcid": "0000-0001-9122-0399", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1f866c10c1df407c8615818274a4decf.json"}}, {"family": "Silberberg", "given": "Gilad", "initials": "G"}, {"family": "Hellgren Kotaleski", "given": "Jeanette", "initials": "J"}, {"family": "Grillner", "given": "Sten", "initials": "S", "orcid": "0000-0002-8951-3691", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/18eacd075f8e45c888e6ed7233928b35.json"}}], "type": "journal article", "published": "2020-04-28", "journal": {"title": "Proc. Natl. Acad. Sci. U.S.A.", "issn": "1091-6490", "volume": "117", "issue": "17", "pages": "9554-9565", "issn-l": "0027-8424"}, "abstract": "The basal ganglia play an important role in decision making and selection of action primarily based on input from cortex, thalamus, and the dopamine system. Their main input structure, striatum, is central to this process. It consists of two types of projection neurons, together representing 95% of the neurons, and 5% of interneurons, among which are the cholinergic, fast-spiking, and low threshold-spiking subtypes. The membrane properties, soma-dendritic shape, and intrastriatal and extrastriatal synaptic interactions of these neurons are quite well described in the mouse, and therefore they can be simulated in sufficient detail to capture their intrinsic properties, as well as the connectivity. We focus on simulation at the striatal cellular/microcircuit level, in which the molecular/subcellular and systems levels meet. We present a nearly full-scale model of the mouse striatum using available data on synaptic connectivity, cellular morphology, and electrophysiological properties to create a microcircuit mimicking the real network. A striatal volume is populated with reconstructed neuronal morphologies with appropriate cell densities, and then we connect neurons together based on appositions between neurites as possible synapses and constrain them further with available connectivity data. Moreover, we simulate a subset of the striatum involving 10,000 neurons, with input from cortex, thalamus, and the dopamine system, as a proof of principle. Simulation at this biological scale should serve as an invaluable tool to understand the mode of operation of this complex structure. This platform will be updated with new data and expanded to simulate the entire striatum.", "doi": "10.1073/pnas.2000671117", "pmid": "32321828", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC7197017"}, {"db": "pii", "key": "2000671117"}], "notes": [], "created": "2026-08-20T09:30:40.174Z", "modified": "2026-08-20T09:30:40.356Z"}]}