{"entity": "researcher", "timestamp": "2026-08-20T20:50:08.448Z", "family": "Blackwell", "given": "Kim T", "initials": "KT", "orcid": "0000-0003-4711-2344", "affiliations": ["The Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA.", "Department of Bioengineering, George Mason University, Fairfax, VA, USA."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/c810676e03014f6389d2cc79889e77d4.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/c810676e03014f6389d2cc79889e77d4"}}, "publications": [{"entity": "publication", "iuid": "11a0dcc1f898408fbf3563cc8e393d23", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/11a0dcc1f898408fbf3563cc8e393d23.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/11a0dcc1f898408fbf3563cc8e393d23"}}, "title": "Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows.", "authors": [{"family": "Eriksson", "given": "Olivia", "initials": "O", "orcid": "0000-0003-0740-4318", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/50ed8ead3a804dd28e4e7d5d3b3556d8.json"}}, {"family": "Bhalla", "given": "Upinder Singh", "initials": "US", "orcid": "0000-0003-1722-5188", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cdffabe50ea9416c81b99c91f5b42983.json"}}, {"family": "Blackwell", "given": "Kim T", "initials": "KT", "orcid": "0000-0003-4711-2344", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c810676e03014f6389d2cc79889e77d4.json"}}, {"family": "Crook", "given": "Sharon M", "initials": "SM", "orcid": "0000-0003-1659-0749", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b4b169ae29de4474a271926f09e8889f.json"}}, {"family": "Keller", "given": "Daniel", "initials": "D", "orcid": "0000-0003-3280-6255", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/815355e0fa8f4a02913e758c3d296bf5.json"}}, {"family": "Kramer", "given": "Andrei", "initials": "A", "orcid": "0000-0002-3828-6978", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8181305816dc47afbd6a2bb17b9bab63.json"}}, {"family": "Linne", "given": "Marja-Leena", "initials": "ML", "orcid": "0000-0003-2577-7329", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/4bb446e2144a4f22afc2bb5c38769282.json"}}, {"family": "Saudargien\u0117", "given": "Ausra", "initials": "A", "orcid": "0000-0003-2768-3334", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cd194515e38a4230a32c995136ab2395.json"}}, {"family": "Wade", "given": "Rebecca C", "initials": "RC", "orcid": "0000-0001-5951-8670", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/502d793871ee450e9b3961a027d3199d.json"}}, {"family": "Hellgren Kotaleski", "given": "Jeanette", "initials": "J", "orcid": "0000-0002-0550-0739", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a920517bd1c142878f03bee05e843b62.json"}}], "type": "journal article", "published": "2022-07-06", "journal": {"title": "Elife", "issn": "2050-084X", "volume": "11", "issn-l": "2050-084X"}, "abstract": "Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data - such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles - also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock-Cooper-Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.", "doi": "10.7554/eLife.69013", "pmid": "35792600", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC9259018"}, {"db": "pii", "key": "69013"}], "notes": [], "created": "2026-08-20T13:53:50.356Z", "modified": "2026-08-20T13:53:51.537Z"}, {"entity": "publication", "iuid": "9f23967b7f0a4d57813fdccd5ded6916", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/9f23967b7f0a4d57813fdccd5ded6916.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/9f23967b7f0a4d57813fdccd5ded6916"}}, "title": "Molecular mechanisms underlying striatal synaptic plasticity: relevance to chronic alcohol consumption and seeking.", "authors": [{"family": "Blackwell", "given": "Kim T", "initials": "KT", "orcid": "0000-0003-4711-2344", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c810676e03014f6389d2cc79889e77d4.json"}}, {"family": "Salinas", "given": "Armando G", "initials": "AG"}, {"family": "Tewatia", "given": "Parul", "initials": "P"}, {"family": "English", "given": "Brad", "initials": "B"}, {"family": "Hellgren Kotaleski", "given": "Jeanette", "initials": "J"}, {"family": "Lovinger", "given": "David M", "initials": "DM"}], "type": "journal article", "published": "2019-03-00", "journal": {"title": "Eur. J. Neurosci.", "issn": "1460-9568", "volume": "49", "issue": "6", "pages": "768-783", "issn-l": "0953-816X"}, "abstract": "The striatum, the input structure of the basal ganglia, is a major site of learning and memory for goal-directed actions and habit formation. Spiny projection neurons of the striatum integrate cortical, thalamic, and nigral inputs to learn associations, with cortico-striatal synaptic plasticity as a learning mechanism. Signaling molecules implicated in synaptic plasticity are altered in alcohol withdrawal, which may contribute to overly strong learning and increased alcohol seeking and consumption. To understand how interactions among signaling molecules produce synaptic plasticity, we implemented a mechanistic model of signaling pathways activated by dopamine D1 receptors, acetylcholine receptors, and glutamate. We use our novel, computationally efficient simulator, NeuroRD, to simulate stochastic interactions both within and between dendritic spines. Dopamine release during theta burst and 20-Hz stimulation was extrapolated from fast-scan cyclic voltammetry data collected in mouse striatal slices. Our results show that the combined activity of several key plasticity molecules correctly predicts the occurrence of either LTP, LTD, or no plasticity for numerous experimental protocols. To investigate spatial interactions, we stimulate two spines, either adjacent or separated on a 20-\u03bcm dendritic segment. Our results show that molecules underlying LTP exhibit spatial specificity, whereas 2-arachidonoylglycerol exhibits a spatially diffuse elevation. We also implement changes in NMDA receptors, adenylyl cyclase, and G protein signaling that have been measured following chronic alcohol treatment. Simulations under these conditions suggest that the molecular changes can predict changes in synaptic plasticity, thereby accounting for some aspects of alcohol use disorder.", "doi": "10.1111/ejn.13919", "pmid": "29602186", "labels": [], "xrefs": [{"db": "mid", "key": "NIHMS954619"}, {"db": "pmc", "key": "PMC6165719"}], "notes": [], "created": "2026-08-20T11:17:40.943Z", "modified": "2026-08-20T11:17:41.022Z"}]}