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It combines customizable neuronal and synaptic mechanisms with high-performance computing, supporting multi-core CPU and GPU systems.<\/jats:p>\n                  <jats:p>In humans and other animals, synaptic plasticity processes play a vital role in cognitive functions, including learning and memory. Recent studies have shown that intracellular molecular processes in dendrites significantly influence single-neuron dynamics. However, for understanding how the complex interplay between dendrites and synaptic processes influences network dynamics, computational modeling is required.<\/jats:p>\n                  <jats:p>To enable the modeling of large-scale networks of morphologically detailed neurons with diverse plasticity processes, we have extended the Arbor library to support simulations of a large variety of spike-driven plasticity paradigms. To showcase the features of the extended framework, we present examples of computational models, beginning with single-synapse dynamics, progressing to multi-synapse rules, and finally scaling up to large recurrent networks. While cross-validating our implementations by comparison with other simulators, we show that Arbor allows simulating plastic networks of multi-compartment neurons at nearly no additional cost in runtime compared to point-neuron simulations. In addition, we demonstrate that Arbor is highly efficient in terms of runtime and memory use as compared to other simulators.<\/jats:p>\n                  <jats:p>Using the extended framework, as an example, we investigate the impact of dendritic structures on network dynamics across a timescale of several hours, finding a relation between the length of dendritic trees and the ability of the network to efficiently store information.<\/jats:p>\n                  <jats:p>By our extension of Arbor, we aim to provide a valuable tool that will support future studies on the impact of synaptic plasticity, especially, in conjunction with neuronal morphology, in large networks.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1013926","type":"journal-article","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T18:47:47Z","timestamp":1770922067000},"page":"e1013926","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Plastic Arbor: A modern simulation framework for synaptic plasticity\u2014From single synapses to networks of morphological neurons"],"prefix":"10.1371","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1959-5644","authenticated-orcid":true,"given":"Jannik","family":"Luboeinski","sequence":"first","affiliation":[]},{"given":"Sebastian","family":"Schmitt","sequence":"additional","affiliation":[]},{"given":"Shirin","family":"Shafiee","sequence":"additional","affiliation":[]},{"given":"Thorsten","family":"Hater","sequence":"additional","affiliation":[]},{"given":"Fabian","family":"B\u00f6sch","sequence":"additional","affiliation":[]},{"given":"Christian","family":"Tetzlaff","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2026,2,12]]},"reference":[{"issue":"1","key":"pcbi.1013926.ref001","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/0020-7101(89)90007-X","article-title":"A program for simulation of nerve equations with branching geometries","volume":"24","author":"M Hines","year":"1989","journal-title":"Int J Biomed Comput."},{"key":"pcbi.1013926.ref002","doi-asserted-by":"crossref","first-page":"63","DOI":"10.3389\/fninf.2019.00063","article-title":"CoreNEURON: an optimized compute engine for the NEURON simulator","volume":"13","author":"P Kumbhar","year":"2019","journal-title":"Front Neuroinform."},{"key":"pcbi.1013926.ref003","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.4249\/scholarpedia.1430","article-title":"NEST (NEural Simulation Tool)","volume":"2","author":"MO Gewaltig","year":"2007","journal-title":"Scholarpedia."},{"key":"pcbi.1013926.ref004","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.47314","article-title":"Brian 2, an intuitive and efficient neural simulator","volume":"8","author":"M Stimberg","year":"2019","journal-title":"Elife."},{"key":"pcbi.1013926.ref005","doi-asserted-by":"crossref","first-page":"884046","DOI":"10.3389\/fninf.2022.884046","article-title":"Modernizing the NEURON simulator for sustainability, portability, and performance","volume":"16","author":"O Awile","year":"2022","journal-title":"Front Neuroinform."},{"key":"pcbi.1013926.ref006","doi-asserted-by":"crossref","unstructured":"Akar NA, Cumming B, Karakasis V, Kusters A, Klijn W, Peyser A, et al. 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