{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:48:00Z","timestamp":1779205680920,"version":"3.51.4"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010068","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T00:00:00Z","timestamp":1652918400000}}],"reference-count":91,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T00:00:00Z","timestamp":1652054400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000011","name":"Howard Hughes Medical Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000011","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Swartz Foundation"},{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["NSF DBI-1707356 NeuroNex"],"award-info":[{"award-number":["NSF DBI-1707356 NeuroNex"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Center for Multiscale Modeling of Biological Systems"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Chemical synapses exhibit a diverse array of internal mechanisms that affect the dynamics of transmission efficacy. Many of these processes, such as release of neurotransmitter and vesicle recycling, depend strongly on activity-dependent influx and accumulation of Ca\n                    <jats:sup>2+<\/jats:sup>\n                    . To model how each of these processes may affect the processing of information in neural circuits, and how their dysfunction may lead to disease states, requires a computationally efficient modelling framework, capable of generating accurate phenomenology without incurring a heavy computational cost per synapse. Constructing a phenomenologically realistic model requires the precise characterization of the timing and probability of neurotransmitter release. Difficulties arise in that functional forms of instantaneous release rate can be difficult to extract from noisy data without running many thousands of trials, and in biophysical synapses, facilitation of per-vesicle release probability is confounded by depletion. To overcome this, we obtained traces of free Ca\n                    <jats:sup>2+<\/jats:sup>\n                    concentration in response to various action potential stimulus trains from a molecular MCell model of a hippocampal Schaffer collateral axon. Ca\n                    <jats:sup>2+<\/jats:sup>\n                    sensors were placed at varying distance from a voltage-dependent calcium channel (VDCC) cluster, and Ca\n                    <jats:sup>2+<\/jats:sup>\n                    was buffered by calbindin. Then, using the calcium traces to drive deterministic state vector models of synaptotagmin 1 and 7 (Syt-1\/7), which respectively mediate synchronous and asynchronous release in excitatory hippocampal synapses, we obtained high-resolution profiles of instantaneous release rate, to which we applied functional fits. Synchronous vesicle release occurred predominantly within half a micron of the source of spike-evoked Ca\n                    <jats:sup>2+<\/jats:sup>\n                    influx, while asynchronous release occurred more consistently at all distances. Both fast and slow mechanisms exhibited multi-exponential release rate curves, whose magnitudes decayed exponentially with distance from the Ca\n                    <jats:sup>2+<\/jats:sup>\n                    source. Profile parameters facilitate on different time scales according to a single, general facilitation function. These functional descriptions lay the groundwork for efficient mesoscale modelling of vesicular release dynamics.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1010068","type":"journal-article","created":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T13:46:55Z","timestamp":1652104015000},"page":"e1010068","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":7,"title":["Multiscale modeling of presynaptic dynamics from molecular to mesoscale"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6811-0415","authenticated-orcid":true,"given":"Jonathan W.","family":"Garcia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas M.","family":"Bartol","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0622-7391","authenticated-orcid":true,"given":"Terrence J.","family":"Sejnowski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2022,5,9]]},"reference":[{"issue":"7010","key":"pcbi.1010068.ref001","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1038\/nature03010","article-title":"Synaptic computation","volume":"431","author":"L.F. 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