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How dendritically targeted inhibition affects local dendritic excitability is not fully understood. Here we use computational models of branched dendrites to investigate where GABAergic synapses should be placed to minimise dendritic excitability over time. To do so, we formulate a metric we term the \u201cInhibitory Level\u201d (IL), which quantifies the effectiveness of synaptic inhibition for reducing the depolarising effect of nearby excitatory input. GABAergic synaptic inhibition is dependent on the reversal potential for GABA\n                    <jats:sub>A<\/jats:sub>\n                    receptors (EGABA), which is primarily set by the transmembrane chloride ion (Cl\n                    <jats:sup>-<\/jats:sup>\n                    ) concentration gradient. We, therefore, investigated how variable EGABA and dynamic chloride affects dendritic inhibition. We found that the inhibitory effectiveness of dendritic GABAergic synapses combines at an encircled branch junction. The extent of this inhibitory accumulation is dependent on the number of branches and location of synapses but is independent of EGABA. This inhibitory accumulation occurs even for very distally placed inhibitory synapses when they are hyperpolarising\u2013but not when they are shunting. When accounting for Cl\n                    <jats:sup>-<\/jats:sup>\n                    fluxes and dynamics in Cl\n                    <jats:sup>-<\/jats:sup>\n                    concentration, we observed that Cl\n                    <jats:sup>-<\/jats:sup>\n                    loading is detrimental to inhibitory effectiveness. This enabled us to determine the most inhibitory distribution of GABAergic synapses which is close to\u2013but not at\u2013a shared branch junction. This distribution balances a trade-off between a stronger combined\n                    <jats:italic>inhibitory influence<\/jats:italic>\n                    when synapses closely encircle a branch junction with the deleterious effects of increased Cl\n                    <jats:sup>-<\/jats:sup>\n                    by loading that occurs when inhibitory synapses are co-located.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1010534","type":"journal-article","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T14:33:21Z","timestamp":1663943601000},"page":"e1010534","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":6,"title":["Computational models reveal how chloride dynamics determine the optimal distribution of inhibitory synapses to minimise dendritic excitability"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4809-5059","authenticated-orcid":true,"given":"Christopher Brian","family":"Currin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8266-3128","authenticated-orcid":true,"given":"Joseph Valentino","family":"Raimondo","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,9,23]]},"reference":[{"key":"pcbi.1010534.ref001","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1007\/s00422-002-0377-3","article-title":"Deriving physical connectivity from neuronal morphology.","volume":"88","author":"N Kalisman","year":"2003","journal-title":"Biol Cybern"},{"key":"pcbi.1010534.ref002","doi-asserted-by":"crossref","first-page":"5419","DOI":"10.1073\/pnas.1016051108","article-title":"A synaptic organizing principle for cortical neuronal groups","volume":"108","author":"R Perin","year":"2011","journal-title":"Proc Natl Acad Sci U S A"},{"key":"pcbi.1010534.ref003","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.cell.2015.09.029","article-title":"Reconstruction and simulation of neocortical microcircuitry","volume":"163","author":"H Markram","year":"2015","journal-title":"Cell"},{"key":"pcbi.1010534.ref004","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1146\/annurev.neuro.28.061604.135703","article-title":"Dendritic computation.","volume":"28","author":"M London","year":"2005","journal-title":"Annu Rev Neurosci"},{"key":"pcbi.1010534.ref005","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/j.conb.2010.07.009","article-title":"The single dendritic branch as a fundamental functional unit in the nervous system","volume":"20","author":"T Branco","year":"2010","journal-title":"Curr Opin Neurobiol"},{"key":"pcbi.1010534.ref006","volume-title":"Dendrites.","author":"N Spruston","year":"2016"},{"key":"pcbi.1010534.ref007","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1038\/nrn2864","article-title":"Neuronal arithmetic.","volume":"11","author":"RA Silver","year":"2010","journal-title":"Nat Rev Neurosci"},{"key":"pcbi.1010534.ref008","doi-asserted-by":"crossref","first-page":"2180","DOI":"10.1152\/jn.00360.2016","article-title":"Is realistic neuronal modeling realistic?","volume":"116","author":"M Almog","year":"2016","journal-title":"J Neurophysiol"},{"key":"pcbi.1010534.ref009","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1016\/j.neuron.2016.03.019","article-title":"Mnemonic functions for nonlinear dendritic integration in hippocampal pyramidal circuits","volume":"90","author":"P Kaifosh","year":"2016","journal-title":"Neuron"},{"key":"pcbi.1010534.ref010","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1126\/science.aax6239","article-title":"Dendritic action potentials and computation in human layer 2\/3 cortical neurons","volume":"367","author":"A Gidon","year":"2020","journal-title":"Science (80-)."},{"key":"pcbi.1010534.ref011","doi-asserted-by":"crossref","first-page":"e1002550","DOI":"10.1371\/journal.pcbi.1002550","article-title":"Location-dependent effects of inhibition on local spiking in pyramidal neuron dendrites","volume":"8","author":"M Jadi","year":"2012","journal-title":"PLoS Comput Biol."},{"key":"pcbi.1010534.ref012","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1038\/nn.3024","article-title":"Regulation of neuronal input transformations by tunable dendritic inhibition","volume":"15","author":"M Lovett-Barron","year":"2012","journal-title":"Nat Neurosci"},{"key":"pcbi.1010534.ref013","doi-asserted-by":"crossref","first-page":"e22901","DOI":"10.7554\/eLife.22901","article-title":"Towards deep learning with segregated dendrites.","volume":"6","author":"J Guerguiev","year":"2017","journal-title":"Elife"},{"key":"pcbi.1010534.ref014","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/S0006-3495(62)86953-7","article-title":"Electrophysiology of a Dendritic Neuron Model","volume":"2","author":"W. 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