FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2020587316> ?p ?o ?g. }

• W2020587316 endingPage "341" @default.
• W2020587316 startingPage "330" @default.
• W2020587316 abstract "Synaptic inhibition plays a key role in shaping the dynamics of neuronal networks and selecting cell assemblies. Typically, an inhibitory axon contacts a particular dendritic subdomain of its target neuron, where it often makes 10–20 synapses, sometimes on very distal branches. The functional implications of such a connectivity pattern are not well understood. Our experimentally based theoretical study highlights several new and counterintuitive principles for dendritic inhibition. We show that distal “off-path” rather than proximal “on-path” inhibition effectively dampens proximal excitable dendritic “hotspots,” thus powerfully controlling the neuron's output. Additionally, with multiple synaptic contacts, inhibition operates globally, spreading centripetally hundreds of micrometers from the inhibitory synapses. Consequently, inhibition in regions lacking inhibitory synapses may exceed that at the synaptic sites themselves. These results offer new insights into the synergetic effect of dendritic inhibition in controlling dendritic excitability and plasticity and in dynamically molding functional dendritic subdomains and their output. Synaptic inhibition plays a key role in shaping the dynamics of neuronal networks and selecting cell assemblies. Typically, an inhibitory axon contacts a particular dendritic subdomain of its target neuron, where it often makes 10–20 synapses, sometimes on very distal branches. The functional implications of such a connectivity pattern are not well understood. Our experimentally based theoretical study highlights several new and counterintuitive principles for dendritic inhibition. We show that distal “off-path” rather than proximal “on-path” inhibition effectively dampens proximal excitable dendritic “hotspots,” thus powerfully controlling the neuron's output. Additionally, with multiple synaptic contacts, inhibition operates globally, spreading centripetally hundreds of micrometers from the inhibitory synapses. Consequently, inhibition in regions lacking inhibitory synapses may exceed that at the synaptic sites themselves. These results offer new insights into the synergetic effect of dendritic inhibition in controlling dendritic excitability and plasticity and in dynamically molding functional dendritic subdomains and their output. Distal dendritic inhibition effectively controls excitability at proximal regions Inhibitory shunt spreads centripetally in dendrites encircled by multiple I-synapses Maximal inhibition may occur in dendritic domains lacking I-synapses A small number of I-synapses can effectively inhibit the entire dendritic tree Neurons are unique input-output devices. While their output is generated at the soma and/or axon region, it is first and foremost shaped by local processes in the dendritic tree (Koch and Segev, 2000Koch C. Segev I. The role of single neurons in information processing.Nat. Neurosci. 2000; 3: 1171-1177Crossref PubMed Scopus (365) Google Scholar; Häusser and Mel, 2003Häusser M. Mel B. Dendrites: bug or feature?.Curr. Opin. Neurobiol. 2003; 13: 372-383Crossref PubMed Scopus (280) Google Scholar; Polsky et al., 2004Polsky A. Mel B.W. Schiller J. Computational subunits in thin dendrites of pyramidal cells.Nat. Neurosci. 2004; 7: 621-627Crossref PubMed Scopus (550) Google Scholar; London and Häusser, 2005London M. Häusser M. Dendritic computation.Annu. Rev. Neurosci. 2005; 28: 503-532Crossref PubMed Scopus (744) Google Scholar; Spruston, 2008Spruston N. Pyramidal neurons: dendritic structure and synaptic integration.Nat. Rev. Neurosci. 2008; 9: 206-221Crossref PubMed Scopus (1080) Google Scholar; Branco and Häusser, 2010Branco T. Häusser M. The single dendritic branch as a fundamental functional unit in the nervous system.Curr. Opin. Neurobiol. 2010; 20: 494-502Crossref PubMed Scopus (223) Google Scholar). The latter is covered with an abundance of inhibitory and excitatory synaptic inputs and with a mixture of voltage-dependent membrane conductances that may trigger plasticity-inducing signals, such as dendritic N-methyl-D-aspartate (NMDA) and Ca2+ spikes (Häusser and Mel, 2003Häusser M. Mel B. Dendrites: bug or feature?.Curr. Opin. Neurobiol. 2003; 13: 372-383Crossref PubMed Scopus (280) Google Scholar; Lynch, 2004Lynch M.A. Long-term potentiation and memory.Physiol. Rev. 2004; 84: 87-136Crossref PubMed Scopus (1434) Google Scholar; London and Häusser, 2005London M. Häusser M. Dendritic computation.Annu. Rev. Neurosci. 2005; 28: 503-532Crossref PubMed Scopus (744) Google Scholar; Magee and Johnston, 2005Magee J.C. Johnston D. Plasticity of dendritic function.Curr. Opin. Neurobiol. 2005; 15: 334-342Crossref PubMed Scopus (143) Google Scholar; Magee, 2007Magee J.C. Voltage-gated ion channels in dendrites.in: Stuart G. Spruston N. Häusser M. Oxford University Press, New York2007: 139-160Google Scholar; Sjöström et al., 2008Sjöström P.J. Rancz E.A. Roth A. Häusser M. Dendritic excitability and synaptic plasticity.Physiol. Rev. 2008; 88: 769-840Crossref PubMed Scopus (494) Google Scholar; Sejnowski, 2009Sejnowski T.J. Consequences of non-uniform active currents in dendrites.Front Neurosci. 2009; 3: 332-333PubMed Google Scholar). In the hippocampus (Klausberger and Somogyi, 2008Klausberger T. Somogyi P. Neuronal diversity and temporal dynamics: the unity of hippocampal circuit operations.Science. 2008; 321: 53-57Crossref PubMed Scopus (1428) Google Scholar), the neocortex (Douglas and Martin, 2009Douglas R.J. Martin K.A. Inhibition in cortical circuits.Curr. Biol. 2009; 19: R398-R402Abstract Full Text Full Text PDF PubMed Scopus (21) Google Scholar; Helmstaedter et al., 2009Helmstaedter M. Sakmann B. Feldmeyer D. Neuronal correlates of local, lateral, and translaminar inhibition with reference to cortical columns.Cereb. Cortex. 2009; 19: 926-937Crossref PubMed Scopus (80) Google Scholar), and other brain regions (Tepper et al., 2004Tepper J.M. Koós T. Wilson C.J. GABAergic microcircuits in the neostriatum.Trends Neurosci. 2004; 27: 662-669Abstract Full Text Full Text PDF PubMed Scopus (330) Google Scholar), individual inhibitory axons from distinct input sources target specific dendritic subdomains, sometimes very distal dendritic regions, where each axon may form 10–20 synapses (Thomson and Deuchars, 1997Thomson A.M. Deuchars J. Synaptic interactions in neocortical local circuits: dual intracellular recordings in vitro.Cereb. Cortex. 1997; 7: 510-522Crossref PubMed Scopus (249) Google Scholar; Markram et al., 2004Markram H. Toledo-Rodriguez M. Wang Y. Gupta A. Silberberg G. Wu C. Interneurons of the neocortical inhibitory system.Nat. Rev. Neurosci. 2004; 5: 793-807Crossref PubMed Scopus (2105) Google Scholar; Kapfer et al., 2007Kapfer C. Glickfeld L.L. Atallah B.V. Scanziani M. Supralinear increase of recurrent inhibition during sparse activity in the somatosensory cortex.Nat. Neurosci. 2007; 10: 743-753Crossref PubMed Scopus (304) Google Scholar; Silberberg and Markram, 2007Silberberg G. Markram H. Disynaptic inhibition between neocortical pyramidal cells mediated by Martinotti cells.Neuron. 2007; 53: 735-746Abstract Full Text Full Text PDF PubMed Scopus (513) Google Scholar). For example, the axons of calretinin- and somatostatin-expressing neurons contact the distal dendritic domain of the postsynaptic target cell, parvalbumin-expressing basket cells target the soma and proximal dendrites, and the axon of chandelier cells targets very specifically the axons' initial segment (Kisvárday and Eysel, 1993Kisvárday Z.F. Eysel U.T. Functional and structural topography of horizontal inhibitory connections in cat visual cortex.Eur. J. Neurosci. 1993; 5: 1558-1572Crossref PubMed Scopus (71) Google Scholar; DeFelipe, 1997DeFelipe J. Types of neurons, synaptic connections and chemical characteristics of cells immunoreactive for calbindin-D28K, parvalbumin and calretinin in the neocortex.J. Chem. Neuroanat. 1997; 14: 1-19Crossref PubMed Scopus (454) Google Scholar; Defelipe et al., 1999Defelipe J. González-Albo M.C. Del Río M.R. Elston G.N. Distribution and patterns of connectivity of interneurons containing calbindin, calretinin, and parvalbumin in visual areas of the occipital and temporal lobes of the macaque monkey.J. Comp. Neurol. 1999; 412: 515-526Crossref PubMed Scopus (142) Google Scholar; Markram et al., 2004Markram H. Toledo-Rodriguez M. Wang Y. Gupta A. Silberberg G. Wu C. Interneurons of the neocortical inhibitory system.Nat. Rev. Neurosci. 2004; 5: 793-807Crossref PubMed Scopus (2105) Google Scholar; Pouille and Scanziani, 2004Pouille F. Scanziani M. Routing of spike series by dynamic circuits in the hippocampus.Nature. 2004; 429: 717-723Crossref PubMed Scopus (327) Google Scholar). This domain-specific division of labor between different inhibitory neuronal subclasses is expected to play a key role in selecting particular cell assemblies (Runyan et al., 2010Runyan C.A. Schummers J. Van Wart A. Kuhlman S.J. Wilson N.R. Huang Z.J. Sur M. Response features of parvalbumin-expressing interneurons suggest precise roles for subtypes of inhibition in visual cortex.Neuron. 2010; 67: 847-857Abstract Full Text Full Text PDF PubMed Scopus (180) Google Scholar) and in shaping (e.g., synchronizing) their activity (Cardin et al., 2009Cardin J.A. Carlén M. Meletis K. Knoblich U. Zhang F. Deisseroth K. Tsai L.-H. Moore C.I. Driving fast-spiking cells induces gamma rhythm and controls sensory responses.Nature. 2009; 459: 663-667Crossref PubMed Scopus (1771) Google Scholar; Vierling-Claassen et al., 2010Vierling-Claassen D. Cardin J.A. Moore C.I. Jones S.R. Computational modeling of distinct neocortical oscillations driven by cell-type selective optogenetic drive: separable resonant circuits controlled by low-threshold spiking and fast-spiking interneurons.Front Hum Neurosci. 2010; 4: 198Crossref PubMed Scopus (62) Google Scholar) and in controlling local dendritic nonlinear and plastic process (Llinás et al., 1968Llinás R. Nicholson C. Freeman J.A. Hillman D.E. Dendritic spikes and their inhibition in alligator Purkinje cells.Science. 1968; 160: 1132-1135Crossref PubMed Scopus (95) Google Scholar; Miles et al., 1996Miles R. Tóth K. Gulyás A.I. Hájos N. Freund T.F. Differences between somatic and dendritic inhibition in the hippocampus.Neuron. 1996; 16: 815-823Abstract Full Text Full Text PDF PubMed Scopus (748) Google Scholar; Larkum et al., 1999Larkum M.E. Zhu J.J. Sakmann B. A new cellular mechanism for coupling inputs arriving at different cortical layers.Nature. 1999; 398: 338-341Crossref PubMed Scopus (777) Google Scholar; Komaki et al., 2007Komaki A. Shahidi S. Lashgari R. Haghparast A. Malakouti S.M. Noorbakhsh S.M. Effects of GABAergic inhibition on neocortical long-term potentiation in the chronically prepared rat.Neurosci. Lett. 2007; 422: 181-186Crossref PubMed Scopus (27) Google Scholar; Sjöström et al., 2008Sjöström P.J. Rancz E.A. Roth A. Häusser M. Dendritic excitability and synaptic plasticity.Physiol. Rev. 2008; 88: 769-840Crossref PubMed Scopus (494) Google Scholar; Isaacson and Scanziani, 2011Isaacson J.S. Scanziani M. How inhibition shapes cortical activity.Neuron. 2011; 72: 231-243Abstract Full Text Full Text PDF PubMed Scopus (979) Google Scholar). Our theoretical understanding of dendritic inhibition is grounded on several, by now classical, analytical studies (Rall, 1964Rall W. Theoretical significance of dendritic trees for neuronal input-output relations.in: Reiss R.F. Stanford University Press, Palo Alto, CA1964: 73-97Google Scholar; Rinzel and Rall, 1974Rinzel J. Rall W. Transient response in a dendritic neuron model for current injected at one branch.Biophys. J. 1974; 14: 759-790Abstract Full Text PDF PubMed Scopus (171) Google Scholar; Jack et al., 1975Jack J.J.B. Noble D. Tsien R.W. Electric Current Flow in Excitable Cells. Clarendon Press, Oxford1975Google Scholar; Koch et al., 1983Koch C. Poggio T. Torre V. Nonlinear interactions in a dendritic tree: localization, timing, and role in information processing.Proc. Natl. Acad. Sci. USA. 1983; 80: 2799-2802Crossref PubMed Scopus (353) Google Scholar, Koch et al., 1990Koch C. Douglas R. Wehmeier U. Visibility of synaptically induced conductance changes: theory and simulations of anatomically characterized cortical pyramidal cells.J. Neurosci. 1990; 10: 1728-1744PubMed Google Scholar; Hao et al., 2009Hao J. Wang X.D. Dan Y. Poo M.M. Zhang X.H. An arithmetic rule for spatial summation of excitatory and inhibitory inputs in pyramidal neurons.Proc. Natl. Acad. Sci. USA. 2009; 106: 21906-21911Crossref PubMed Scopus (86) Google Scholar). These studies mostly explore the case of single inhibitory synapses impinging on passive dendritic trees and focus on the impact of such inhibitory synapses on the soma and/or axon's initial segment. For example, how “visible” is the dendritic synaptic conductance change when measured at the soma? What is the optimal locus of inhibition that maximally prevents the excitatory current from reaching the soma? These studies provided several important insights that still dominate our present view on dendritic inhibition; some of these predictions were later verified experimentally. In particular, (1) inhibitory conductance change is highly local (Liu, 2004Liu G. Local structural balance and functional interaction of excitatory and inhibitory synapses in hippocampal dendrites.Nat. Neurosci. 2004; 7: 373-379Crossref PubMed Scopus (236) Google Scholar; Mel and Schiller, 2004Mel B.W. Schiller J. On the fight between excitation and inhibition: location is everything.Sci. STKE. 2004; 2004: PE44PubMed Google Scholar; Williams, 2004Williams S.R. Spatial compartmentalization and functional impact of conductance in pyramidal neurons.Nat. Neurosci. 2004; 7: 961-967Crossref PubMed Scopus (101) Google Scholar), (2) inhibitory conductance change is always maximal at the inhibitory synaptic contact itself (Jack et al., 1975Jack J.J.B. Noble D. Tsien R.W. Electric Current Flow in Excitable Cells. Clarendon Press, Oxford1975Google Scholar), and (3) inhibition is maximally effective in dampening the excitatory current reaching the soma when inhibition is located “on the path” between the excitatory synapse and the soma, rather than when it is located more distally to the excitation (“off-path” inhibition; Koch et al., 1983Koch C. Poggio T. Torre V. Nonlinear interactions in a dendritic tree: localization, timing, and role in information processing.Proc. Natl. Acad. Sci. USA. 1983; 80: 2799-2802Crossref PubMed Scopus (353) Google Scholar; Hao et al., 2009Hao J. Wang X.D. Dan Y. Poo M.M. Zhang X.H. An arithmetic rule for spatial summation of excitatory and inhibitory inputs in pyramidal neurons.Proc. Natl. Acad. Sci. USA. 2009; 106: 21906-21911Crossref PubMed Scopus (86) Google Scholar). Here we suggest that the spatial pattern of dendritic innervation by inhibitory axons—the domain-specific, targeting distal branches and the multiple synapses per inhibitory axons—is optimized to control local and global dendritic excitability and plasticity processes in the dendritic tree, rather than to directly affect excitatory current flow to the soma and/or axon region. Toward this end, we defined a new measure for the impact of dendritic inhibition—the shunt level (SL)—and solved Rall's cable equation (Rall, 1959Rall W. Branching dendritic trees and motoneuron membrane resistivity.Exp. Neurol. 1959; 1: 491-527Crossref PubMed Scopus (545) Google Scholar) for SL for both single and multiple inhibitory synapses. Using SL, we could systematically characterize functional (as opposed to anatomical) inhibitory dendritic subdomains and showed that an effective control of local dendritic excitability requires a counterintuitive pattern of inhibitory innervation over the dendrites. We verified our theoretical predictions in detailed, experimentally based numerical models of three-dimensional (3D) reconstructed excitable dendritic trees receiving inhibitory synapses. Our study enabled us (1) to propose a functional role for very distal dendritic inhibition; (2) to demonstrate the regional effect of multiple, rather than single, inhibitory synapses in terms of the spread of their collective shunting effect in the dendritic tree; and (3) to suggest an explanation as to why, in both cortex and hippocampus, the total number of inhibitory dendritic synapses per pyramidal cell is smaller (about 20%) than that of excitatory synapses. This study thus provides a new perspective on the biophysical design principles that govern the operation of inhibition in dendrites. When an inhibitory synapse is activated at a dendritic location, i, a local conductance perturbation gi (a shunt) is induced in the dendritic membrane. Depending on the reversal potential of that synapse, either an inhibitory postsynaptic potential (IPSP) is also generated or no potential change is observed (a “shunting” or “silent” inhibition; Koch and Poggio, 1985Koch C. Poggio T. The synaptic veto mechanism: does it underlying direction and orientation selectivity in the visual cortex.in: Dobson V.G. Rose D. Wiley-Blackwell, Chichester, UK1985: 408-419Google Scholar). Although the membrane shunt due to the activation of the inhibitory synapses at i is highly local, its effect spreads to (i.e., is visible at) other dendritic locations (Rall, 1967Rall W. Distinguishing theoretical synaptic potentials computed for different soma-dendritic distributions of synaptic input.J. Neurophysiol. 1967; 30: 1138-1168PubMed Google Scholar; Koch et al., 1990Koch C. Douglas R. Wehmeier U. Visibility of synaptically induced conductance changes: theory and simulations of anatomically characterized cortical pyramidal cells.J. Neurosci. 1990; 10: 1728-1744PubMed Google Scholar; Williams, 2004Williams S.R. Spatial compartmentalization and functional impact of conductance in pyramidal neurons.Nat. Neurosci. 2004; 7: 961-967Crossref PubMed Scopus (101) Google Scholar). Indeed, this spatial spread is reflected by a change in input resistance, ΔRd, at location d. We define the shunt level at location d, SLd, as,SLd=ΔRdRd(1) where Rd is the input resistance in location d prior to the activation of gi. SLd is thus the relative drop in Rd at location d due to the activation of single (or multiple) steady conductance changes at arbitrary dendritic locations (see Figures S8 and S9 and related text available online for generalization to the transient case). The value of SLd ranges from 0 (no shunt) to 1 (infinite shunt) and depends on the particular dendritic distribution of gis. For example, SLd = 0.2 implies that the inhibitory synapse reduced the input resistance at location d by 20%, which is also the relative drop in the steady voltage at d due to the inhibition after the injection of steady current at location d. Thus, in order to characterize the effect of the inhibitory shunt in the most general way, it is natural to ask how much increase in excitatory current is required in order to exactly counter effect the shunting inhibition. This is exactly what SL implies. Note that the SL measure is applicable also for assessing the change in input resistance due to excitatory synapses that, like inhibition, exert a local membrane conductance change. The spatial spread of SL can be solved using cable theory for arbitrary passive dendritic trees receiving multiple inhibitory synapses (see Experimental Procedures and Supplemental Information). This solution provides several new and counterintuitive results regarding the overall impact of multiple inhibitory dendritic synapses in dendrites and explains several experimental and modeling results that were not fully understood prior to the present study. We started with a geometrically simple case, whereby a single inhibitory synapse impinges on a dendritic cylinder that is sealed ended at one side and is coupled to an isopotential excitable soma at the other (Figure 1A). The dendritic cylinder is comprised of a hotspot (Magee et al., 1995Magee J.C. Christofi G. Miyakawa H. Christie B. Lasser-Ross N. Johnston D. Subthreshold synaptic activation of voltage-gated Ca2+ channels mediates a localized Ca2+ influx into the dendrites of hippocampal pyramidal neurons.J. Neurophysiol. 1995; 74: 1335-1342PubMed Google Scholar; Schiller et al., 1997Schiller J. Schiller Y. Stuart G. Sakmann B. Calcium action potentials restricted to distal apical dendrites of rat neocortical pyramidal neurons.J. Physiol. 1997; 505: 605-616Crossref PubMed Scopus (377) Google Scholar, Schiller et al., 2000Schiller J. Major G. Koester H.J. Schiller Y. NMDA spikes in basal dendrites of cortical pyramidal neurons.Nature. 2000; 404: 285-289Crossref PubMed Scopus (484) Google Scholar; Larkum et al., 1999Larkum M.E. Zhu J.J. Sakmann B. A new cellular mechanism for coupling inputs arriving at different cortical layers.Nature. 1999; 398: 338-341Crossref PubMed Scopus (777) Google Scholar; Antic et al., 2010Antic S.D. Zhou W.L. Moore A.R. Short S.M. Ikonomu K.D. The decade of the dendritic NMDA spike.J. Neurosci. Res. 2010; 88: 2991-3001Crossref PubMed Scopus (124) Google Scholar), which is modeled by a cluster of 20 NMDA synapses, each randomly activated at 20 Hz (red circle and red synapse in Figure 1A). We then searched for the strategic placement of the inhibitory synapse that would effectively dampen this local dendritic hotspot. Using numerical simulations for the nonlinear cable model that includes the spiking soma and NMDA synapses depicted in Figure 1A, we found that when the inhibitory conductance change, gi, was placed distally (“off-path”) to the hotspot, the rate of the soma action potentials (black trace in Figure 1B) was reduced more effectively than when the same inhibitory synapse was placed proximally (“on-path”) at the same distance from the hotspot (orange trace in Figure 1B). Indeed, such asymmetry in the impact of proximal versus distal inhibition for dampening local dendritic hotspot was previously observed in vitro (Miles et al., 1996Miles R. Tóth K. Gulyás A.I. Hájos N. Freund T.F. Differences between somatic and dendritic inhibition in the hippocampus.Neuron. 1996; 16: 815-823Abstract Full Text Full Text PDF PubMed Scopus (748) Google Scholar; Jadi et al., 2012Jadi M. Polsky J. Schiller J. Mel B.W. Location-dependent effects of inhibition on local spiking in pyramidal neuron dendrites.PLoS Comput Biol. 2012; 8: e1002550Crossref PubMed Scopus (75) Google Scholar; Lovett-Barron et al., 2012Lovett-Barron M. Turi G.F. Kaifosh P. Lee P.H. Bolze F. Sun X.-H. Nicoud J.-F. Zemelman B.V. Sternson S.M. Losonczy A. Regulation of neuronal input transformations by tunable dendritic inhibition.Nat. Neurosci. 2012; 15 (S1–S3): 423-430Crossref PubMed Scopus (283) Google Scholar; see also Liu, 2004Liu G. Local structural balance and functional interaction of excitatory and inhibitory synapses in hippocampal dendrites.Nat. Neurosci. 2004; 7: 373-379Crossref PubMed Scopus (236) Google Scholar) and in simulations (Archie and Mel, 2000Archie K.A. Mel B.W. A model for intradendritic computation of binocular disparity.Nat. Neurosci. 2000; 3: 54-63Crossref PubMed Scopus (110) Google Scholar; Rhodes, 2006Rhodes P. The properties and implications of NMDA spikes in neocortical pyramidal cells.J. Neurosci. 2006; 26: 6704-6715Crossref PubMed Scopus (71) Google Scholar), but the basis for this counterintuitive result has remained unclear. In order to provide an explanation for this result, we analytically computed the value for SL at the hotspot (h) and thus assessed the impact of inhibition at this location (Figures 1C–1E). In the corresponding passive case, SLh at the hotspot that is due to the inhibitory conductance change gi at location i can be expressed as the product of SL amplitude at location i (SLi) and the attenuation of SL from i to h (SLi,h), i.e.,SLh=SLi×SLi,h.(2) It can be shown (see Equations 4, 5, and 6 in Experimental Procedures) thatSLi,h=Ah,i×Ai,h,(3) where Ah,i is the steady voltage attenuation from h to i (i.e., Vi / Vh for steady current injected at h) and vice versa for Ai,h. Biophysically, Equation 3 can be explained as follows: depolarization originating at h attenuates to i (Ah,i), where it changes the driving force for the inhibitory synapse. Consequently, the inhibitory synapse induces an outward current at i, resulting in a reduction in local depolarization at i that propagates back to site h (Ai,h). Consequently, the local conductance change at the inhibitory synapse is also visible at other locations. The asymmetry of the impact of distal versus proximal inhibition (Figures 1D and 1E) on location h (the hotspot) results from the difference in the model's boundary conditions, namely, sealed-end boundary at the distal end and an isopotential soma at the proximal end. This difference implies that the input resistance and SLi (in cases of a fixed gi) also increase monotonically with distance from the soma (Figure 1C and Equation 6 in Experimental Procedures). Thus, the distal SLi (e.g., black circle at X = +0.4, Figure 1C) is larger than that at the corresponding proximal site (SLi at X = –0.4, orange circle). Additionally, the overall voltage attenuation from the inhibitory synapses to the hotspot and back to the synapses, and thus SLi,h (Equation 3), is shallower for the distal synapses than for the proximal synapses, because the latter is more affected by the somatic current sink (Figure 1D, compare black arrowed dashed line to the orange dashed line). The product of these two effects—the initially larger SLi at the distal synapse and the shallower attenuation of SLi from the distal synapse to the hotspot—implies that SL at the hotspot (SLh) is larger for this synapse (Figure 1E). The later conclusion also holds for transient inhibitory synaptic conductance (Figures S8 and S9). The above analysis considered the impact of the inhibitory conductance change per se, namely, the case of a “silent inhibition,” whereby the reversal potential of the inhibitory synapse, Ei, equals the resting potential, Vrest. Do the results depicted in Figure 1 still hold when Ei is more negative than Vrest (hyperpolarizing inhibition)? Figure 2 shows that the advantage of the “off-path” inhibition over the corresponding “on-path” inhibition in dampening the hotspot is actually enhanced for hyperpolarizing inhibition (compare Figure 1B to Figure 2B). Due to the asymmetry in the boundary conditions, the distal synapse induces a larger hyperpolarization at the hotspot compared to the proximal synapse. Both the larger hyperpolarization and the larger SL at the hotspot generated by the distal synapse are combined to enhance its inhibitory impact on the hotspot (and thus on the soma firing) as compared to the proximal synapse (Figure 2C and see more detailed analysis in Figures S5–S7). These results are also valid for different loci with respect to the hotspot of the inhibitory synapses along the dendritic cable model (Figure S5). Note that the results in Figures 1 and 2 hold for any dendritic region producing inward current (e.g., via an α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid [AMPA] synapse). But the advantage of distal versus proximal inhibition at that region is amplified in the voltage-dependent (nonlinear) case (e.g., NMDA currents as in Figures 1B and 2B or active Ca+2 or Na+ inward currents) because inhibition at the hotspot increases the threshold for the activation of regenerative inward currents (Jadi et al., 2012Jadi M. Polsky J. Schiller J. Mel B.W. Location-dependent effects of inhibition on local spiking in pyramidal neuron dendrites.PLoS Comput Biol. 2012; 8: e1002550Crossref PubMed Scopus (75) Google Scholar). We also note that the advantage of the “off-path” inhibition over the corresponding “on-path” inhibition in dampening a local dendritic hotspot is augmented in distal thin dendrites because, in such branches, the asymmetry in (distal versus proximal) boundary conditions is even larger than the cylindrical case modeled in Figures 1 and 2 (Rall and Rinzel, 1973Rall W. Rinzel J. Branch input resistance and steady attenuation for input to one branch of a dendritic neuron model.Biophys. J. 1973; 13: 648-687Abstract Full Text PDF PubMed Scopus (237) Google Scholar). Figure 3 depicts SL in the case of an idealized branched dendritic tree (Rall and Rinzel, 1973Rall W. Rinzel J. Branch input resistance and steady attenuation for input to one branch of a dendritic neuron model.Biophys. J. 1973; 13: 648-687Abstract Full Text PDF PubMed Scopus (237) Google Scholar) receiving a single conductance perturbation in a distal dendritic terminal. For comparison, the steady voltage (V, dotted line) attenuation is also shown. V attenuation is steep from the distal (input) branch toward the branch point (P) but is shallow in the direction of the sibling branch S (Figure 3, black arrow) because of the sealed-end boundary condition in this branch (Rall and Rinzel, 1973Rall W. Rinzel J. Branch input resistance and steady attenuation for input to one branch of a dendritic neuron model.Biophys. J. 1973; 13: 648-687Abstract Full Text PDF PubMed Scopus (237) Google Scholar; Golding et al., 2005Golding N.L. Mickus T.J. Katz Y. Kath W.L. Spruston N. Factors mediating powerful voltage attenuation along CA1 pyramidal neuron dendrites.J. Physiol. 2005; 568: 69-82Crossref PubMed Scopus (150) Google Scholar). Similarly to V, SL attenuates steeply toward the soma; however, in contrast to V, SL attenuates steeply toward terminal S (blue line). This follows directly from Equation 3, as SL attenuation from P to S depends on the (steep) voltage attenuation from S to P (AS,P). Consequently, the impact of conductance perturbation diminishes rapidly with distance in such thin dendritic branches. Hence, excitatory currents in distal dendrites are electrically “protected” from the inhibitory shunt, unless the inhibitory synapses directly" @default.
• W2020587316 created "2016-06-24" @default.
• W2020587316 creator A5013964408 @default.
• W2020587316 creator A5025022011 @default.
• W2020587316 date "2012-07-01" @default.
• W2020587316 modified "2023-10-18" @default.
• W2020587316 title "Principles Governing the Operation of Synaptic Inhibition in Dendrites" @default.
• W2020587316 cites W1488737327 @default.
• W2020587316 cites W1515489022 @default.
• W2020587316 cites W1517714302 @default.
• W2020587316 cites W1566861944 @default.
• W2020587316 cites W1623755432 @default.
• W2020587316 cites W1645542202 @default.
• W2020587316 cites W1836848727 @default.
• W2020587316 cites W1967038391 @default.
• W2020587316 cites W1967324141 @default.
• W2020587316 cites W1969584830 @default.
• W2020587316 cites W1970359800 @default.
• W2020587316 cites W1971335223 @default.
• W2020587316 cites W1972759447 @default.
• W2020587316 cites W1991762922 @default.
• W2020587316 cites W1992182809 @default.
• W2020587316 cites W1992646289 @default.
• W2020587316 cites W1995649895 @default.
• W2020587316 cites W1997953085 @default.
• W2020587316 cites W2005944770 @default.
• W2020587316 cites W2006902614 @default.
• W2020587316 cites W2008027377 @default.
• W2020587316 cites W2012839705 @default.
• W2020587316 cites W2021201177 @default.
• W2020587316 cites W2025603247 @default.
• W2020587316 cites W2031248308 @default.
• W2020587316 cites W2032044543 @default.
• W2020587316 cites W2037210922 @default.
• W2020587316 cites W2040407982 @default.
• W2020587316 cites W2044648666 @default.
• W2020587316 cites W2044670260 @default.
• W2020587316 cites W2046426203 @default.
• W2020587316 cites W2046771549 @default.
• W2020587316 cites W2047403459 @default.
• W2020587316 cites W2055938779 @default.
• W2020587316 cites W2057613746 @default.
• W2020587316 cites W2061186544 @default.
• W2020587316 cites W2063752629 @default.
• W2020587316 cites W2067327770 @default.
• W2020587316 cites W2070493184 @default.
• W2020587316 cites W2078529082 @default.
• W2020587316 cites W2083577693 @default.
• W2020587316 cites W2083863715 @default.
• W2020587316 cites W2090278031 @default.
• W2020587316 cites W2090490936 @default.
• W2020587316 cites W2091354315 @default.
• W2020587316 cites W2091984887 @default.
• W2020587316 cites W2100260601 @default.
• W2020587316 cites W2101574677 @default.
• W2020587316 cites W2107633128 @default.
• W2020587316 cites W2108502510 @default.
• W2020587316 cites W2112541959 @default.
• W2020587316 cites W2116228954 @default.
• W2020587316 cites W2117120818 @default.
• W2020587316 cites W2119259421 @default.
• W2020587316 cites W2124892164 @default.
• W2020587316 cites W2127457386 @default.
• W2020587316 cites W2129620008 @default.
• W2020587316 cites W2129761500 @default.
• W2020587316 cites W2131515526 @default.
• W2020587316 cites W2133406691 @default.
• W2020587316 cites W2135470655 @default.
• W2020587316 cites W2136884792 @default.
• W2020587316 cites W2137576371 @default.
• W2020587316 cites W2140845062 @default.
• W2020587316 cites W2141998004 @default.
• W2020587316 cites W2144205676 @default.
• W2020587316 cites W2146655366 @default.
• W2020587316 cites W2147916701 @default.
• W2020587316 cites W2149561999 @default.
• W2020587316 cites W2154421570 @default.
• W2020587316 cites W2156222687 @default.
• W2020587316 cites W2159749299 @default.
• W2020587316 cites W2162019295 @default.
• W2020587316 cites W2168025484 @default.
• W2020587316 cites W2403177503 @default.
• W2020587316 cites W2406265206 @default.
• W2020587316 cites W4212952892 @default.
• W2020587316 doi "https://doi.org/10.1016/j.neuron.2012.05.015" @default.
• W2020587316 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/22841317" @default.
• W2020587316 hasPublicationYear "2012" @default.
• W2020587316 type Work @default.
• W2020587316 sameAs 2020587316 @default.
• W2020587316 citedByCount "192" @default.
• W2020587316 countsByYear W20205873162012 @default.
• W2020587316 countsByYear W20205873162013 @default.
• W2020587316 countsByYear W20205873162014 @default.
• W2020587316 countsByYear W20205873162015 @default.
• W2020587316 countsByYear W20205873162016 @default.
• W2020587316 countsByYear W20205873162017 @default.
• W2020587316 countsByYear W20205873162018 @default.
• W2020587316 countsByYear W20205873162019 @default.

• next