Spine fusion and branching effects synaptic response (Rusakov et al 1996, 1997)

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Accession:18502
This compartmental model of a hippocampal granule cell has spinous synapses placed on the second-order dendrites. Changes in shape and connectivity of the spines usually does not effect the synaptic response of the cell unless active conductances are incorporated into the spine membrane (e.g. voltage-dependent Ca2+ channels). With active conductances, spines can generate spike-like events. We showed that changes like fusion and branching, or in fact any increase in the equivalent spine neck resistance, could trigger a dramatic increase in the spine's influence on the dendritic shaft potential.
References:
1 . Rusakov DA, Richter-Levin G, Stewart MG, Bliss TV (1997) Reduction in spine density associated with long-term potentiation in the dentate gyrus suggests a spine fusion-and-branching model of potentiation. Hippocampus 7:489-500 [PubMed]
2 . Rusakov DA, Stewart MG, Korogod SM (1996) Branching of active dendritic spines as a mechanism for controlling synaptic efficacy. Neuroscience 75:315-23 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism:
Cell Type(s): Dentate gyrus granule GLU cell;
Channel(s): I Na,t; I K; I K,Ca; I Sodium; I Calcium; I Potassium;
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Dendritic Action Potentials; Active Dendrites; Influence of Dendritic Geometry; Detailed Neuronal Models; Synaptic Plasticity; Long-term Synaptic Plasticity;
Implementer(s): Rusakov, DA [D.Rusakov at ion.ucl.ac.uk];
Search NeuronDB for information about:  Dentate gyrus granule GLU cell; AMPA; NMDA; I Na,t; I K; I K,Ca; I Sodium; I Calcium; I Potassium;
objectvar save_window_, rvp_
objectvar scene_vector_[5]
objectvar ocbox_, ocbox_list_, scene_, scene_list_
{ocbox_list_ = new List()  scene_list_ = new List()}
{pwman_place(12,535)}
{
xpanel("RunControl", 0)
v_init = -68
xvalue("Init","v_init", 1,"stdinit()", 1, 1 )
xbutton("Init & Run","run()")
xbutton("Stop","stoprun=1")
runStopAt = 5
xvalue("Continue til","runStopAt", 1,"{continuerun(runStopAt) stoprun=1}", 1, 1 )
runStopIn = 1
xvalue("Continue for","runStopIn", 1,"{continuerun(t + runStopIn) stoprun=1}", 1, 1 )
xbutton("Single Step","steprun()")
t = 50
xvalue("t","t", 2 )
tstop = 50
xvalue("Tstop","tstop", 1,"tstop_changed()", 0, 1 )
dt = 0.025
xvalue("dt","dt", 1,"setdt()", 0, 1 )
steps_per_ms = 40
xvalue("Points plotted/ms","steps_per_ms", 1,"setdt()", 0, 1 )
xcheckbox("Quiet",&stdrun_quiet,"")
realtime = 13
xvalue("Real Time","realtime", 0,"", 0, 1 )
xpanel(12,108)
}
{
save_window_ = new PlotShape(0)
save_window_.size(-30.9314,240.932,-130,80.0001)
save_window_.variable("v")
scene_vector_[2] = save_window_
{save_window_.view(-30.9314, -130, 271.863, 210, 382, 6, 375.3, 289.9)}
fast_flush_list.append(save_window_)
save_window_.save_name("fast_flush_list.")
}
{
save_window_ = new Graph(0)
save_window_.size(0,50,-80,40)
scene_vector_[4] = save_window_
{save_window_.view(0, -80, 50, 120, 906, 7, 396.9, 200.8)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addexpr("v(.5)", 1, 1, 0.8, 0.9, 2)
}
{
save_window_ = new Graph(0)
save_window_.size(0,50,-80,40)
scene_vector_[3] = save_window_
{save_window_.view(0, -80, 50, 120, 908, 345, 396.9, 200.8)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addexpr("spinh[41].v( 0.5 )", 1, 1, 0.8, 0.9, 2)
save_window_.addexpr("spinh[40].v( 0.5 )", 1, 1, 0.8, 0.9, 2)
save_window_.addexpr("spinh[42].v( 0.5 )", 1, 1, 0.8, 0.9, 2)
save_window_.addexpr("spinh[23].v( 0.5)", 1, 1, 0.8, 0.9, 2)
save_window_.addexpr("spinh[37].v( 0.5 )", 1, 1, 0.8, 0.9, 2)
}
objectvar scene_vector_[1]
{doNotify()}

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