Balance of excitation and inhibition (Carvalho and Buonomano 2009)

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Accession:125689
" ... Here, theoretical analyses reveal that excitatory synaptic strength controls the threshold of the neuronal input-output function, while inhibitory plasticity alters the threshold and gain. Experimentally, changes in the balance of excitation and inhibition in CA1 pyramidal neurons also altered their input-output function as predicted by the model. These results support the existence of two functional modes of plasticity that can be used to optimize information processing: threshold and gain plasticity."
Reference:
1 . Carvalho TP, Buonomano DV (2009) Differential effects of excitatory and inhibitory plasticity on synaptically driven neuronal input-output functions. Neuron 61:774-85 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Synaptic Integration;
Implementer(s):
objectvar save_window_, rvp_
objectvar scene_vector_[5]
objectvar ocbox_, ocbox_list_, scene_, scene_list_
{ocbox_list_ = new List()  scene_list_ = new List()}
{
save_window_ = new Graph(0)
save_window_.size(0,100,-80,300)
scene_vector_[0] = save_window_
{save_window_.view(0, -80, 100, 380, 706, -5, 300.6, 200.8)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addvar("Ex[0].soma.v", 1, 1, 0.8, 0.9, 2)
save_window_.addexpr("Ex[1].soma.v+1*10", 3, 1, 0.8, 0.9, 2)
}
{
save_window_ = new Graph(0)
save_window_.size(0,1,-2,1)
scene_vector_[4] = save_window_
{save_window_.view(0, -2, 1, 3, 1122, -3, 300.6, 200.8)}
save_window_.label(0.5, 0.95, "Triang=Inh->Ex", 2, 1, 0, 0, 1)
save_window_.label(0.5, 0.9, "Circ=Ex->Inh", 2, 1, 0, 0, 1)
}
{
save_window_ = new Graph(0)
save_window_.size(0,100,-80,300)
scene_vector_[3] = save_window_
{save_window_.view(0, -80, 100, 380, 706, 331, 300.6, 200.8)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addvar("Inh[0].soma.v", 1, 1, 0.8, 0.9, 2)
}
objectvar scene_vector_[1]
{doNotify()}