Voltage- and Branch-specific Climbing Fiber Responses in Purkinje Cells (Zang et al 2018)

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Accession:243446
"Climbing fibers (CFs) provide instructive signals driving cerebellar learning, but mechanisms causing the variable CF responses in Purkinje cells (PCs) are not fully understood. Using a new experimentally validated PC model, we unveil the ionic mechanisms underlying CF-evoked distinct spike waveforms on different parts of the PC. We demonstrate that voltage can gate both the amplitude and the spatial range of CF-evoked Ca2+ influx by the availability of K+ currents. ... The voltage- and branch-specific CF responses can increase dendritic computational capacity and enable PCs to actively integrate CF signals."
Reference:
1 . Zang Y, Dieudonné S, De Schutter E (2018) Voltage- and Branch-Specific Climbing Fiber Responses in Purkinje Cells Cell Reports 24(6):1536-1549 [PubMed]
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: Cerebellum;
Cell Type(s): Cerebellum Purkinje GABA cell;
Channel(s): Ca pump; I K; I K,Ca; I Na,p; I h;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Action Potential Initiation; Active Dendrites; Synaptic Integration; Dendritic Action Potentials; Detailed Neuronal Models;
Implementer(s): Zang, Yunliang ;
Search NeuronDB for information about:  Cerebellum Purkinje GABA cell; I Na,p; I K; I h; I K,Ca; Ca pump;
objectvar save_window_, rvp_
objectvar scene_vector_[8]
objectvar ocbox_, ocbox_list_, scene_, scene_list_
{ocbox_list_ = new List()  scene_list_ = new List()}
{
save_window_ = new Graph(0)
save_window_.size(0,19,-0.008,0.004)
scene_vector_[2] = save_window_
{save_window_.view(0, -0.008, 19, 0.012, 568, 57, 769.92, 344.32)}
graphList[2].append(save_window_)
save_window_.save_name("graphList[2].")
save_window_.addexpr("somaA.ina_naRsg( 0.5 )", 1, 1, 0.8, 0.9, 2)
save_window_.addexpr("somaA.ik_mslo( 0.5 )", 4, 1, 0.8, 0.9, 2)
save_window_.addexpr("somaA.ina_nap( 0.5 )", 2, 1, 0.8, 0.9, 2)
save_window_.addexpr("somaA.ik_SK2( 0.5 )", 3, 1, 0.8, 0.9, 2)
save_window_.addexpr("somaA.i_hpkj( 0.5)", 5, 1, 0.8, 0.9, 2)
save_window_.addexpr("somaA.ik_abBK( 0.5 )", 7, 1, 0.8, 0.9, 2)
save_window_.addexpr("somaA.i_pas( 0.5 )", 7, 1, 0.8, 0.9, 2)
save_window_.addexpr("somaA.ica_newCaP( 0.5 )", 6, 1, 0.8, 0.9, 2)
}
{
save_window_ = new Graph(0)
save_window_.size(0,19,-57.1,-53.8)
scene_vector_[3] = save_window_
{save_window_.view(0, -57.1, 19, 3.3, 567, 470, 773.76, 345.28)}
graphList[3].append(save_window_)
save_window_.save_name("graphList[3].")

save_window_.addexpr("dendA1_0.v( 1 )", 4, 1, 0.8, 0.9, 2)
save_window_.addexpr("dendA1_001011101010100.v( 0.5 )", 4, 1, 0.8, 0.9, 2)
save_window_.addexpr("dendA1_0010111101.v(0.5)", 1, 1, 0.8, 0.9, 2)				//about 80 um far from soma
save_window_.addexpr("dendA1_001011110110010110.v( 0.5 )", 2, 1, 0.8, 0.9, 2)


save_window_.addexpr("dendA1_00110110000011010.v( 0.5 )", 3, 1, 0.8, 0.9, 2)
save_window_.addexpr("dendA1_001101.v( 0.5 )", 4, 1, 0.8, 0.9, 2)

save_window_.addexpr("dendA1_0100100100110110001.v( 0.5 )", 5, 1, 0.8, 0.9, 2)
save_window_.addexpr("dendA1_01001.v( 0.5 )", 6, 1, 0.8, 0.9, 2)
save_window_.addexpr("dendA1_01010101011.v( 0.5 )", 7, 1, 0.8, 0.9, 2)

save_window_.addexpr("somaA.v( 0.5 )", 1, 1, 0.8, 0.9, 2)
save_window_.addexpr("AIS.v( 1 )", 5, 1, 0.8, 0.9, 2)
}
objectvar scene_vector_[1]
{doNotify()}

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