Frog second-order vestibular neuron models (Rossert et al. 2011)

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Accession:139654
This implements spiking Hodgkin-Huxley type models of tonic and phasic second-order vestibular neurons. Models fitted to intracellular spike and membrane potential recordings from frog (Rana temporaria). The models can be stimulated by intracellular step current, frequency current (ZAP) or synaptic stimulation.
Reference:
1 . Rossert C, Straka H, Moore LE, Glasauer S (2011) Cellular and network contributions to vestibular signal processing: impact of ion conductances, synaptic inhibition and noise. J Neurosci 31:8359-8372
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): Vestibular neuron; Abstract Morris-Lecar neuron;
Channel(s): I T low threshold; I K,Ca; I Sodium; I Potassium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Glycine; Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Simplified Models; Action Potentials; Sensory processing; Vestibular;
Implementer(s): Roessert, Christian [christian.a at roessert.de];
Search NeuronDB for information about:  I T low threshold; I K,Ca; I Sodium; I Potassium; Glycine; Gaba; Glutamate;
p_N=  9.00000000 
p_El=-70.00000000 
p_Els=-70.00000000 
p_GNa=  0.10200000 
p_vsh=-70.00000000 
p_tfac=  1.00000000 
p_GK=  0.01300000 
p_bn=-20.00000000 
p_gn= 10.00000000 
p_tn=  4.60000000 
p_GKCa=  0.00000000 
p_tca=  1.00000000 
objref vp_rho, vp_Le, vp_CS, vp_GS, vp_GSh, vp_GKd
vp_rho=new Vector() 
vp_rho.append(4.472,4.6762,2.5427,8.838,2.2172,5.2085,9.0397,6.2,6.1298,9.4523,9.7313,2.2582,2.4814,5.1809,8.6484,8.7486,9.21)
vp_Le=new Vector() 
vp_Le.append(1.6564,0.85601,0.78641,1.4162,1.0459,0.73418,1.4089,0.745,0.70056,0.73318,1.0911,1.1669,1.1526,0.77224,0.87994,0.8123,1.1015)
vp_CS=new Vector() 
vp_CS.append(39.3917,27.1394,32.3237,38.7637,23.2813,22.1987,41.6386,46.6,12.259,36.5968,56.8492,48.8234,21.7533,61.7947,74.7665,37.6842,22.9892)
vp_GS=new Vector() 
vp_GS.append(1.9573,5.9531,4.9468,0.37948,9.0609,0.43098,3.4112,2.9,2.1638,0.47101,1.3213,19.4561,3.9855,3.9902,0.89702,0.90392,1.3245)
vp_GSh=new Vector() 
vp_GSh.append(33.9221,46.0639,14.0236,6.4657,29.7362,33.7974,31.6237,21.1,25.6834,42.2308,3.0851,0.8345,14.4792,9.8861,28.5546,36.8054,42.3257)
vp_GKd=new Vector() 
vp_GKd.append(0.0034259,0.0014917,0.0027094,0.0047376,0.00070788,0.0016619,0.004402,0.00257,0.0070778,0.0012334,0.0081855,0.0019715,0.0019545,0.0017332,0.001856,0.00084843,0.00074648)


t_N=  9.00000000 
t_El=-70.00000000 
t_Els=-70.00000000 
t_vsh=-70.00000000 
t_bn=-20.00000000 
t_gn= 10.00000000 
t_GKd=  0.00000000 
t_tfac=  1.00000000 
objref vt_rho, vt_Le, vt_CS, vt_GS, vt_GSh, vt_GNa, vt_GK, vt_tn, vt_GKCa, vt_tca 
vt_rho=new Vector() 
vt_rho.append(6.2157,6.8604,7.8941,8.4592,9.3248,6.4137,5.1405)
vt_Le=new Vector() 
vt_Le.append(1.0496,0.6722,0.97511,1.2165,1.4985,0.90818,0.74583)
vt_CS=new Vector() 
vt_CS.append(84.2765,93.13,118.1209,75.7611,111.4739,62.8039,100.2423)
vt_GS=new Vector() 
vt_GS.append(0.58696,0.87289,1.6431,0.95161,0.17143,0.67686,2.1585)
vt_GSh=new Vector() 
vt_GSh.append(14.945,35.69,36.4586,31.5504,30.6639,17.0598,21.3729)
vt_GNa=new Vector() 
vt_GNa.append(0.073665,0.10845,0.097778,0.12534,0.089723,0.10605,0.11193)
vt_GK=new Vector() 
vt_GK.append(0.016254,0.012751,0.01291,0.0092623,0.017703,0.0096224,0.014521)
vt_tn=new Vector() 
vt_tn.append(7.1885,3.1097,4.107,2.8634,6.3744,6.0135,2.6181)
vt_GKCa=new Vector() 
vt_GKCa.append(0.073391,0.12348,0.10471,0.10636,0.2399,0.17131,0.15604)
vt_tca=new Vector() 
vt_tca.append(58.5757,51.084,44.3175,46.0411,37.7094,65.1824,42.1696)


t_tau1E=  5.00000000 
t_tau2E= 50.00000000 
t_GE=  0.009

p_tau1E=  5.00000000 
p_tau2E= 50.00000000 
p_GE=  0.035

tau1In=  5.00000000 
tau2In=150.00000000 
GIn=  0.020/7

EIn=-75.00000000 


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