Pyramidal Neuron Deep: Constrained by experiment (Dyhrfjeld-Johnsen et al. 2005)

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Accession:93349
"... As a practical demonstration of the use of CoCoDat we constructed a detailed computer model of an intrinsically bursting (IB) layer V pyramidal neuron from the rat barrel cortex supplementing experimental data (Schubert et al., 2001) with information extracted from the database. The pyramidal neuron morphology (Fig. 10B) was reconstructed from histological sections of a biocytin-stained IB neuron using the NeuroLucida software package..."
Reference:
1 . Dyhrfjeld-Johnsen J, Maier J, Schubert D, Staiger J, Luhmann HJ, Stephan KE, K├Âtter R (2005) CoCoDat: a database system for organizing and selecting quantitative data on single neurons and neuronal microcircuitry. J Neurosci Methods 141:291-308 [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:
Cell Type(s): Neocortex L5/6 pyramidal GLU cell;
Channel(s): I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: GENESIS;
Model Concept(s): Bursting;
Implementer(s): Dyhrfjeld-Johnsen, Jonas [jdyhrfje at uci.edu];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca;
float I_inj = -700e-12
str inj_label = "h700pA"
float Q10 = 2.3
float t_sim = 32

//Base H-current conductances before distribution
float GH_s = 0.15
float GH_d = 0.15

//Synaptic parameters
float G_Glu = -0.75e-8
float Glu_tau1 = 5.0e-3
float Glu_tau2 = 30.0e-3


//Ca-pool parameters
float B = 5.2e4
float CaTau_s = 100.0e-3
float CaTau_d = 20.0e-3

//Conductance scaling parameters
float DNaP = 1.0
float DKC = 0.30
float DKM = 0.56

//somatic conductances
float GNaF_s = 4800
float GNaP_s = 0.0032 * {GNaF_s} * {DNaP}
float GKDr_s = 1250
float GKA_s = 300
float GKC_s = 75 * {DKC}
float GKAHP_s = 1.0
float GK2_s = 1.0
float GKM_s = 50.0 * {DKM}
float GCaL_s = 5.0
float GCaT_s = 1.0

//Apical shaft
float GNaF_shaft = 350
float GKA_shaft = 300.0
float GNaP_shaft = 0.0032 * {GNaF_shaft} * {DNaP}
float GKDr_shaft = 350
float GKC_shaft = 75 * {DKC}
float GKAHP_shaft = 1.0
float GK2_shaft = 1.0
float GKM_shaft = 50.0 * {DKM}
float GCaL_shaft = 3.0
float GCaT_shaft = 1.0

//basal dendritic conductances
float GNaF_bd = 350
float GNaP_bd = 0.0032 * {GNaF_bd} * {DNaP}
float GKDr_bd = 350
float GKA_bd = 20.0
float GKC_bd = 75 * {DKC}
float GKAHP_bd = 1.0
float GK2_bd = 1.0
float GKM_bd = 50.0 * {DKM}
float GCaL_bd = 3.0
float GCaT_bd = 1.0

//proximal apical dendritic conductances
float GNaF_pad = 350
float GNaP_pad = 0.0032 * {GNaF_pad} * {DNaP}
float GKDr_pad = 350
float GKA_pad = 20.0
float GKC_pad = 75 * {DKC}
float GKAHP_pad = 1.0
float GK2_pad = 1.0
float GKM_pad = 50.0 * {DKM}
float GCaL_pad = 3.0
float GCaT_pad = 1.0

//medial apical dendritic conductances
float GNaF_mad = 350
float GNaP_mad = 0.0032 * {GNaF_mad} * {DNaP}
float GKDr_mad = 350
float GKA_mad = 20.0
float GKC_mad = 75 * {DKC}
float GKAHP_mad = 1.0
float GK2_mad = 1.0
float GKM_mad = 43.0 * {DKM}
float GCaL_mad = 3.0
float GCaT_mad = 1.0

//distal conductances
float GNaF_dd = 62.5
float GNaP_dd = 0.0032 * {GNaF_dd} * {DNaP}
float GKDr_dd = 0.0
float GKA_dd = 20
float GKC_dd = 0
float GKAHP_dd = 1.0
float GK2_dd = 1.0
float GKM_dd = 9.25
//float GCaL_dd = 15.0
float GCaL_dd = 15.0
float GCaT_dd = 1.0













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