5-neuron-model of neocortex for producing realistic extracellular AP shapes (Van Dijck et al. 2012)

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Accession:226812
This is a 5-neuron model of neocortex, containing one tufted layer-5 pyramidal cell, two non-tufted pyramidal cells, and two inhibitory interneurons. It was used to reproduce extracellular spike shapes in a study comparing algorithms for spike sorting and electrode selection. The neuron models are adapted from Dyhrfjeld-Johnsen et al. (2005).
Reference:
1 . Van Dijck G, Seidl K, Paul O, Ruther P, Van Hulle MM, Maex R (2012) Enhancing the yield of high-density electrode arrays through automated electrode selection. Int J Neural Syst 22:1-19 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Extracellular; Neuron or other electrically excitable cell; Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Neocortex U1 L5B pyramidal pyramidal tract GLU cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: GENESIS;
Model Concept(s):
Implementer(s): Maex, Reinoud [reinoud at bbf.uia.ac.be];
Search NeuronDB for information about:  Neocortex U1 L5B pyramidal pyramidal tract GLU cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell;
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Five-neuron-neocortex
L5P37C-onlybasal
channels
README
Axon_chans.g *
Axon_chans_tab.g
Axon_comps.g
DiffRm.g *
DS1_141099_rot2_sc_defmesh_axon_onlybasal.p
electrodes_fixbug.g
electrodes_try.g *
Excitatory_fibres.g
Fibres.g *
Firing_rate_modulation.g *
Firing_rate_profile.g *
Golgi_grc_multicomp_ax.g
Gran_chan_tab.g *
Gran_comp_soma_dend_axon.g
Gran_const.g *
Gran_synchan.g *
Harsch-Robinson_modulation.g *
Hgradient.g *
Inhibitory_fibres.g *
L5P_ascout.g
L5P_ascout_exp.g *
L5P_chans.g *
L5P_chans_tab.g
L5P_chans_tab_Temp.g *
L5P_chans_Temp.g *
L5P_comps.g *
L5P_comps+axon+syn.g
L5P_const.g *
L5P_const+axon+syn.g *
L5P_graph.g
L5P_history.g
L5P_onlybasal_make.g
L5P_synchan.g
L5P37C_onlybasal.g
nsynapses.g *
                            
// tabchannel indices
float tab_xmin = -0.10
float tab_xmax = 0.05
int tab_xdivs = 149

float cai_min = 1.0
float cai_max = 300


// only used for proto channels
float GNa = 1
float GCa = 1
float GK = 1
float GH = 1

float ENa = 0.050
float ECa = 0.125
float EK = -0.09 
float EH = -0.035

float Temp = 37 // the temperature of the model

float I_inj = -700e-12
str inj_label = "h700pA"
float Q10 = 2.3
float t_sim = 32 // the temperature at which rate constants were obtained

// the factor for converting time constants (not rate constants)
float Temp_corr_factor = {1 / (Q10**{({Temp} - {t_sim}) / 10})}

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


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

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


// Comment by Reinoud Maex, June 2007
// The units are a bit strange; the values are assigned without scaling in L5P_comp.g 
// Because the prototype compartments there all have PI m^2 as surface area, it is better
// to divide here by the same factor to obtain Siemens over square meters as units (and to
// multiply by surface area in L5P_comps).
 
//somatic conductances
float GNaF_s = 4800 / {PI}
float GNaP_s = 0.0032 * {GNaF_s} * {DNaP} // already divided by {PI}
float GKDr_s = 1250 / {PI}
float GKA_s = 300 / {PI}
float GKC_s = 75 * {DKC} / {PI} // / {Temp_corr_factor}
float GKAHP_s = 1.0 / {PI}
float GK2_s = 1.0 / {PI}
float GKM_s = 50.0 * {DKM} / {PI}
float GCaL_s = 5.0 / {PI}  / {Temp_corr_factor}
float GCaT_s = 1.0 / {PI} / {Temp_corr_factor}

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

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

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

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

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


// axonal conductances, from Granule cell model and axon-junction paper
float scaling_f = 314.15 / 2012.67 // surface soma over surface full Gabbiani model,
// scaling needed because active channels only on soma in Gabbiani model

float GInNa_a = 2.5       \  //   correction for 10 mV shift of (in-)activation rates
             * 2         \  //   correction for 37 deg. Celsius
             * scaling_f \  //   conversion to single compartment
             * 10        \  //   conversion to SI units (S/m^2)
             * 70           //   the Gabbiani value
float GKDr_a   = 1.5  * 2 * scaling_f * 10 * 19


/* preset constants */
// Ek value used for the leak conductance
float EREST_ACT = -0.0650
// !!Change!!Ek value used for the RESET
float RESET_ACT = -0.0650
float ELEAK = -0.07 // -0.065 // -0.07 // -0.0650


/* cable parameters */
float CM = 0.01
float RMs =  3.0300
float RA = 1.0 
// CAVE : the values of CM, RMs and RA are overwritten by the cell
// description file 


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



//New synaptic parameters copied from Purkinje cell model
//for GABAA and AMPA channels 
float E_GABA = -0.08 // -0.07 // -0.080  // CAVE was -0.07 in granule cell model
float G_GABA = 70.0  // this value is reset in L5P_synchan.g
float GB_GABA = 20.0
float GB_GABAs = 20.0
float G_GABAm, G_GABAs
float E_AMPA = 0.000
float G_par_syn = 750.0 // this density gives on PC spines a peak conductance of 1.3 nS


//New synaptic parameters copied from granule cell model
//for NMDA and GABAB channels
float E_NMDA = 0.0
float G_NMDA =  1200.0e-12 / 2012.67e-12   // the Gabbiani value expressed as conductance density
float E_GABAB = -0.070
float G_GABAB = 1.0 
float GNMDAs = G_NMDA
float G_GABABs = G_GABAB



// synaptic connection parameters

float E_fibre_rate = 5  // spikes per second
float I_fibre_rate = 5  // spikes per second

float weight_AMPA_synapse = 1.0
float weight_GABA_synapse = 1.0
float weight_distribution = 0.0

float E_fibre_conduction_velocity = 1.0   // meter per second
float I_fibre_conduction_velocity = 1.0   // meter per second
float delay_distribution = 0.002











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