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-notuft
channels
README
Axon_chans.g *
Axon_chans_tab.g
Axon_comps.g
DiffRm.g *
DS1_141099_rot2_sc_defmesh_axon_notuft.p
electrodes_fixbug.g *
electrodes_try.g *
Excitatory_fibres.g *
Fibres.g *
Firing_rate_modulation.g *
Firing_rate_profile.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.g
L5P_comps+axon+syn.g
L5P_const.g *
L5P_const+axon+syn.g *
L5P_graph.g
L5P_history.g *
L5P_notuft_make.g
L5P_synchan.g
L5P37C_notuft.g
nsynapses.g *
test_position.g *
                            
//genesis - function

//Function setting differential Rm for compartments based on distance from soma

function DiffRm(cellname, Rm_base, Rm_end, d_half, steep)
  float Rm_base, Rm_end, Rm_new, d_half, steep
  str cellname
  str wild_path = {{cellname} @ "/##[][TYPE=compartment]"}
  str comp_path
  str soma_path = {{cellname} @ "/soma"}
  float x0 = {getfield {soma_path} x}
  float y0 = {getfield {soma_path} y}
  float z0 = {getfield {soma_path} z}
  float x,y,z,len,dia,surf,dist

  foreach comp_path ({el {wild_path}})
     x = {getfield {comp_path} x}
     y = {getfield {comp_path} y}
     z = {getfield {comp_path} z}
     dist = {sqrt {((x-x0)**2)+((y-y0)**2)+((z-z0)**2)}}
     Rm_new = Rm_end + ((Rm_base - Rm_end) / (1 + {exp {((d_half - dist) / - steep)}}))
     len = {getfield {comp_path} len}
     dia = {getfield {comp_path} dia}
     surf = 3.14159*dia*len
     
     if (comp_path != soma_path)
        setfield {comp_path} Rm {Rm_new/surf}
     end

  end
end


     




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