Layer 5 Pyramidal Neuron (Shai et al., 2015)

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Accession:180373
This work contains a NEURON model for a layer 5 pyramidal neuron (based on Hay et al., 2011) with distributed groups of synapses across the basal and tuft dendrites. The results of that simulation are used to fit a phenomenological model, which is also included in this file.
Reference:
1 . Shai AS, Anastassiou CA, Larkum ME, Koch C (2015) Physiology of layer 5 pyramidal neurons in mouse primary visual cortex: coincidence detection through bursting. PLoS Comput Biol 11:e1004090 [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):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Dendritic Action Potentials; Active Dendrites;
Implementer(s):
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; Glutamate;
// This is a new biophysics for use in the Shai et al. (2015) PLoS Computational
// Biology Paper


// Author: Etay Hay, 2011
//    Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of
//    Dendritic and Perisomatic Active Properties
//    (Hay et al., PLoS Computational Biology, 2011) 
//
// Model of L5 Pyramidal Cell, constrained both for BAC firing and Current Step Firing
//    with AP initiation in the axon


begintemplate L5PCbiophys
public biophys

proc biophys() {
	forsec $o1.all {
	  insert pas
		cm = 1
		Ra = 100
		e_pas = -90
	}

  forsec $o1.axonal {
  	insert Im 
  	insert Ca_LVAst 
  	insert Ca_HVA
  	insert CaDynamics_E2 
  	insert SKv3_1 
  	insert SK_E2 
  	insert K_Tst 
  	insert K_Pst 
  	insert Nap_Et2 
  	insert NaTa_t 
		ek = -85
		ena = 50
    insert Ih
  	gIhbar_Ih = 0.0001/2
		g_pas = 3e-5
  	gImbar_Im = 0.013322 
  	decay_CaDynamics_E2 = 277.300774 
  	gamma_CaDynamics_E2 = 0.000525 
  	gCa_LVAstbar_Ca_LVAst = 0.000813 
  	gCa_HVAbar_Ca_HVA = 0.000222 
  	gSKv3_1bar_SKv3_1 = 0.473799 
  	gSK_E2bar_SK_E2 = 0.000047 
  	gK_Tstbar_K_Tst = 0.077274 
  	gK_Pstbar_K_Pst = 0.188851 
  	gNap_Et2bar_Nap_Et2 = 0.005834 
  	gNaTa_tbar_NaTa_t = 3.89618 
	}

  forsec $o1.somatic {
  	insert Im 
  	insert Ca_LVAst 
  	insert Ca_HVA
  	insert CaDynamics_E2 
  	insert SK_E2 
  	insert SKv3_1 
  	insert NaTs2_t 
		ek = -85
		ena = 50
    insert Ih
  	gIhbar_Ih = 0.0001*0.75
		g_pas = 3e-5
  	gImbar_Im = 0.000008 
  	decay_CaDynamics_E2 = 294.679571 
  	gamma_CaDynamics_E2 = 0.000509 
  	gCa_LVAstbar_Ca_LVAst = 0.000557 
  	gCa_HVAbar_Ca_HVA = 0.000644 
  	gSK_E2bar_SK_E2 = 0.09965 
  	gSKv3_1bar_SKv3_1 = 0.338029 
  	gNaTs2_tbar_NaTs2_t = 0.998912 
  }

	forsec $o1.apical {
    insert CaDynamics_E2 
  	insert SK_E2 
  	insert Ca_LVAst 
  	insert Ca_HVA 
  	insert SKv3_1 
  	insert NaTs2_t 
  	insert Im 
    insert Ih
		ek = -85
		ena = 50
		cm = 2
		g_pas = 6e-5
  	decay_CaDynamics_E2 = 35.725651 
  	gamma_CaDynamics_E2 = 0.000637 
  	gSK_E2bar_SK_E2 = 0.000002 
  	gCa_HVAbar_Ca_HVA = 0.000701 
  	gSKv3_1bar_SKv3_1 = 0.001808 
  	gNaTs2_tbar_NaTs2_t = 0.021489
  	gImbar_Im = 0.00099 
  	gIhbar_Ih =  .00015 //0.00001*1.5
	}

  $o1.distribute_channels("apic","gIhbar_Ih",2,-0.8696,3.6161,0.0,2.0870,0.00010000000) 
  $o1.distribute_channels("apic","gCa_LVAstbar_Ca_LVAst",3,1.000000,0.010000,685.000000,885.000000,0.1419540000*1.6) 
	
  forsec $o1.basal {
    insert Ih
  	gIhbar_Ih = 0.0001/2
		cm = 2
		g_pas = 6e-5
	}
}

endtemplate L5PCbiophys

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