Preserving axosomatic spiking features despite diverse dendritic morphology (Hay et al., 2013)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:149100
The authors found that linearly scaling the ion channel conductance densities of a reference model with the conductance load in 28 3D reconstructed layer 5 thick-tufted pyramidal cells was necessary to match the experimental statistics of these cells electrical firing properties.
Reference:
1 . Hay E, Schürmann F, Markram H, Segev I (2013) Preserving axosomatic spiking features despite diverse dendritic morphology. J Neurophysiol 109:2972-81 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Axon; Channel/Receptor; Dendrite;
Brain Region(s)/Organism: Neocortex;
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 h; I K,Ca; I Calcium; I A, slow;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Parameter Fitting; Action Potentials; Parameter sensitivity;
Implementer(s): Hay, Etay [etay.hay at mail.huji.ac.il];
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 h; I K,Ca; I Calcium; I A, slow;
/
HayEtAl2013
models
morphologies
readme.html
Ca_HVA.mod *
Ca_LVAst.mod *
CaDynamics_E2.mod *
Ih.mod *
K_Pst.mod *
K_Tst.mod *
Nap_Et2.mod *
NaTg.mod *
SK_E2.mod *
SKv3_1.mod *
mosinit.hoc
mRho.hoc
mRin.hoc
screenshot.png
step_current_firing_scaling.hoc
                            
//Author: Etay Hay, 2013
// Preserving axosomatic spiking features despite diverse dendritic morphology (Hay et al., 2013, J.Neurophysiology)
//
// Calculate the input resistance and conductance load at the soma or axon
// $o1: cell object
// $2: measuring location (0 - soma, 1 - axon)
obfunc mRho(){ local Rin1,Rin2,Rin_total,gin1,gin_total localobj c1,Rvec
	c1 = $o1
	Rvec = new Vector()

	Rin_total = mRin(c1,$2)
	gin_total = 1/Rin_total
  
	if ($2){
		forsec c1.basal delete_section()
		forsec c1.somatic delete_section()
		forsec c1.apical delete_section()
		c1.myelin delete_section()
	} else {
		forsec c1.basal delete_section()
		forsec c1.axonal delete_section()
		forsec c1.apical delete_section()
		c1.myelin delete_section()
	}	

	Rin1 = mRin(c1,$2) //input resistance due to the section of interest (soma or axon)
	gin1 = 1/Rin1
	Rin2 = 1/(gin_total - gin1) //input resistance due to the rest of the cell
	
	Rvec.append((gin_total-gin1)/gin1) //conductance load
	Rvec.append(Rin_total)
	Rvec.append(Rin1)
	Rvec.append(Rin2)
	return (Rvec)  
}

Loading data, please wait...