Effect of ionic diffusion on extracellular potentials (Halnes et al 2016)

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Accession:225311
"Recorded potentials in the extracellular space (ECS) of the brain is a standard measure of population activity in neural tissue. Computational models that simulate the relationship between the ECS potential and its underlying neurophysiological processes are commonly used in the interpretation of such measurements. Standard methods, such as volume-conductor theory and current-source density theory, assume that diffusion has a negligible effect on the ECS potential, at least in the range of frequencies picked up by most recording systems. This assumption remains to be verified. We here present a hybrid simulation framework that accounts for diffusive effects on the ECS potential. ..."
Reference:
1 . Halnes G, Mäki-Marttunen T, Keller D, Pettersen KH, Andreassen OA, Einevoll GT (2016) Effect of Ionic Diffusion on Extracellular Potentials in Neural Tissue. PLoS Comput Biol 12:e1005193 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Extracellular; Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Neocortex U1 L6 pyramidal corticalthalamic cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB; NEURON;
Model Concept(s): Extracellular Fields;
Implementer(s): Halnes, Geir [geir.halnes at nmbu.no]; Maki-Marttunen, Tuomo [tuomo.maki-marttunen at tut.fi];
Search NeuronDB for information about:  Neocortex U1 L6 pyramidal corticalthalamic cell;
// 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


begintemplate L5PCbiophys
public biophys

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

  forsec $o1.somatic {
	  insert Ca_LVAst 
	  insert Ca_HVA 
	  insert SKv3_1 
	  insert SK_E2 
	  insert K_Tst 
	  insert K_Pst 
	  insert Nap_Et2 
	  insert NaTa_t
		insert CaDynamics_E2
		insert Ih
		ek = -85
		ena = 50
		gIhbar_Ih = 0.0002
    g_pas = 0.0000338 
  	decay_CaDynamics_E2 = 460.0 
  	gamma_CaDynamics_E2 = 0.000501 
  	gCa_LVAstbar_Ca_LVAst = 0.00343 
  	gCa_HVAbar_Ca_HVA = 0.000992 
  	gSKv3_1bar_SKv3_1 = 0.693 
  	gSK_E2bar_SK_E2 = 0.0441 
  	gK_Tstbar_K_Tst = 0.0812 
  	gK_Pstbar_K_Pst = 0.00223 
  	gNap_Et2bar_Nap_Et2 = 0.00172 
  	gNaTa_tbar_NaTa_t = 2.04 
  }

	forsec $o1.apical {
		cm = 2
		insert Ih
  	insert SK_E2 
  	insert Ca_LVAst 
  	insert Ca_HVA 
  	insert SKv3_1 
  	insert NaTa_t 
  	insert Im 
  	insert CaDynamics_E2
		ek = -85
		ena = 50
    decay_CaDynamics_E2 = 122 
    gamma_CaDynamics_E2 = 0.000509 
    gSK_E2bar_SK_E2 = 0.0012 
  	gSKv3_1bar_SKv3_1 = 0.000261 
  	gNaTa_tbar_NaTa_t = 0.0213 
  	gImbar_Im = 0.0000675 
  	g_pas = 0.0000589 
	}
	$o1.distribute_channels("apic","gIhbar_Ih",2,-0.8696,3.6161,0.0,2.0870,0.00020000000) 
	$o1.distribute_channels("apic","gCa_LVAstbar_Ca_LVAst",3,1.000000,0.010000,685.000000,885.000000,0.0187000000) 
	$o1.distribute_channels("apic","gCa_HVAbar_Ca_HVA",3,1.000000,0.100000,685.000000,885.000000,0.0005550000) 
	
  forsec $o1.basal {
		cm = 2
		insert Ih
		gIhbar_Ih = 0.0002
  	g_pas = 0.0000467 
	}

  forsec $o1.axonal {
  	g_pas = 0.0000325 
	}
}

endtemplate L5PCbiophys

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