Layer V pyramidal cell model with reduced morphology (Mäki-Marttunen et al 2018)

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Accession:187474
" ... In this work, we develop and apply an automated, stepwise method for fitting a neuron model to data with fine spatial resolution, such as that achievable with voltage sensitive dyes (VSDs) and Ca2+ imaging. ... We apply our method to simulated data from layer 5 pyramidal cells (L5PCs) and construct a model with reduced neuronal morphology. We connect the reduced-morphology neurons into a network and validate against simulated data from a high-resolution L5PC network model. ..."
References:
1 . Hay E, Hill S, Schürmann F, Markram H, Segev I (2011) Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties. PLoS Comput Biol 7:e1002107 [PubMed]
2 . Hay E, Segev I (2015) Dendritic Excitability and Gain Control in Recurrent Cortical Microcircuits. Cereb Cortex 25:3561-71 [PubMed]
3 . Mäki-Marttunen T, Halnes G, Devor A, Metzner C, Dale AM, Andreassen OA, Einevoll GT (2018) A stepwise neuron model fitting procedure designed for recordings with high spatial resolution: Application to layer 5 pyramidal cells. J Neurosci Methods 293:264-283 [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: 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 M; I h; I K,Ca; I Calcium; I A, slow;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; NEURON (web link to model); Python; NeuroML;
Model Concept(s):
Implementer(s): Maki-Marttunen, Tuomo [tuomo.maki-marttunen at tut.fi]; Metzner, Christoph [c.metzner at herts.ac.uk];
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 M; I h; I K,Ca; I Calcium; I A, slow;
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reducedhaymodel
snmf
models
morphologies
README.html
Ca_HVA.mod *
Ca_LVAst.mod *
CaDynamics_E2.mod *
epsp.mod *
Ih.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
Nap_Et2.mod *
NaTa_t.mod *
SK_E2.mod *
SKv3_1.mod *
drawfits_withmids_combfs.py
drawfits_withmids_combfs_nseg5.py
drawmorphs.py
drawobjective_evolution.py
emoo.py
mytools.py
originalrun.sav
pars_withmids_combfs_1a.sav
pars_withmids_combfs_1a_0a.sav
pars_withmids_combfs_1a_0a_5a.sav
pars_withmids_combfs_1a_0a_5a_5a.sav
pars_withmids_combfs_final.sav *
pars_withmids_combfs_fixed_final.sav
screenshot.png
snmf_protocols.py
snmf_target.py
snmf_withmids_combfs.py
                            
: SK-type calcium-activated potassium current
: Reference : Kohler et al. 1996

NEURON {
       SUFFIX SK_E2
       USEION k READ ek WRITE ik
       USEION ca READ cai
       RANGE gSK_E2bar, gSK_E2, ik, offc, zTau, sloc
}

UNITS {
      (mV) = (millivolt)
      (mA) = (milliamp)
      (mM) = (milli/liter)
}

PARAMETER {
          v            (mV)
          gSK_E2bar = .000001 (mho/cm2)
          zTau = 1              (ms)
          ek           (mV)
          cai          (mM)
          offc = 0.00043 (mM)
	  sloc = 4.8
}

ASSIGNED {
         zInf
         ik            (mA/cm2)
         gSK_E2	       (S/cm2)
}

STATE {
      z   FROM 0 TO 1
}

BREAKPOINT {
           SOLVE states METHOD cnexp
           gSK_E2  = gSK_E2bar * z
           ik   =  gSK_E2 * (v - ek)
}

DERIVATIVE states {
        rates(cai)
        z' = (zInf - z) / zTau
}

PROCEDURE rates(ca(mM)) {
          if(ca < 1e-7){
	              ca = ca + 1e-07
          }
          zInf = 1/(1 + (offc / ca)^sloc)
}

INITIAL {
        rates(cai)
        z = zInf
}

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