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
single_cell
models
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 *
fullhay_run_1.dat
fullhay_run_2.dat
fullhay_run_3.dat
fullhay_run_3a.dat
mosinit.hoc
run_ctrl_vgraph.ses
runmodel.hoc
runmodel.py
screenshot.png
                            
from neuron import h
from pylab import *

h("""load_file("runmodel.hoc")""")

filenames = ['run_1.dat', 'run_2.dat', 'run_3.dat', 'run_3a.dat']
titles = ['Somatic pulse', 'Apical pulse', 'BAC firing', 'No BAC firing (somatic only)', 'No BAC firing (apical only)']

f,ax = subplots((len(filenames)+1)/2, 2)
cols = ['#FF0000','#0000FF']
extensions = ['', 'fullhay_']
for ifile in range(0,len(filenames)):
  for iextension in range(0,len(extensions)):
    extension = extensions[iextension]
    ts = []
    vs = []
    vdends = []
    with open(extension+filenames[ifile], 'r') as file:        
      for row in file:
        t,v,vdend = row.split()
        ts.append(float(t))
        vs.append(float(v))
        vdends.append(float(vdend))
    ax[ifile/2, ifile%2].plot(ts,vs,color=cols[iextension],linestyle='-')
    l,=ax[ifile/2, ifile%2].plot(ts,vdends,color=cols[iextension],linestyle='--')
    l.set_dashes([2,2,2,2])
  ax[ifile/2, ifile%2].set_xlim([10000,10500])
  ax[ifile/2, ifile%2].set_ylim([-90,40])
  ax[ifile/2, ifile%2].set_xlabel('t (ms)',fontsize=6)
  ax[ifile/2, ifile%2].set_ylabel('V (mV)',fontsize=6)
  ax[ifile/2, ifile%2].set_title(titles[ifile],fontsize=6)
  for tick in ax[ifile/2, ifile%2].yaxis.get_major_ticks()+ax[ifile/2, ifile%2].xaxis.get_major_ticks():
    tick.label.set_fontsize(6)
  if ifile==1:
    ax[0,1].legend(['Soma, reduced morphology','Apical dend, reduced morphology', 'Soma, full morphology','Apical dend, full morphology'],fontsize=6)
f.savefig("runs.eps")



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