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

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" ... 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. ..."
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:
Simulation Environment: NEURON; NEURON (web link to model); Python; NeuroML;
Model Concept(s):
Implementer(s): Maki-Marttunen, Tuomo [tuomo.maki-marttunen at]; Metzner, Christoph [c.metzner at];
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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SKv3_1.mod *
pars_withmids_combfs_final.sav *
# approxhaynetstuff_nonparallel
# A Python library for setting the (non-synaptic) parameters of the model network.
# Assumes a parallelized simulation, where the L5PCs are labeled as "epnm.pc.gid2cell(i)", i from 0 to Nmc-1
# Tuomo Maki-Marttunen, 2015-2016
from neuron import h
import matplotlib
import numpy
from pylab import *

# params: a dictionary of model parameters
# Nmc: the number of cells
def setparams(params,Nmc):
  global dists_apical, dists_basal

  keys = params.keys()
  lengthChanged = False
  for ikey in range(0,len(keys)):
    key = keys[ikey]
    if key[0:2] == "L_":
      lengthChanged = True
    underscoreind = key.rfind('_')
    section = key[underscoreind+1:len(key)]
    if section == "*":
      for i in range(0,Nmc):
i = """+str(i)+"""
if (epnm.gid_exists(i)) {
  forsec epnm.pc.gid2cell(i).all """+key[0:underscoreind]+""" = """+str(params[key])+"""
      h("forall "+key[0:underscoreind]+" = "+str(params[key]))
      for i in range(0,Nmc):
i = """+str(i)+"""
if (epnm.gid_exists(i)) {
  epnm.pc.gid2cell(i)."""+section+""" """+key[0:underscoreind]+""" = """+str(params[key])+"""

  if lengthChanged:
    for i in range(0,Nmc):
i = """+str(i)+"""
if (epnm.gid_exists(i)) {
    epnm.pc.gid2cell(i).soma diam = """+str(360.132/params['L_soma'])+"""
    epnm.pc.gid2cell(i).dend diam = """+str(2821.168/params['L_dend'])+"""
    epnm.pc.gid2cell(i).apic[0] diam = """+str(4244.628/params['L_apic[0]'])+"""
    epnm.pc.gid2cell(i).apic[1] diam = """+str(2442.848/params['L_apic[1]'])+"""

    lengthA = 0
    lengthB = 0
    forsec epnm.pc.gid2cell(i).apical {
      lengthA = lengthA + L
    forsec epnm.pc.gid2cell(i).basal {
      lengthB = lengthB + L
    epnm.pc.gid2cell(i).pA = lengthA/(lengthA + lengthB)

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