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
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README.html
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screenshot.png
simseedburst_func_nonparallel_nonadaptive_allions.py
                            
# Python file for drawing results from network (N=150) simulations
# Tuomo Maki-Marttunen, 2015-2016
import mytools
from pylab import *
import pickle

Nmc = 150
gSynCoeffs = [1.1, 1.25, 1.4]

spikers_all_all = []
spts_all_all = []
cumFRs_all_all = []
dtFR = 5.0
T = 10200
cols = ['#0000FF','#FF0000','#B3B300']
dimcols = ['#AAAAFF','#FFAAAA','#E5E5AA']

for igsyn in range(0,3):
  spikers_all = []
  spts_all = []
  cumFRs_all = []
  gSynCoeff = gSynCoeffs[igsyn]
  for myseed in range(1,15):
    unpicklefile = open('spikes_nonadaptive_'+str(Nmc)+'_gsyn'+str(gSynCoeff)+'_seed'+str(myseed)+'.sav', 'r')
    unpickledlist = pickle.load(unpicklefile)
    unpicklefile.close()
    spikes = unpickledlist[:]
    spts_all.append(spikes[0])
    spikers_all.append(spikes[1])
    cumFRs_this = [sum([1 for x in spikes[0] if x <= i*dtFR]) for i in range(0,int(T/dtFR))]
    cumFRs_all.append(cumFRs_this[:])
    print "myseed = "+str(myseed)+" analyzed"
  spts_all_all.append(spts_all[:])
  spikers_all_all.append(spikers_all[:])
  cumFRs_all_all.append(cumFRs_all[:])

close("all")
f,axarr = subplots(2,1)
for igsyn in range(0,3):
  axarr[0].plot(spts_all_all[igsyn][0],[150*igsyn+x for x in spikers_all_all[igsyn][0]],'r.',color=cols[igsyn],markersize=1.0)
  for isamp in range(0,14):
    axarr[1].plot([i*dtFR for i in range(0,int(T/dtFR))], cumFRs_all_all[igsyn][isamp], 'r-', color=dimcols[igsyn])
for igsyn in range(0,3):
  axarr[1].plot([i*dtFR for i in range(0,int(T/dtFR))], [mean([cumFRs_all_all[igsyn][isamp][i] for isamp in range(0,14)]) for i in range(0,len(cumFRs_all_all[igsyn][0]))], 'r-', color=cols[igsyn],linewidth=2)

axarr[0].set_xlim([0,10000])
axarr[1].set_xlim([0,10000])
f.savefig("cumFRs.eps")

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