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
network
models
README.html
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Ca_LVAst.mod *
CaDynamics_E2.mod *
Ih.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
Nap_Et2.mod *
NaTa_t.mod *
ProbAMPANMDA2.mod
ProbAMPANMDA2group.mod
ProbAMPANMDA2groupdet.mod
ProbUDFsyn2.mod *
ProbUDFsyn2group.mod
ProbUDFsyn2groupdet.mod
SK_E2.mod *
SKv3_1.mod *
approxhaynetstuff.py
approxhaynetstuff_nonparallel.py
calculate_spike_trains.py
drawcumfr.py
mytools.py
pars_withmids_combfs_final.sav *
screenshot.png
simseedburst_func_nonparallel_nonadaptive_allions.py
                            
# Python file for running the network (N=150) simulations and saving the resulting spike trains
# Tuomo Maki-Marttunen, 2015-2016
import mytools
import simseedburst_func_nonparallel_nonadaptive_allions
from pylab import *
from neuron import h
import pickle
import sys

Nmc = 150
gSynCoeffs = [1.1, 1.25, 1.4]
gNoiseCoeff = 1.11
counter = -1
for igsyn in range(0,3):
  for myseed in range(1,15):
    counter = counter + 1
    if len(sys.argv) > 1 and int(sys.argv[1]) != counter:
      continue
    gSynCoeff = gSynCoeffs[igsyn]
    Q = simseedburst_func_nonparallel_nonadaptive_allions.simseedburst_func(Nmc, 11000, myseed, 0.0004, 0.001, 5, 1.0, gNoiseCoeff, gSynCoeff)
    picklelist = Q[2][:]
    file = open('spikes_nonadaptive_'+str(Nmc)+'_gsyn'+str(gSynCoeff)+'_seed'+str(myseed)+'.sav', 'w')
    pickle.dump(picklelist,file)
    file.close()

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