Computer models of corticospinal neurons replicate in vitro dynamics (Neymotin et al. 2017)

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Accession:195615
"Corticospinal neurons (SPI), thick-tufted pyramidal neurons in motor cortex layer 5B that project caudally via the medullary pyramids, display distinct class-specific electrophysiological properties in vitro: strong sag with hyperpolarization, lack of adaptation, and a nearly linear frequency-current (FI) relationship. We used our electrophysiological data to produce a pair of large archives of SPI neuron computer models in two model classes: 1. Detailed models with full reconstruction; 2. Simplified models with 6 compartments. We used a PRAXIS and an evolutionary multiobjective optimization (EMO) in sequence to determine ion channel conductances. ..."
Reference:
1 . Neymotin SA, Suter BA, Dura-Bernal S, Shepherd GM, Migliore M, Lytton WW (2017) Optimizing computer models of corticospinal neurons to replicate in vitro dynamics. J Neurophysiol 117:148-162 [PubMed]
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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 M1 L5B pyramidal pyramidal tract GLU cell; Neocortex primary motor area pyramidal layer 5 corticospinal cell;
Channel(s): I A; I h; I_KD; I K,Ca; I L high threshold; I Na,t; I N; Ca pump; Kir;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Parameter Fitting; Activity Patterns; Active Dendrites; Detailed Neuronal Models; Simplified Models;
Implementer(s): Suter, Benjamin ; Neymotin, Sam [samn at neurosim.downstate.edu]; Dura-Bernal, Salvador [salvadordura at gmail.com]; Forzano, Ernie ;
Search NeuronDB for information about:  Neocortex M1 L5B pyramidal pyramidal tract GLU cell; I Na,t; I L high threshold; I N; I A; I h; I K,Ca; I_KD; Ca pump; Kir;
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spidemo
data
readme.html
cadad.mod
cal2.mod
can_mig.mod
h_kole.mod
kap_BS.mod
kBK.mod
kdmc_BS.mod
kdr_BS.mod
misc.mod *
nax_BS.mod
savedist.mod
vecst.mod *
archfig.py
axonMorph.py
BS0284.ASC
BS0409.ASC
conf.py
Fig6.py
figure_1.png
misc.h
morph.py
mosinit.py
PTcell.BS0284.cfg *
PTcell.BS0409.cfg
PTcell.cfg *
sim.py
SPI6.cfg
SPI6.py
utils.py
                            
[fixed]
SPI6.gbar_h = 0.000140956438043 0.000140956438043 0.000140956438043 True
SPI6.apicCap = 1.03418636866 1.03418636866 1.03418636866 True
SPI6.somaDiam = 28.2149102762 28.2149102762 28.2149102762 True
SPI6.somaL = 48.4123467666 48.4123467666 48.4123467666 True
SPI6.bdendDiam = 2.2799248874 2.2799248874 2.2799248874 True
SPI6.apicRM = 10751.193413 10751.193413 10751.193413 True
SPI6.bdendL = 299.810775175 299.810775175 299.810775175 True
SPI6.bdendCap = 1.89771901209 1.89771901209 1.89771901209 True
SPI6.bdendRM = 13123.00174 13123.00174 13123.00174 True
SPI6.somaCap = 1.78829677463 1.78829677463 1.78829677463 True
SPI6.Vrest = -88.5366550238 -88.5366550238 -88.5366550238 True
SPI6.apicL = 261.904636003 261.904636003 261.904636003 True
SPI6.somaRM = 18501.7540916 18501.7540916 18501.7540916 True
h.v_init = -75.0413649414 -75.0413649414 -75.0413649414 True
SPI6.axonDiam = 1.40966286462 1.40966286462 1.40966286462 True
SPI6.axonCap = 1.01280903702 1.01280903702 1.01280903702 True
SPI6.axonRM = 3945.2107187 3945.2107187 3945.2107187 True
SPI6.rall = 114.510490019 114.510490019 114.510490019 True
SPI6.axonL = 594.292937602 594.292937602 594.292937602 True
SPI6.gbar_kdmc = 0.000404311891107 0.000404311891107 0.000404311891107 True
SPI6.apicDiam = 1.5831889597 1.5831889597 1.5831889597 True
[params]
# each param is assignment string (comma-separated variable names), min, max, original, strictly bounded
SPI6.gbar_kdmc = 0.000321832265322 0.000574835869658 0.000404311891107 False 
SPI6.cal_gcalbar = 3.90099501249e-06 6.30684079224e-06 5e-06 False 
SPI6.can_gcanbar = 3.88149003743e-06 6.24424226355e-06 5e-06 False 
SPI6.kBK_gpeak = 4.75212e-05 8.11555038867e-05 6e-05 False 
SPI6.gbar_kap = 0.0335635444148 0.103852760174 0.05 False 
SPI6.gbar_kdr = 0.005572 0.0136947257086 0.007 False 
SPI6.gbar_nax = 0.0129625180451 0.030189094197 0.02 False 
SPI6.kBK_caVhminShift = 40.2991818393 53.6081467341 50.0 False 
SPI6.cadad_taur = 1.0 101.0025 1.0 False 
SPI6.cadad_depth = 0.0839007107474 0.116784559305 0.1 False 
# kinetics used here
# for kinetics - only a few of the many params
SPI6.kdr_vhalfn = 12.0 14.0 13.0 False 
SPI6.kap_vhalfn = 30.0 40.0 35.0 False 
SPI6.kap_vhalfl = -61.0 -51.0 -56.0 False 
SPI6.kap_tq = -50.0 -40.0 -45.0 False
[run]
#cellimport is the py file that has code for creating a cell
cellimport = SPI6
#cellfunc is function that returns a cell
cellfunc = SPI6
#postassign (optional) gets called after params assigned to the cell 
postassign = cell.set_props()
#locations to record voltage from. first location recorded from is used in voltage optimization
recordV = cell.soma(0.5)
#recordSpike is location where spikes are recorded from
recordSpike = cell.soma(0.5)
usecvode = True
tstop = 3000.0
baset = 1000.0
stimdel = 500.0
stimdur = 1000.0
[data]
#experimental voltage traces - in entirety
evolts = data/BS0284_tracedata_10KHz.npy
# stimulus amplitudes
lstimamp = data/BS0284_lstimamp.npy
# sampling rate
sampr = 10000