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

 Download zip file 
Help downloading and running models
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]
Citations  Citation Browser
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;
/
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
                            
TITLE Ih-current
: modified from http://senselab.med.yale.edu/ModelDB/showmodel.cshtml?model=64195&file=%5cStochastic%5cStochastic_Na%5cih.mod
: /u/samn/papers/jnsci_26_1677.pdf
: 
: @article{kole2006single,
:  title={Single Ih channels in pyramidal neuron dendrites: properties, distribution, and impact on action potential output},
:  author={Kole, M.H.P. and Hallermann, S. and Stuart, G.J.},
:  journal={The Journal of neuroscience},
:  volume={26},
:  number={6},
:  pages={1677--1687},
:  year={2006},
:  publisher={Soc Neuroscience}
: }

COMMENT
Author: Stefan Hallermann; modified by Sam Neymotin (parameterized)
Provides deterministic Ih-currents as described in Kole et al. (2006).
ENDCOMMENT

UNITS {
  (mA) = (milliamp)
  (mV) = (millivolt)
}

PARAMETER {
  v (mV)
  erev=-45  		(mV) 	:ih-reversal potential			       
  gbar=0.00015 	(S/cm2)	:default Ih conductance; exponential distribution is set in Ri18init.hoc 
  q10 = 2.2
  ascale = 0.00643
  bscale = 0.193
  ashift = 154.9
  aslope = 11.9
  bslope = 33.1
}

NEURON {
 THREADSAFE
  SUFFIX ih
  NONSPECIFIC_CURRENT i
  RANGE i,gbar,ascale,bscale,ashift,aslope,bslope
}

STATE {
  m
}

ASSIGNED {
  i (mA/cm2)
}

INITIAL { LOCAL a,b
  a = alpha(v)
  b = beta(v)
  m = a / (a + b)
}

BREAKPOINT {
  SOLVE state METHOD cnexp
  i = gbar*m*(v-erev)
}

: tau = 1 / (alpha + beta)
FUNCTION alpha(v(mV)) {
  alpha = ascale*(v+ashift)/(exp((v+ashift)/aslope)-1)  
  :parameters are estimated by direct fitting of HH model to activation time constants and voltage activation curve recorded at 34C
}

FUNCTION beta(v(mV)) {
  beta = bscale*exp(v/bslope)
}

DERIVATIVE state {
  m' = (1-m)*alpha(v) - m*beta(v)
}