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

 Download zip file 
Help downloading and running models
"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. ..."
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:
Simulation Environment: NEURON; Python;
Model Concept(s): Parameter Fitting; Activity Patterns; Active Dendrites; Detailed Neuronal Models; Simplified Models;
Implementer(s): Suter, Benjamin ; Neymotin, Sam [Samuel.Neymotin at]; Dura-Bernal, Salvador [salvadordura at]; 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;
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 *
PTcell.BS0284.cfg *
PTcell.cfg *
# code by sam neymotin & ernie forzano
from neuron import h
from pylab import *
import sys
import pickle
import numpy
h.install_vecst() # for samp and other NQS/vecst functions
from conf import *
import os

# check if data exists - if not, run simulations, saving output data
dataDict = {'SPI6': 'SPI6','morph':'PTcell'}
for k, v in list(dataDict.items()) :
    if not os.path.exists('data/'+ k +'.pkl'):
        os.system('python ' + v +'.cfg')


rcParams['lines.markersize'] = 15
rcParams['lines.linewidth'] = 4
rcParams['font.size'] = 45
tl = tight_layout

def drtxt (ax,lett,tx=-0.075,ty=1.03,fsz=45): text(tx,ty,lett,fontweight='bold',transform=ax.transAxes,fontsize=fsz)

def naxbin (ax,nb): ax.locator_params(nbins=nb);

lmodel = ['Detailed', 'Simple']
lmodelpath = ['data/morph.pkl','data/SPI6.pkl']
lclr = ['r', 'b']

dmod = {}
for m,p in zip(lmodel,lmodelpath): dmod[m] = pickle.load(open(p))

# determine config file name
def setfcfg ():
    fcfg = "PTcell.cfg" # default config file name
    for i in range(len(sys.argv)):
        if sys.argv[i].endswith(".cfg") and os.path.exists(sys.argv[i]):
            fcfg = sys.argv[i]
    #print "config file is " , fcfg
    return fcfg

fcfg=setfcfg() # config file name
dconf = readconf(fcfg)
dprm = dconf['params']
dfixed = dconf['fixed']
sampr = dconf['sampr'] # sampling rate
I = numpy.load(dconf['lstimamp'])
evolts = numpy.load(dconf['evolts']) # experimental voltage traces
tt = numpy.array(dmod[lmodel[0]]['vt'])
tte = linspace(0, 1e3*evolts.shape[0]/sampr, evolts.shape[0])

def indexof (a,f):
    for i,val in enumerate(a):
        if abs(val-f) < 0.01: return i
    return -1

mytstop = dconf['tstop']
mybase = dconf['baset']
stimdel = dconf['stimdel']
stimdur = dconf['stimdur']
evolts = numpy.load(dconf['evolts']) # experimental voltage traces

ISubth = I[0:6]
ISup = I[6:] # subthresh right before threshold & superthreshold traces

def drawsuptraces ():
    tx,ty=-.05,1.02; offy = amin(tt[0]) - 30
    cdx = 1
    ncol = len(lmodel) + 1
    ax=subplot(1,ncol,cdx); ax.set_xticks([]); ax.set_yticks([]);
    ypos = offy
    ltxt = ['','a','b','c','d']
    for j,i in enumerate(ISup):
        idx = indexof(I,i)
        plot(tte,evolts[:,idx] + ypos,'k')
        if j > 0: ypos += 95
        else: ypos += 15
    cdx += 1
    for mdx,m in enumerate(lmodel):
        ax=subplot(1,ncol,cdx); title(m,fontweight='bold')
        ypos = offy
        for j,i in enumerate(ISup):
            plot(tt, dmod[m][i] + ypos,lclr[mdx])
            if j > 0: ypos += 95
            else: ypos += 15
        cdx += 1
        ax.set_xticks([]); ax.set_yticks([]);
    for i in range(1,ncol+1,1):