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

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"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]
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
from scipy.stats.stats import pearsonr
from utils import dtrans
import shutil

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

useRMP = False # True # use RMP for fitness calculation?
useVoltDiff = False
useISI = False # this is for evaluation of full isi voltage
useISIFeat = False # this is for evaluation of isi voltage features
useISIDepth = False # this is for evaluation of isi voltage depth (min voltage)
useISIDur = False # this is for evaluation of isi voltage duration
useSag = False # whether to use sag for fitness
useSpikeAmp = False # spike amplitude (peak - treshold voltage) - do not need when using SpikeThresh and SpikePeak
useSpikePeak = False # spike peak (absolute voltage)
useSpikeW = False # spike widths at 25% and 50%
useSpikeSlope = False # min,max dv/dt
useSpikeThresh = False # spike threshold voltage
useSpikeShape = False # overall spike shape - uses features (peak,width,slope,thresh)
useSpikeTimes = useSpikeCoinc = False
useSFA = False # spike-frequency adaptation measure
useLVar = False
useInstRate = False
useTTFS = False # use time-to-first-spike for fitness

def getfitdims ():
  fitdims = []
  if useRMP: fitdims.append('RMP')
  if useSag: fitdims.append('Sag')
  if useFI: fitdims.append('FI')
  if useISI: fitdims.append('ISIVolt')
  if useISIFeat: fitdims.append('ISIFeat')
  if useSFA: fitdims.append('SFA')
  if useLVar: fitdims.append('LVar')
  if useInstRate: fitdims.append('InstRate')
  if useTTFS: fitdims.append('TTFS')
  if useSpikeTimes: fitdims.append('SpikeTimes')
  if useSpikeCoinc: fitdims.append('SpikeCoinc')
  if useSpikeAmp: fitdims.append('SpikeAmp')
  if useSpikePeak: fitdims.append('SpikePeak')
  if useSpikeW: fitdims.append('SpikeW')
  if useSpikeSlope: fitdims.append('SpikeSlope')
  if useSpikeThresh: fitdims.append('SpikeThresh')
  if useSpikeShape: fitdims.append('SpikeShape')
  if useVoltDiff: fitdims.append('VoltDiff')
  if useISIDepth: fitdims.append('ISIDepth')
  if useISIDur: fitdims.append('ISIDur')
  return fitdims

# determine config file name
def setfcfg ():
  fcfg = "PTcell.BS0284.cfg" # default config file name
  for i in xrange(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

dmod = {}

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
tte = linspace(0, 1e3*evolts.shape[0]/sampr, evolts.shape[0])
evolts = numpy.load(dconf['evolts']) # experimental voltage traces


def geterramp (nqa,row,lc):
  err = 0.0
  for c in lc:
    if != -1:
      err += (nqa.getcol(c).x[row] / nqa.getcol(c).mean())**2
  return sqrt(err)

def adderrampcol (nqa,lc):
  if'erramp')== -1.0: nqa.resize('erramp'); nqa.pad()
  for i in xrange(int(nqa.v[0].size())): nqa.getcol('erramp').x[i] = geterramp(nqa,i,lc)
  nqa.stat('erramp') # 

# convert population to NQS
def pop2nq (fpop,fitdims=None):
  if fitdims == None: fitdims=getfitdims()
  nqa = None
    nqa = h.NQS()
    h.load_file("nqs.hoc"); #h.load_file("decnqs.hoc")
    nqa = h.NQS()
  # first setup the fitness dimensions
  for s in fitdims: nqa.resize(s)
  for m in fpop:
    fit =
    for i,val in enumerate(fit): nqa.v[i].append(val)
  # then setup the parameter values
  for k in dprm.keys(): nqa.resize(k)
  for i,m in enumerate(fpop):
    idx = len(fitdims)
    prm = m.candidate
    jdx = idx; kdx = 0
    while jdx < nqa.m[0]:      
      nqa.v[jdx].x[i] = prm[kdx]
      jdx += 1; kdx += 1
  return nqa

# print out param values (nqa is table, idx is row)
def rowprmstr (nq,idx):
  s = ''
  for i in xrange(len(fitdims),int(nq.m[0]),1): s += str(nq.v[i].x[idx]) + ' ' 
  return s

# loads model archive and stores in global ark and nqa objects
def loadark (fn):
  global ark,nqa
  ark = pickle.load(open(fn))
  print len(ark), ' models in ', fn, ' archive.'
  nqa = pop2nq(ark,fitdims) 

if fcfg == 'SPI6.cfg': # simplified model
  fitdims=getfitdims() # reset fitness dimensions(fitdims), which differ from detailed model
  loadark(os.path.join('data','simparch.pkl')) # load simple model archive
else: # detailed model
  loadark(os.path.join('data','detarch.pkl')) # load detailed model archive

# add text to a plot
def addtext (row,col,lgn,ltxt,tx=-0.025,ty=1.03,c='k'):
  for gn,txt in zip(lgn,ltxt):
    ax = subplot(row,col,gn)

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

# print full row (fitness and param values) at the given row (idx) from table (nqa)
def rowstr (nq,idx):
  s = ''
  for i in xrange(int(nq.m[0])): s += nq.s[i].s + ':' + str(nq.v[i].x[idx]) + "\n"
  return s

# print param values at the given row (idx) from table (nqa)
def rowprmvals (nq,idx):
  lval = []
  for i in xrange(len(fitdims),int(nq.m[0]),1): lval.append((nq.v[i].x[idx]))
  return lval

# find index of f in a (if not there return -1)
def indexof (a,f):
  for i,val in enumerate(a):
    if abs(val-f) < 0.01: return i
  return -1

ISubth = I[0:6] # subthreshold current injections
ISup = I[6:] # current injections for subthresh right before threshold & superthreshold traces 
IAll = list(ISubth); IAll.extend(list(ISup))

# draw traces from experiment (uses black color)
def drawexptraces ():
  tx,ty=-.05,1.02; offy = amin(tte[0]) - 30
  ax=gca(); ax.set_xticks([]); ax.set_yticks([]);
  ypos = offy 
  for j,i in enumerate(IAll):
    idx = indexof(I,i)
    plot(tte,evolts[:,idx] + ypos,'k')
    if j > len(ISubth): ypos += 95
    else: ypos += 15  

cdx=0 # index into color list
# draw traces from the model (cycles through colors)
def drawtraces (model):
  global cdx
  lclr = ['r','g','b','c','m','y']
  tt = numpy.array(dmod[model]['vt'])
  tx,ty=-.05,1.02; offy = amin(tt[0]) - 30
  if len(get_fignums())==0: drawexptraces()
  mdx=0; m=model
  ypos = offy 
  for j,i in enumerate(IAll):
    plot(tt, dmod[m][i] + ypos,lclr[cdx%len(lclr)])
    if j > len(ISubth): ypos += 95
    else: ypos += 15
  ax.set_xticks([]); ax.set_yticks([]);

# run model idx using params in ark/nqa, then load/draw the data
def runmodel (idx):
  global lastmodel
  # should move pkl file to arch index location so dont have to rerun
  fnew = os.path.join('data', fcfg.split('.cfg')[0] + '_' + str(idx) + '.pkl')
  if os.path.exists(fnew):
    print 'model ' + str(idx) + ' already ran, data in', fnew
    cmd = 'python ' + fcfg + ' ' + rowprmstr(nqa,idx)
    print cmd
    if fcfg.startswith('PTcell'):
    if not os.path.exists(fnew):
      print 'ERROR: could not run model!'
  lastmodel = (fcfg,idx)
  dmod[lastmodel] = pickle.load(open(fnew)) # load the data
  print 'model fitness error/params:', rowstr(nqa,idx)

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

# draw archive figure showing param values of bottom/top percentiles
def drawarchfig ():
  if fcfg == 'SPI6.cfg':
    lprm = ['SPI6.gbar_kdmc','SPI6.cal_gcalbar','SPI6.can_gcanbar','SPI6.kBK_gpeak','SPI6.gbar_kap','SPI6.gbar_kdr','SPI6.gbar_nax','SPI6.kBK_caVhminShift','SPI6.cadad_taur','SPI6.cadad_depth','h.vhalfn_kdr','h.vhalfn_kap','h.vhalfl_kap','h.tq_kap']
    lprm = ['morph.nax_gbar', 'morph.kdmc_gbar','morph.kdr_gbar','morph.kap_gbar','morph.kBK_gpeak','morph.kBK_caVhminShift','morph.cal_gcalbar','morph.can_gcanbar','morph.cadad_taur','morph.cadad_depth']
  xlim((0.5,10.5)); ylim((-3,4.5))
  mbotAMP,mtopAMP = getprct(nqa,'erramp',0.01,lprm)
  mcAMP = getprmcors(nqa,'erramp',0.01,lprm)
  ax = subplot(2,2,2)
  colorbar(); ax.set_xticks([]); ax.set_yticks([])
  mytxt = 'Worst                         Best'; xlabel(mytxt); ylabel(mytxt);
  title('Parameter correlations')
  xlim((0.5,10.5)); ylim((-3,4.5))
  mbotFI,mtopFI = getprct(nqa,'FI',0.01,lprm)
  mcFI = getprmcors(nqa,'FI',0.01,lprm)
  ax = subplot(2,2,4)
  colorbar(); ax.set_xticks([]); ax.set_yticks([])
  mytxt = 'Worst                         Best'; xlabel(mytxt); ylabel(mytxt);
  title('Parameter correlations')
  subplot(2,2,1); title('Rank by Error Amplitude');
  subplot(2,2,3); title('Rank by FI Error')

def draw1dfig (nq,scc,prct,lprm,nrow=1,ncol=1,gdx=1,stxt='a'):
  nqt = h.NQS()
  botsidx,boteidx = 0,int(prct*nqt.v[0].size()) # good
  topsidx,topeidx = int(nqt.v[0].size()*(1.0-prct)),int(nqt.v[0].size()-1) # bad
  ax = subplot(nrow,ncol,gdx)
  for pdx,prm in enumerate(lprm):
    dat = numpy.array(nqt.getcol(prm).to_python())
    dat = dat - mean(dat)
    dat = dat / std(dat)
    plot([pdx+1 for j in xrange(boteidx-botsidx)],dat[botsidx:boteidx],'^',markeredgecolor='m',markerfacecolor='none',markersize=60,linewidth=8)
    plot([pdx+1 for j in xrange(topeidx-topsidx)],dat[topsidx:topeidx],'v',markeredgecolor='c',markerfacecolor='none',markersize=60,linewidth=8)
  ax.set_xticklabels([dtrans[prm] for prm in lprm])
  ylabel('Normalized parameter value'); #ylim((-4.2,4.2))

# get bottom/top percentile from nq using column scc
def getprct (nq,scc,prct,lprm):
  nqt = h.NQS()
  botsidx,boteidx = 0,int(prct*nqt.v[0].size()) # good
  topsidx,topeidx = int(nqt.v[0].size()*(1.0-prct)),int(nqt.v[0].size()-1) # bad
  mtop = zeros((topeidx-topsidx,len(lprm)))
  mbot = zeros((boteidx-botsidx,len(lprm)))
  for pdx,prm in enumerate(lprm):
    dat = numpy.array(nqt.getcol(prm).to_python())
    dat = dat - mean(dat)
    dat = dat / std(dat)
    mbot[:,pdx] = dat[botsidx:boteidx]
    mtop[:,pdx] = dat[topsidx:topeidx]
  return mbot,mtop

# get parameter correlations
def getprmcors (nq,scc,prct,lprm):
  mbot,mtop = getprct(nq,scc,prct,lprm)
  nrow,ncol = mbot.shape
  mprct = zeros((nrow*2,ncol))
  mprct[0:nrow,:] = mbot
  mprct[nrow:,:] = mtop
  mc = ones((nrow*2,nrow*2))
  for i in xrange(nrow*2):
    for j in xrange(i+1,nrow*2,1):
  return mc

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