from neuron import h
import matplotlib
matplotlib.use('Agg')
import numpy
from pylab import *
import mytools
import pickle
import time
import sys
import random
from setparams import *
random.seed(1)
v0 = -80
ca0 = 0.0001
proximalpoint = 400
distalpoint = 620
fs = 8
ITERS = 20
tstop = 11000.0
ISIs = [4*x for x in range(0,25)]
ISIs = [x for x in ISIs if mod(x,20) != 0]
currCoeff = 1.1
import mutation_stuff
MT = mutation_stuff.getMT()
defVals = mutation_stuff.getdefvals()
keyList = defVals.keys()
for idefval in range(0,len(keyList)):
if type(defVals[keyList[idefval]]) is not list:
defVals[keyList[idefval]] = [defVals[keyList[idefval]], defVals[keyList[idefval]]] #make the dictionary values [somatic, apical]
updatedVars = ['somatic','apical','basal'] # the possible classes of segments that defVals may apply to
whichDefVal = [0,1,0] # use the defVal[0] for somatic and basal segments and defVal[1] for apical segments
unpicklefile = open('scalings_cs.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
theseCoeffsAllAll = unpickledlist[0]
theseMutValsAllAll = unpickledlist[2]
paramdicts = []
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 1.0, 'S_gCa_HVAbar_Ca_HVA': 1.0}) # 1 spike per burst, control
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 1.6}) # 1-2 spikes per burst
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 2.2}) # 2-3 spikes per burst
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 2.2, 'S_gCa_HVAbar_Ca_HVA': 0.9}) # 3-4 spikes per burst
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 2.2, 'S_gCa_HVAbar_Ca_HVA': 0.625}) # 3-5 spikes per burst
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 2.2, 'S_gCa_HVAbar_Ca_HVA': 0.5}) # 4-6 spikes per burst
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 2.2, 'S_gCa_HVAbar_Ca_HVA': 0.3}) # 5-9 spikes per burst
#lensToStart = [100.0 + x*50 for x in range(0,16)]
lensToStart = [150.0, 300.0, 450.0, 600.0, 650.0]
gCoeffsAllAllAll = []
for istartdist in range(0,len(lensToStart)):
startdist = lensToStart[istartdist]
gCoeffsAllAll = []
if len(sys.argv) > 2 and int(float(sys.argv[2])) != istartdist:
continue
unpicklefile = open('synlocs'+str(startdist)+'.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
Nsyns = unpickledlist[0]
synlocsAll = unpickledlist[3]
startdist = int(startdist)
unpicklefile = open('thresholddistalamp'+str(startdist)+'.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
gsAllAll = unpickledlist[1]
maxLens = [1300,1185]
for icell in range(0,7):
synlocs = synlocsAll[0]
gCoeffsAll = []
if len(sys.argv) > 3 and int(float(sys.argv[3])) != icell:
continue
morphology_file = "morphologies/cell1.asc"
biophys_file = "models/L5PCbiophys3.hoc"
template_file = "models/L5PCtemplate.hoc"
theseCoeffsAll = theseCoeffsAllAll[icell]
h("""
load_file("stdlib.hoc")
load_file("stdrun.hoc")
objref cvode
cvode = new CVode()
cvode.active(1)
load_file("import3d.hoc")
objref L5PC
load_file(\""""+biophys_file+"""\")
load_file(\""""+template_file+"""\")
L5PC = new L5PCtemplate(\""""+morphology_file+"""\")
objref st1
st1 = new IClamp(0.5)
L5PC.soma st1
forsec L5PC.somatic {
}
forsec L5PC.apical {
}
L5PC.distribute_channels("apic","gIhbar_Ih",2,-0.8696,3.6161,0.0,1.0*2.0870,0.0002)
L5PC.distribute_channels("apic","gCa_HVAbar_Ca_HVA",3,1.0,0.1,685.0,885.0,1.0*0.000555)
L5PC.distribute_channels("apic","gCa_LVAstbar_Ca_LVAst",3,1.0,0.01,685.0,885.0,1.0*0.0187)
objref vsoma, vdend, recSite, vdend2, isoma, cadend, cadend2, casoma
vsoma = new Vector()
casoma = new Vector()
vdend = new Vector()
cadend = new Vector()
objref sl,st2,ns,syn1,con1,isyn, tvec, syns["""+str(2*Nsyns)+"""]
isyn = new Vector()
tvec = new Vector()
sl = new List()
double siteVec[2]
sl = L5PC.locateSites("apic","""+str(distalpoint)+""")
maxdiam = 0
for(i=0;i<sl.count();i+=1){
dd1 = sl.o[i].x[1]
dd = L5PC.apic[sl.o[i].x[0]].diam(dd1)
if (dd > maxdiam) {
j = i
maxdiam = dd
}
}
siteVec[0] = sl.o[j].x[0]
siteVec[1] = sl.o[j].x[1]
print "distalpoint gCa_HVA: ", L5PC.apic[siteVec[0]].gCa_HVAbar_Ca_HVA
print "distalpoint gCa_LVA: ", L5PC.apic[siteVec[0]].gCa_LVAstbar_Ca_LVAst
access L5PC.apic[siteVec[0]]
st2 = new IClamp(siteVec[1])
st2.amp = 0
L5PC.apic[siteVec[0]] {
st2
syn1 = new epsp(siteVec[1])
syn1.tau0 = 0.5
syn1.imax = 0
syn1.tau1 = 5
syn1.onset = 145
cvode.record(&syn1.i,isyn,tvec)
}
access L5PC.soma
cvode.record(&v(0.5),vsoma,tvec)
cvode.record(&cai(0.5),casoma,tvec)
access L5PC.apic[siteVec[0]]
cvode.record(&v(siteVec[1]),vdend,tvec)
cvode.record(&cai(siteVec[1]),cadend,tvec)
""")
for istim in range(0,Nsyns):
h("""
siteVec[0] = """+str(synlocs[istim][0])+"""
siteVec[1] = """+str(synlocs[istim][1])+"""
access L5PC.apic[siteVec[0]]
L5PC.apic[siteVec[0]] {
syns["""+str(istim)+"""] = new AlphaSynapse(siteVec[1])
syns["""+str(istim)+"""].e = 0
syns["""+str(istim)+"""].tau = 5
syns["""+str(istim)+"""].onset = 10000
syns["""+str(Nsyns+istim)+"""] = new AlphaSynapse(siteVec[1])
syns["""+str(Nsyns+istim)+"""].e = 0
syns["""+str(Nsyns+istim)+"""].tau = 5
syns["""+str(Nsyns+istim)+"""].onset = 10000
}
""")
paramdict = paramdicts[icell]
setparams(paramdict)
styles = ['g-','g-','g-','g-','g-','g-','g-','g-','g-']
#cols = ['#aaffaa','#aaffaa','#66ff66','#66aaaa','#00aaaa','#00aaaa']
#cols = ['#00aaaa','#00bb77','#11cc44','#11dd11','#55ee00','#99dd00']
cols = ['#00aaaa','#11cc44','#55ee00','#bbaa00','#ee6600','#ff0000', '#aa00aa','#772277','#333333']
#yplus = [1, 2, 3, 4, 5, 6, -1, -2, -3]
#yplus = [x+3 for x in yplus]
yplus = [0, 0, 0, 0, 0, 0, 0, 0, 0]
coeffCoeffs = [[0.25,0],[0.125,0],[0.5,0],[0.5,1.0/3],[0.5,2.0/3],[0.5,1.0],[-0.25,0],[-0.125,0],[-0.5,0]]
counter = -1
for igene in range(0,len(MT)):
gCoeffsThisGene = []
for imut in range(0,len(MT[igene])):
gCoeffsThisMut = []
nVals = len(MT[igene][imut])*[0]
thesemutvars = []
theseCoeffs = theseCoeffsAll[igene][imut]
for imutvar in range(0,len(MT[igene][imut])):
thesemutvars.append(MT[igene][imut][imutvar][0])
if type(MT[igene][imut][imutvar][1]) is int or type(MT[igene][imut][imutvar][1]) is float:
MT[igene][imut][imutvar][1] = [MT[igene][imut][imutvar][1]]
nVals[imutvar] = len(MT[igene][imut][imutvar][1])
cumprodnVals = cumprod(nVals)
allmutvars = cumprodnVals[len(MT[igene][imut])-1]*[thesemutvars]
allmutvals = []
for iallmutval in range(0,cumprodnVals[len(MT[igene][imut])-1]):
allmutvals.append([0]*len(thesemutvars))
for iallmutval in range(0,cumprodnVals[len(MT[igene][imut])-1]):
for imutvar in range(0,len(MT[igene][imut])):
if imutvar==0:
allmutvals[iallmutval][imutvar] = MT[igene][imut][imutvar][1][iallmutval%nVals[imutvar]]
else:
allmutvals[iallmutval][imutvar] = MT[igene][imut][imutvar][1][(iallmutval/cumprodnVals[imutvar-1])%nVals[imutvar]]
for iallmutval in range(0,cumprodnVals[len(MT[igene][imut])-1]):
counter = counter + 1
if len(sys.argv) > 1 and int(float(sys.argv[1])) != counter:
continue
gCoeffsThisMutVal = []
close("all")
f, axarr = plt.subplots(2, 2)
maxCac = 0
maxCadc = 0
#for iter in [2, 5, 8, -1]:
iters = [0, 2, 5, 6, 8, -1]
nspsThisVal = []
for iiter in range(0,len(iters)):
nspsThisIter = []
iter = iters[iiter]
gCoeffsThisIter = []
if iter >= 0:
thisCoeff = coeffCoeffs[iter][0]*theseCoeffs[iallmutval] + coeffCoeffs[iter][1]*(1.0 - 0.5*theseCoeffs[iallmutval])
else:
thisCoeff = 0
if iter == -1 and (igene > 0 or imut > 0 or iallmutval > 0):
continue # do the control only once!
if iter == 5:
continue
thisg = gsAllAll[icell][igene][imut][iallmutval][iiter]
print "iter="+str(iter)+", thisCoeff="+str(thisCoeff)+", thisg="+str(thisg)
mutText = ""
for imutvar in range(0,len(MT[igene][imut])):
if imutvar > 0 and imutvar%2==0:
mutText = mutText+"\n"
mutvars = allmutvars[iallmutval][imutvar]
mutvals = allmutvals[iallmutval][imutvar]
if type(mutvars) is str:
mutvars = [mutvars]
mutText = mutText + str(mutvars) + ": "
for kmutvar in range(0,len(mutvars)):
mutvar = mutvars[kmutvar]
if mutvar.find('offm') > -1 or mutvar.find('offh') > -1 or mutvar.find('ehcn') > -1:
newVal = [x+mutvals*thisCoeff for x in defVals[mutvar]]
if mutvals >= 0 and kmutvar==0:
mutText = mutText + "+" + str(mutvals) +" mV"
elif kmutvar==0:
mutText = mutText + str(mutvals) +" mV"
else:
newVal = [x*(mutvals**thisCoeff) for x in defVals[mutvar]]
if kmutvar==0:
mutText = mutText + "*" + str(mutvals)
if kmutvar < len(mutvars)-1:
mutText = mutText + ", "
if mutvar.find('_Ih') > -1:
updateThese = [1,1,1]
elif mutvar.find('_Ca_HVA') > -1 or mutvar.find('_Ca_LVAst') > -1 or mutvar.find('_SKv3.1') > -1 or mutvar.find('_Ca_HVA') > -1 or mutvar.find('_SK_E2') > -1 or mutvar.find('_NaTa_t') > -1 or mutvar.find('_CaDynamics_E2') > -1:
updateThese = [1,1,0]
elif mutvar.find('_K_Pst') > -1 or mutvar.find('_K_Tst') > -1 or mutvar.find('_Nap_Et2') > -1:
updateThese = [1,0,0]
elif mutvar.find('_Im') > -1:
updateThese = [0,1,0]
else:
print "Error: str=" + str(mutvar)
updatedThese = [0,0,0]
for iupdated in range(0,3):
if updateThese[iupdated]:
print """forsec L5PC."""+str(updatedVars[iupdated])+""" {
"""+mutvar+""" = """+str(newVal[whichDefVal[iupdated]])+"""
}"""
h("""forsec L5PC."""+str(updatedVars[iupdated])+""" {
"""+mutvar+""" = """+str(newVal[whichDefVal[iupdated]])+"""
}""")
print mutText
thisCa = h.L5PC.soma[0].minCai_CaDynamics_E2
for iISI in range(0,len(ISIs)):
gCoeffsThisISI = []
PPIdt = ISIs[iISI]
nextCoeffs = [0,15.0,4.0]
hasSpiked = 0
for iterI in range(0,ITERS+2):
for istim in range(0,Nsyns):
h("syns["+str(istim)+"].gmax = "+str(thisg*currCoeff))
h("syns["+str(Nsyns+istim)+"].gmax = "+str(thisg*currCoeff*nextCoeffs[min(iterI,2)]))
h("syns["+str(Nsyns+istim)+"].onset = "+str(10000+PPIdt))
h("""
tstop = """+str(tstop)+"""
cai0_ca_ion = """+str(thisCa)+"""
v_init = """+str(v0)+"""
st1.amp = 0
st1.del = 200
st1.dur = 10
""")
timenow = time.time()
h.init()
try:
h.run()
except RuntimeError:
hasErred = 1
print "Too large g!"
times=np.array(h.tvec)
Vsoma=np.array(h.vsoma)
spikes = mytools.spike_times(times,Vsoma,-20,-45)
nSpikes1 = len(spikes)
print "nextCoeffs="+str(nextCoeffs)+", "+str(nSpikes1)+" spikes, simulation done in "+str(time.time()-timenow)+" seconds"
nSpikes_normal = 1
if icell > 0 or startdist <= 200: # For icell=0, 1 spike normally generated (except for the inputs nearest to soma), while for icell=1,2,3,4,5,6, two spikes normally generated
nSpikes_normal = 2
hasSpiked = hasSpiked or (nSpikes1 > nSpikes_normal)
if iterI == 0 and hasSpiked:
print "istartdist="+str(istartdist)+", icell="+str(icell)+", igene="+str(igene)+", imut="+str(imut)+", iallmuval="+str(iallmutval)+", iiter="+str(iiter)+", iISI="+str(iISI)+": extra spikes elicited for iterI=0!"
if iterI > 0 and not hasSpiked:
print "istartdist="+str(istartdist)+", icell="+str(icell)+", igene="+str(igene)+", imut="+str(imut)+", iallmuval="+str(iallmutval)+", iiter="+str(iiter)+", iISI="+str(iISI)+": no extra spikes for iterI>0!"
nextCoeffs = [nextCoeffs[1],2*nextCoeffs[1],1.5*nextCoeffs[1]]
continue
if iterI > 1 and nSpikes1 > nSpikes_normal:
nextCoeffs = [nextCoeffs[0],nextCoeffs[2],0.5*(nextCoeffs[0]+nextCoeffs[2])]
if iterI > 1 and nSpikes1 <= nSpikes_normal:
nextCoeffs = [nextCoeffs[2],nextCoeffs[1],0.5*(nextCoeffs[2]+nextCoeffs[1])]
#print str(nSpikes1)+", nextCoeffs="+str(nextCoeffs)
gCoeffsThisISI = nextCoeffs[:]
gCoeffsThisIter.append(gCoeffsThisISI[:])
#Print the parameters and their default values:
for idefval in range(0,len(defVals.keys())):
thisdefval = defVals.keys()[idefval]
if thisdefval.find('_Im') > -1:
h('print "L5PC.apic[0].'+thisdefval+' = ", L5PC.apic[0].'+thisdefval+', "Default = ", '+str(defVals[thisdefval][1]))
#) #+" (def="+str(defVals[thisdefval])+")"
else:
h('print "L5PC.soma[0].'+thisdefval+' = ", L5PC.soma[0].'+thisdefval+', "Default = ", '+str(defVals[thisdefval][0]))
#h('print L5PC.soma[0]."+thisdefval) #+" (def="+str(defVals[thisdefval])+")"
#Restore default values:
for imutvar in range(0,len(MT[igene][imut])):
mutvars = allmutvars[iallmutval][imutvar]
mutvals = allmutvals[iallmutval][imutvar]
if type(mutvars) is str:
mutvars = [mutvars]
for kmutvar in range(0,len(mutvars)):
mutvar = mutvars[kmutvar]
newVal = defVals[mutvar]
if mutvar.find('_Ih') > -1:
updateThese = [1,1,1]
elif mutvar.find('_Ca_HVA') > -1 or mutvar.find('_Ca_LVAst') > -1 or mutvar.find('_SKv3.1') > -1 or mutvar.find('_Ca_HVA') > -1 or mutvar.find('_SK_E2') > -1 or mutvar.find('_NaTa_t') > -1 or mutvar.find('_CaDynamics_E2') > -1:
updateThese = [1,1,0]
elif mutvar.find('_K_Pst') > -1 or mutvar.find('_K_Tst') > -1 or mutvar.find('_Nap_Et2') > -1:
updateThese = [1,0,0]
elif mutvar.find('_Im') > -1:
updateThese = [0,1,0]
else:
print "Error: str=" + str(mutvar)
updatedThese = [0,0,0]
for iupdated in range(0,3):
if updateThese[iupdated]:
print """forsec L5PC."""+str(updatedVars[iupdated])+""" {
"""+mutvar+""" = """+str(newVal[whichDefVal[iupdated]])+"""
}"""
h("""forsec L5PC."""+str(updatedVars[iupdated])+""" {
"""+mutvar+""" = """+str(newVal[whichDefVal[iupdated]])+"""
}""")
gCoeffsThisMutVal.append(gCoeffsThisIter[:])
picklelist = [theseCoeffsAll,gCoeffsThisMutVal,ISIs,MT]
file = open('PPIcoeffs_complement_'+str(startdist)+'_cs'+str(icell)+'_'+str(counter)+'.sav', 'w')
pickle.dump(picklelist,file)
file.close()
gCoeffsThisMut.append(gCoeffsThisMutVal[:])
gCoeffsThisGene.append(gCoeffsThisMut[:])
gCoeffsAll.append(gCoeffsThisGene[:])
gCoeffsAllAll.append(gCoeffsAll[:])
gCoeffsAllAllAll.append(gCoeffsAllAll[:])
#picklelist = [theseCoeffsAllAll,gsThisAllAll,MT]
#file = open('thresholddistalamp'+str(startdist)+'_cs'+str(icell)+'_'+str(counter)+'.sav', 'w')
#pickle.dump(picklelist,file)
#file.close()