def initdend (self):
self.dend = h.Section()
self.dend.nseg = 1
self.dend.diam = 2
self.dend.L = 500
self.dend.Ra = 1
self.dend.insert('pas')
self.ampa = h.AMPA(0.5,sec=self.dend)
self.nmda = h.NMDA(0.5,sec=self.dend)
self.gabaa0 = h.GABAa(0.5,sec=self.dend)
from neuron import h
import neuron.gui
soma = h.Section(name='soma')
soma.insert('hh')
ampa = h.AMPA(0.5, sec=soma)
ampa.gbar = 1 # Just increasing, for testing purpose
#expSyn = h.ExpSyn(0.5, soma)
#expSyn.e = 10
#expSyn.i = 3
#expSyn.tau = 3
netStim = h.NetStim()
netStim.start = 1
netStim.interval = 1
netStim.number = 3
netCon = h.NetCon(netStim, ampa)
#netCon = h.NetCon(netStim, expSyn)
netCon.weight[0] = 1
def __init__(self, TTX = False, Pool1_num = 9, Pool2_num = 9, Beta = 0.067,
Cdur = 1, Syn_w1 = 0.01, Syn_w2 = 0.01, Loc = [0.2, 0.6]):
"""
Model the Glumate Stimulation.
Model the Receptors in 2 pools:
Pool 1: AMPA + NMDA (same synaptic weight, represent spine conductance)
Pool 2: NMDA only (represent the extrasyanptic NMDARs)
Parameters:
-----------
TTX: True or False.
True: setting all the sodium channel conductance to 0 to mimic
TTX application in experiments
False: default
Pool1_num: syanptic AMPA/NMDA numbers
Pool2_num: extrasyanptic NMDA numbers
Beta: parameter for NMDA Receptors
Cdur: parameter for NMDA Receptors
Syn_w1: the syanptic weight of AMPA/NMDA receptors in pool1
Syn_w2: the syanptic weight of AMPA/NMDA receptors in pool2
Loc: the stimulation location
-----------
Outputs:
Figures: recording from soma and 3 different locations from basal dendrites
json: soma and dendritc voltage recording and parameters info
"""
Cell = de.CA229(HVA_ratio = 0.0, LVA_ratio = 0.0)
self.Cell = Cell
# Can adjust channel conductance ratio here:
# eg. Cell = CA229(KA_ratio = 0.5)
###########################################
timestr = time.strftime("%H%M")
data = time.strftime("%m_%d")
# directory = 'Data_' + data +'/'
directory_root = 'Fig3/'
directory = directory_root + 'DMS/Plot/'
# directory = directory_root + 'DMS/Analysis/'
if (TTX == True):
Cell.TTX()
title = "TTX_Pool1_"+ str(Pool1_num) + "_Pool2_" + str(Pool2_num) + "_NMDA_Beta_" + \
str(Beta) + "_NMDA_Cdur_"+str(Cdur)+ "_Pool1_W_" + str(Syn_w1) + \
"_Pool2_W_" + str(Syn_w2) + "_"+ timestr
else:
title = "Pool1_"+ str(Pool1_num) + "_Pool2_" + str(Pool2_num) + "_NMDA_Beta_" + \
str(Beta) + "_NMDA_Cdur_"+str(Cdur)+ "_Pool1_W_" + str(Syn_w1) + \
"_Pool2_W_" + str(Syn_w2) + "_"+ timestr
###########################################
# Adding Pool 1
###########################################
##### AMPA
SynAMPA = []
nc_AMPA = []
SynNMDA = []
nc_NMDA = []
self.SynAMPA = SynAMPA
self.nc_AMPA = nc_AMPA
self.SynNMDA = SynNMDA
self.nc_NMDA = nc_NMDA
###########################################
loc1 = list(np.linspace(Loc[0], Loc[1], Pool1_num))
###########################################
# Loc and time delay set up
delay1 = random_2(10, 50 + int(Syn_w1*50), Pool1_num)
# delay1 = random_beta(10, 50 + int(Syn_w1*50), Pool1_num)
ns = h.NetStim()
self.ns = ns
ns.interval = 20
ns.number = 1
ns.start = 190
ns.noise = 0
###########################################
for i in range(Pool1_num):
###########################
# Adding AMPA
SynAMPA.append(h.AMPA(Cell.basal[34](loc1[i])))
SynAMPA[-1].gmax = 0.05
nc_AMPA.append(h.NetCon(ns, SynAMPA[i]))
nc_AMPA[-1].delay = delay1[i]
nc_AMPA[-1].weight[0] = Syn_w1
###########################
#Adding NMDA
SynNMDA.append(h.NMDA(Cell.basal[34](loc1[i])))
SynNMDA[-1].gmax = 0.005
SynNMDA[-1].Beta = Beta
SynNMDA[-1].Cdur = Cdur
nc_NMDA.append(h.NetCon(ns, SynNMDA[i]))
nc_NMDA[-1].delay = delay1[i]
nc_NMDA[-1].weight[0] = Syn_w1
###########################################
# Adding Pool 2
###########################################
ExNMDA = []
nc_ExNMDA = []
self.ExNMDA = ExNMDA
self.nc_ExNMDA = nc_ExNMDA
loc2 = list(np.linspace(Loc[0], Loc[1], Pool2_num))
# delay2 = list(np.linspace(5, 10, Pool2_num))
delay2 = random_2(15, 55 + int(Syn_w2*60), Pool2_num)
# delay2 = random_beta(15, 55 + int(Syn_w2*60), Pool2_num)
for i in range(Pool2_num):
###########################
# Adding extrasyanptic NMDA
ExNMDA.append(h.NMDA(Cell.basal[34](loc2[i])))
ExNMDA[-1].gmax = 0.005
ExNMDA[-1].Beta = Beta
ExNMDA[-1].Cdur = Cdur
nc_ExNMDA.append(h.NetCon(ns, ExNMDA[i]))
nc_ExNMDA[-1].delay = delay2[i]
nc_ExNMDA[-1].weight[0] = Syn_w2
###########################################
### Recording
###########################################
t_vec = h.Vector()
t_vec.record(h._ref_t)
v_vec_soma = h.Vector()
v_vec_dend1 = h.Vector()
v_vec_dend2 = h.Vector()
v_vec_dend3 = h.Vector()
cai_soma = h.Vector()
cai_dend = h.Vector()
v_vec_soma.record(Cell.soma[2](0.5)._ref_v)
v_vec_dend1.record(Cell.basal[34](0.8)._ref_v)
v_vec_dend2.record(Cell.basal[34](0.5)._ref_v)
v_vec_dend3.record(Cell.basal[34](0.3)._ref_v)
cai_soma.record(Cell.soma[2](0.5)._ref_cai)
cai_dend.record(Cell.basal[34](0.3)._ref_cai)
###########################################
### Run & Plot
###########################################
h.celsius = 32
h.v_init = -73.6927850677
h.init()
h.tstop = 1000
h.run()
# pdb.set_trace() #Debugging
# print v_vec_soma[-1]
# plt.clf()
# plt.close()
# plt.figure(figsize = (16, 6), dpi = 100)
# plt.plot(t_vec, v_vec_soma, label = 'soma(0.5)', color = 'black')
# plt.plot(t_vec, v_vec_dend1, label = 'bdend[34](0.8)', color = 'red')
# plt.plot(t_vec, v_vec_dend2, label = 'Basal[34](0.5)', color = 'blue')
# plt.plot(t_vec, v_vec_dend3, label = 'Basal[34](0.3)', color = 'green')
# plt.ylim([-90, 40])
# plt.xlim([0, 800])
# plt.legend(loc = 'best')
# plt.ylabel('mV')
# plt.xlabel('Time (ms)')
# plt.title ("Glumate Receptor Activated Plateau Potential")
#
# save(title, directory, ext="png", close=True, verbose=True)
#######################
# Plot the intracelluar calcium concentration
# plt.clf()
# plt.close()
# plt.figure(figsize = (16, 6), dpi = 100)
# plt.plot(t_vec, cai_soma, label = 'soma(0.5)', color = 'black')
# #plt.plot(t_vec, cai_dend, label = 'bdend[34](0.3)', color = 'red')
#
# # plt.ylim([-90, 60])
# plt.xlim([0, 800])
# plt.legend(loc = 'best')
# plt.ylabel('mM')
# plt.xlabel('Time (ms)')
# plt.title ("Calcium concentration")
# title1 = "Calcium_" + title
# ut.save(title1, directory, ext="png", close=True, verbose=True)
data = ut.Vividict()
data['SynAMPA']['num'] = Pool1_num
data['SynAMPA']['locs'] = loc1
data['SynAMPA']['weight'] = Syn_w1
data['SynNMDA']['num'] = Pool1_num
data['SynNMDA']['locs'] = loc1
data['SynNMDA']['weight'] = Syn_w1
data['SynNMDA']['Beta'] = Beta
data['SynNMDA']['Cdur'] = Cdur
data['ExNMDA']['num'] = Pool2_num
data['ExNMDA']['locs'] = loc2
data['ExNMDA']['weight'] = Syn_w2
data['ExNMDA']['Beta'] = Beta
data['ExNMDA']['Cdur'] = Cdur
data['recording']['time'] = list(t_vec)
data['recording']['soma']['voltage'] = list(v_vec_soma)
data['recording']['basal_34']['voltage_0.8'] = list(v_vec_dend1)
data['recording']['basal_34']['voltage_0.5'] = list(v_vec_dend2)
data['recording']['basal_34']['voltage_0.3'] = list(v_vec_dend3)
data['recording']['soma']['ica'] = list(cai_soma)
data['recording']['basal_34']['ica_0.3'] = list(cai_dend)
ut.savejson(data, title, directory, ext = "json", verbose = False)
def prepCell(gList, loc, pos, n, v0, alphaR=True):
f = open('pylog', 'a')
#print ' location, strength, pos'
#print >>f, ' location, strength, pos'
#for i in xrange(n):
# print '#{:2d} {:3d} {: 1.1e} {:0.1f}'.format(i,loc[i],gList[i],pos[i])
# print >>f, '#{:2d} {:3d} {: 1.1e} {:0.1f}'.format(i,loc[i],gList[i],pos[i])
print >> f, 'gList = np.array([',
print 'gList = np.array([',
for i in xrange(n):
if i == n - 1:
print >> f, gList[i],
print gList[i],
else:
print >> f, gList[i], ', ',
print gList[i], ', ',
print >> f, '])'
print '])'
print >> f, 'loc = np.array([',
print 'loc = np.array([',
for i in xrange(n):
if i == n - 1:
print >> f, loc[i],
print loc[i],
else:
print >> f, loc[i], ', ',
print loc[i], ', ',
print >> f, '])'
print '])'
print >> f, 'pos = np.array([',
print 'pos = np.array([',
for i in xrange(n):
if i == n - 1:
print >> f, pos[i],
print pos[i],
else:
print >> f, pos[i], ', ',
print pos[i], ', ',
print >> f, '])'
print '])'
h.cvode.active(0)
cell = RealisticNeuron(v0)
cell.soma.push()
h.distance()
h.pop_section()
vecStimList = [h.VecStim() for i in xrange(n)]
synList = []
dist = np.zeros((n))
print >> f, 'distance = np.array([',
print 'distance = np.array([',
for i in xrange(n):
cell.dend[loc[i]].push()
if gList[i] < 0:
if alphaR:
synList.append(h.ALPHAI())
else:
synList.append(h.GABAa())
else:
if alphaR:
synList.append(h.ALPHAE())
else:
synList.append(h.AMPA())
synList[i].loc(pos[i])
h.setpointer(vecStimList[i]._ref_y, 'pre', synList[i])
h.pop_section()
if gList[i] < 0:
if alphaR:
synList[i].f = -gList[i]
else:
synList[i].gmax = -gList[i]
else:
if alphaR:
synList[i].f = gList[i]
else:
synList[i].gmax = gList[i]
dist[i] = h.distance(cell.dend[loc[i]](pos[i]))
if i == n - 1:
print >> f, dist[i],
print dist[i],
else:
print >> f, dist[i], ', ',
print dist[i], ', ',
print >> f, '])'
print '])'
f.close()
return cell, vecStimList, synList, dist
def Glu_Stim(Bnum=34,
TTX=False,
Pool1_num=9,
Pool2_num=9,
Syn_w1=0.01,
Syn_w2=0.01,
Loc=[0.2, 0.6],
DenLoc=0.5):
"""
Model the Glumate Stimulation.
Model the Receptors in 2 pools:
Pool 1: AMPA + NMDA (same synaptic weight, represent spine conductance)
Pool 2: NMDA only (represent the extrasyanptic NMDARs)
Parameters:
-----------
Bnum: the number of basal branch to explore
TTX: True or False.
True: setting all the sodium channel conductance to 0.
False: default
Pool1_num: syanptic AMPA/NMDA numbers
Pool2_num: extrasyanptic NMDA numbers
Syn_w1: the syanptic weight of AMPA/NMDA receptors in pool1
Syn_w2: the syanptic weight of AMPA/NMDA receptors in pool2
Loc: the stimulation location
DenLoc: the targeted recording location on dendrite
-----------
Outputs:
Figures: recording from soma and 3 different locations from basal dendrites
json: soma and dendritc voltage recording and parameters info
"""
Cell = de.CA229()
timestr = time.strftime("%Y%m%d-%H%M")
data = time.strftime("%m_%d")
directory_root = "Fig5/Major/"
L1 = "{:.2f}".format(Loc[0])
L2 = "{:.2f}".format(Loc[1])
if (TTX == True):
Cell.TTX()
directory = directory_root + "B" + str(
Bnum) + "/Loc" + L1 + "_" + L2 + "/TTX/"
title = "TTX_Pool1_"+ \
str(Pool1_num) + "_Pool2_" + str(Pool2_num) + \
"_Pool1_W_" + str(Syn_w1) + \
"_Pool2_W_" + str(Syn_w2) + "_"+ timestr
else:
directory = directory_root + "B" + str(
Bnum) + "/Loc" + L1 + "_" + L2 + "/N/"
title = "Pool1_"+ \
str(Pool1_num) + "_Pool2_" + str(Pool2_num) + \
"_Pool1_W_" + str(Syn_w1) + \
"_Pool2_W_" + str(Syn_w2) + "_"+ timestr
###########################################
# Adding Pool 1
###########################################
##### AMPA
SynAMPA = []
nc_AMPA = []
SynNMDA = []
nc_NMDA = []
loc1 = list(np.linspace(Loc[0], Loc[1], Pool1_num))
###########################################
delay1 = random_2(10, 20 + int(Syn_w1 * 50), Pool1_num)
ns = h.NetStim()
ns.interval = 20
ns.number = 1
ns.start = 190
ns.noise = 0
for i in range(Pool1_num):
###########################
# Adding AMPA
SynAMPA.append(h.AMPA(Cell.basal[Bnum](loc1[i])))
SynAMPA[-1].gmax = 0.05
nc_AMPA.append(h.NetCon(ns, SynAMPA[i]))
nc_AMPA[-1].delay = delay1[i]
nc_AMPA[-1].weight[0] = Syn_w1
###########################
for i in range(Pool1_num):
tempNMDA = h.nmda(Cell.basal[Bnum](loc1[i]))
tempNMDA.gmax = 0.005 * Syn_w1
tempNMDA.onset = delay1[i] + ns.start
SynNMDA.append(tempNMDA)
###########################################
# Adding Pool 2
###########################################
ExNMDA = []
nc_ExNMDA = []
loc2 = list(np.linspace(Loc[0], Loc[1], Pool2_num))
delay2 = random_2(15, 25 + int(Syn_w2 * 60), Pool2_num)
for i in range(Pool2_num):
###########################
# Adding extrasyanptic NMDA
tempNMDA2 = h.nmda(Cell.basal[Bnum](loc2[i]))
tempNMDA2.gmax = 0.005 * Syn_w2
tempNMDA2.onset = delay2[i] + ns.start
ExNMDA.append(tempNMDA2)
###########################################
### Recording
###########################################
t_vec = h.Vector()
t_vec.record(h._ref_t)
v_vec_soma = h.Vector()
v_vec_dend1 = h.Vector()
v_vec_dend2 = h.Vector()
v_vec_dend3 = h.Vector()
v_vec_dend = h.Vector()
v_vec_soma.record(Cell.soma[2](0.5)._ref_v)
v_vec_dend1.record(Cell.basal[Bnum](0.8)._ref_v)
v_vec_dend2.record(Cell.basal[Bnum](0.5)._ref_v)
v_vec_dend3.record(Cell.basal[Bnum](0.3)._ref_v)
v_vec_dend.record(Cell.basal[Bnum](DenLoc)._ref_v)
###########################################
### Run & Plot
###########################################
h.celsius = 32
h.v_init = -73.6927850677
h.init()
h.tstop = 1000
h.run()
# pdb.set_trace() #Debugging
# plt.figure(figsize = (16, 6), dpi = 100)
# plt.plot(t_vec, v_vec_soma, label = 'soma(0.5)', color = 'black')
# plt.plot(t_vec, v_vec_dend1, label = 'Basal['+str(Bnum)+'](0.8)', color = 'red')
# plt.plot(t_vec, v_vec_dend2, label = 'Basal['+str(Bnum)+'](0.5)', color = 'blue')
# plt.plot(t_vec, v_vec_dend3, label = 'Basal['+str(Bnum)+'](0.3)', color = 'green')
# plt.ylim([-90, 40])
# plt.xlim([0, 700])
# plt.legend(loc = 'best')
# plt.ylabel('mV')
# plt.xlabel('Time (ms)')
# plt.title ("Glumate Receptor Activated Plateau Potential")
# save(title, directory, ext="png", close=True, verbose=True)
data = ut.Vividict()
data['TTX'] = TTX
data['SynAMPA']['num'] = Pool1_num
data['SynAMPA']['locs'] = Loc
data['SynAMPA']['weight'] = Syn_w1
data['SynNMDA']['num'] = Pool1_num
data['SynNMDA']['locs'] = Loc
data['SynNMDA']['weight'] = Syn_w1
data['ExNMDA']['num'] = Pool2_num
data['ExNMDA']['locs'] = Loc
data['ExNMDA']['weight'] = Syn_w2
data['recording']['time'] = list(t_vec)
data['recording']['soma']['voltage'] = list(v_vec_soma)
data['recording']['basal']['voltage_0.8'] = list(v_vec_dend1)
data['recording']['basal']['voltage_0.5'] = list(v_vec_dend2)
data['recording']['basal']['voltage_0.3'] = list(v_vec_dend3)
data['recording']['basal']['voltage_input'] = list(v_vec_dend)
ut.savejson(data, title, directory, ext="json", verbose=False)
print 'distal_re dendrites: ', seldist_re, '\n', seldistname_re
print '------------------------------------------------------'
#print seldistname_re[1]
########################################################################
###### randomly selecting n secs from the list of secs
send = []
receive = []
syn = []
nc = []
vec = {}
for counter in range(0, 3): # (0, how many secs to take)
nprox = randint(0, len(selprox_re) - 1)
send.append(selprox_re[nprox])
ndist = randint(0, len(seldist_re) - 1)
receive.append(seldist_re[ndist])
syni = h.AMPA(0.5, sec=receive[counter])
syn.append(syni)
nci = h.NetCon(send[counter](0.5)._ref_v, syni, sec=send[counter])
nci.weight[0] = 0.2
nc.append(nci)
#print selprox_re[nprox]
print "sender[counter] = ", send[counter].name()
print "receiver[counter] = ", receive[counter].name()
#selprox_re.remove(selprox_re[nprox])
#seldist_re.remove(seldist_re[ndist])
# h.run()
########################################################################
'''
def Glu_Stim(TTX = False, Pool1_num = 9, Pool2_num = 9, Beta = 0.067,
Cdur = 1, Syn_w1 = 0.01, Syn_w2 = 0.01, Loc = [0.2, 0.6]):
"""
Model the Glumate Stimulation.
Model the Receptors in 2 pools:
Pool 1: AMPA + NMDA (same synaptic weight, represent spine conductance)
Pool 2: NMDA only (represent the extrasyanptic NMDARs)
Parameters:
-----------
TTX: True or False.
True: setting all the calcium channel conductance to 0.
False: default
Pool1_num: syanptic AMPA/NMDA numbers
Pool2_num: extrasyanptic NMDA numbers
Beta: NMDA Receptors
Cdur: NMDA Receptors
Syn_w1: the syanptic weight of AMPA/NMDA receptors in pool1
Syn_w2: the syanptic weight of AMPA/NMDA receptors in pool2
Loc: the stimulation location
-----------
Outputs:
Figures: recording from soma and 3 different locations from basal dendrites
json: soma and dendritc voltage recording and parameters info
"""
###########################################
Cell = CA229()
timestr = time.strftime("%Y%m%d-%H%M")
data = time.strftime("%m_%d")
directory = 'Data_' + data +'/'
if (TTX == True):
Cell.TTX()
title = "TTX_Pool1_"+ str(Pool1_num) + "_Pool2_" + str(Pool2_num) + "_NMDA_Beta_" + \
str(Beta) + "_NMDA_Cdur_"+str(Cdur)+ "_Pool1_W_" + str(Syn_w1) + \
"_Pool2_W_" + str(Syn_w2) + "_"+ timestr + "_joe"
else:
title = "Pool1_"+ str(Pool1_num) + "_Pool2_" + str(Pool2_num) + "_NMDA_Beta_" + \
str(Beta) + "_NMDA_Cdur_"+str(Cdur)+ "_Pool1_W_" + str(Syn_w1) + \
"_Pool2_W_" + str(Syn_w2) + "_"+ timestr + "_joe"
##########
glutAmp = Syn_w1
glutAmpExSynScale = Syn_w2 / Syn_w1
glutAmpDecay = 0.0 #5.0 # percent/um
synLocMiddle = (loc[0] + loc[1])/2
synLocRadius = synLocMiddle - loc[0]
initDelay = 10.0
synDelay = 0.5 # ms/um
exSynDelay = 1.0 # ms/um
branch_length = 166.25942491682142 # CA229.basal[34].L
numSyns = Pool1_num
numExSyns = Pool2_num
glutAmp = Syn_w1
syn_locs = np.linspace(synLocMiddle-synLocRadius, synLocMiddle+synLocRadius, numSyns)
syn_dists = branch_length * np.abs(syn_locs - synLocMiddle)
syn_weights = glutAmp * (1 - syn_dists * glutAmpDecay/100)
syn_weights = [weight if weight > 0.0 else 0.0 for weight in syn_weights]
syn_delays = initDelay + (synDelay * syn_dists)
exsyn_locs = np.linspace(synLocMiddle-synLocRadius, synLocMiddle+synLocRadius, numExSyns)
exsyn_dists = branch_length * np.abs(exsyn_locs - synLocMiddle)
exsyn_weights = glutAmpExSynScale * glutAmp * (1 - exsyn_dists * glutAmpDecay/100)
exsyn_weights = [weight if weight > 0.0 else 0.0 for weight in exsyn_weights]
exsyn_delays = initDelay + (exSynDelay * exsyn_dists)
###########################################
# Adding Pool 1
###########################################
##### AMPA
SynAMPA = []
nc_AMPA = []
SynNMDA = []
nc_NMDA = []
loc1 = list(np.linspace(Loc[0], Loc[1], Pool1_num))
#delay1 = random_2(10, 50 + int(Syn_w1*50), Pool1_num)
delay1 = syn_delays
ns = h.NetStim()
ns.interval = 20
ns.number = 1
ns.start = 190
ns.noise = 0
print
print("delay1")
print(delay1)
print
print("mean: %f" % (np.mean(delay1)))
print("std : %f" % (np.std(delay1)))
for i in range(Pool1_num):
###########################
# Adding AMPA
SynAMPA.append(h.AMPA(Cell.basal[34](loc1[i])))
SynAMPA[-1].gmax = 0.04
#SynAMPA1[-1].Beta = 0.28
nc_AMPA.append(h.NetCon(ns, SynAMPA[i]))
nc_AMPA[-1].delay = delay1[i] # delay1[i] #uniform(1,20)
nc_AMPA[-1].weight[0] = syn_weights[i] # Syn_w1
#nc_AMPA[-1].threshold = -20
###########################
#Adding NMDA
# SynNMDA.append(h.NMDA(Cell.basal[34](loc1[i])))
# Using NMDAeee.mod file
SynNMDA.append(h.NMDAeee(Cell.basal[34](loc1[i])))
SynNMDA[-1].gmax = 0.01
SynNMDA[-1].Beta = Beta
SynNMDA[-1].Cdur = Cdur
nc_NMDA.append(h.NetCon(ns, SynNMDA[i]))
nc_NMDA[-1].delay = delay1[i] #uniform(1,20)
nc_NMDA[-1].weight[0] = syn_weights[i] # Syn_w1
###########################################
# Adding Pool 2
###########################################
ExNMDA = []
nc_ExNMDA = []
loc2 = list(np.linspace(Loc[0], Loc[1], Pool2_num))
#delay2 = random_2(15, 55 + int(Syn_w2*60), Pool2_num)
delay2 = exsyn_delays
print
print("delay2")
print(delay2)
print
print("mean: %f" % (np.mean(delay2)))
print("std : %f" % (np.std(delay2)))
for i in range(Pool2_num):
###########################
# Adding extrasyanptic NMDA
# ExNMDA.append(h.NMDA(Cell.basal[34](loc2[i])))
# Using NMDAeee.mod file
ExNMDA.append(h.NMDAeee(Cell.basal[34](loc2[i])))
ExNMDA[-1].gmax = 0.01
ExNMDA[-1].Beta = Beta
ExNMDA[-1].Cdur = Cdur
nc_ExNMDA.append(h.NetCon(ns, ExNMDA[i]))
nc_ExNMDA[-1].delay = delay2[i] #uniform(1,20)
nc_ExNMDA[-1].weight[0] = exsyn_weights[i] # Syn_w2 #(i/5.0)*Syn_w2
###########################################
### Recording
###########################################
t_vec = h.Vector()
t_vec.record(h._ref_t)
v_vec_soma = h.Vector()
v_vec_dend1 = h.Vector()
v_vec_dend2 = h.Vector()
v_vec_dend3 = h.Vector()
cai_soma = h.Vector()
cai_dend = h.Vector()
v_vec_soma.record(Cell.soma[2](0.5)._ref_v)
v_vec_dend1.record(Cell.basal[34](0.8)._ref_v)
v_vec_dend2.record(Cell.basal[34](0.5)._ref_v)
v_vec_dend3.record(Cell.basal[34](0.3)._ref_v)
cai_soma.record(Cell.soma[2](0.5)._ref_cai)
cai_dend.record(Cell.basal[34](0.3)._ref_cai)
###########################################
### Run & Plot
### Be careful, vmax does not have value before run
###########################################
h.celsius = 32 # 32
h.v_init = -79.1863191308 #-81.0866900034 #-78.1162028163 #-67.3
h.init()
h.tstop = 1000
h.run()
plt.clf()
plt.close()
plt.figure(figsize = (16, 6), dpi = 100)
plt.plot(t_vec, v_vec_soma, label = 'soma(0.5)', color = 'black')
plt.plot(t_vec, v_vec_dend1, label = 'bdend[34](0.8)', color = 'red')
plt.plot(t_vec, v_vec_dend2, label = 'Basal[34](0.5)', color = 'blue')
plt.plot(t_vec, v_vec_dend3, label = 'Basal[34](0.3)', color = 'green')
plt.ylim([-90, 40])
plt.xlim([0, 800])
plt.legend(loc = 'best')
plt.ylabel('mV')
plt.xlabel('Time (ms)')
plt.title ("Glumate Receptor Activated Plateau Potential")
save(title, directory, ext="png", close=True, verbose=True)
#######################
# Plot the intracelluar calcium concentration
# plt.clf()
# plt.close()
# plt.figure(figsize = (16, 6), dpi = 100)
# plt.plot(t_vec, cai_soma, label = 'soma(0.5)', color = 'black')
# #plt.plot(t_vec, cai_dend, label = 'bdend[34](0.3)', color = 'red')
#
# # plt.ylim([-90, 60])
# plt.xlim([0, 800])
# plt.legend(loc = 'best')
# plt.ylabel('mM')
# plt.xlabel('Time (ms)')
# plt.title ("Calcium concentration")
# title1 = "Calcium_" + title
# save(title1, directory, ext="png", close=True, verbose=True)
data = Vividict()
data['SynAMPA']['num'] = Pool1_num
data['SynAMPA']['locs'] = loc1
data['SynAMPA']['weight'] = Syn_w1
data['SynNMDA']['num'] = Pool1_num
data['SynNMDA']['locs'] = loc1
data['SynNMDA']['weight'] = Syn_w1
data['SynNMDA']['Beta'] = Beta
data['SynNMDA']['Cdur'] = Cdur
data['ExNMDA']['num'] = Pool2_num
data['ExNMDA']['locs'] = loc2
data['ExNMDA']['weight'] = Syn_w2
data['ExNMDA']['Beta'] = Beta
data['ExNMDA']['Cdur'] = Cdur
data['recording']['time'] = list(t_vec)
data['recording']['soma']['voltage'] = list(v_vec_soma)
data['recording']['basal_34']['voltage_0.8'] = list(v_vec_dend1)
data['recording']['basal_34']['voltage_0.5'] = list(v_vec_dend2)
data['recording']['basal_34']['voltage_0.3'] = list(v_vec_dend3)
data['recording']['soma']['ica'] = list(cai_soma)
data['recording']['basal_34']['ica_0.3'] = list(cai_dend)
savejson(data, title, directory, ext = "json", verbose = False)
def syn_stim():
spi6 = initcell("SPI6")
num_syns = 1
factor = 3.0
nclist = []
synlist = []
stim = h.NetStim()
stim.number = 1
stim.interval = 0.0
stim.start = 200
h.tstop = 800
time = h.Vector()
time.record(h._ref_t)
v_soma = h.Vector()
v_soma.record(spi6.soma(0.5)._ref_v)
v_bdend = h.Vector()
v_bdend.record(spi6.Bdend1(0.5)._ref_v)
# Currents in Bdend: ih, nax, kdr, kap, cal, can, kBk
i_ih = h.Vector()
i_ih.record(spi6.Bdend1(0.5).ih._ref_i)
i_na = h.Vector()
i_na.record(spi6.Bdend1(0.5)._ref_ina)
i_k = h.Vector()
i_k.record(spi6.Bdend1(0.5)._ref_ik)
i_ca = h.Vector()
i_ca.record(spi6.Bdend1(0.5)._ref_ica)
for ind in range(num_syns):
nmda_syn = h.NMDA(spi6.Bdend1(0.5))
nc_nmda = h.NetCon(stim, nmda_syn)
nc_nmda.weight[0] = 0.055 * factor
nc_nmda.delay = 10
synlist.append(nmda_syn)
nclist.append(nc_nmda)
ampa_syn = h.AMPA(spi6.Bdend1(0.5))
nc_ampa = h.NetCon(stim, ampa_syn)
nc_ampa.weight[0] = 0.0055 * factor
nc_ampa.delay = 10
synlist.append(ampa_syn)
nclist.append(nc_ampa)
i_nmda = h.Vector()
i_nmda.record(synlist[0]._ref_i)
i_ampa = h.Vector()
i_ampa.record(synlist[1]._ref_i)
h.run()
plt.figure()
ax1 = plt.subplot(311)
plt.plot(time, v_soma, label="Soma")
plt.plot(time, v_bdend, label="Bdend")
plt.ylabel("Membrane Potential (mV)")
plt.legend()
ax2 = plt.subplot(312, sharex=ax1)
plt.plot(time, i_nmda, label="NMDA (" + str(nc_nmda.weight[0]) + ")")
plt.plot(time, i_ampa, label="AMPA (" + str(nc_ampa.weight[0]) + ")")
plt.ylabel("Synaptic Currents (nA)")
plt.legend()
ax3 = plt.subplot(313, sharex=ax1)
plt.plot(time, i_ih, label="H")
plt.plot(time, i_na, label="Na")
plt.plot(time, i_k, label="K")
plt.plot(time, i_ca, label="Ca")
plt.ylabel("Membrane Currents (nA)")
plt.xlabel("Time (ms)")
plt.xlim([200, 800])
plt.legend()
plt.show()