def main():
# create 13 sections: s[0] -- s[12]
s = [h.Section(name='s[%d]' % i) for i in xrange(13)]
"""
Create the tree
s0
s1 s2 s3
s4 s5 s6
s7 s8 s9 s10
"""
for p, c in [[0, 1], [0, 2], [0, 3], [1, 4], [4, 7], [3, 5], [3, 6], [5, 8], [5, 9], [6, 10]]:
s[c].connect(s[p])
"""
and now s11 and s12, connected at the 0 ends
"""
s[11].connect(s[12](0))
# have NEURON print the topology
h.topology()
# now try it our way
morph = MorphologyDB()
for sec in s:
print sec.name() + ':'
print ' children:', ', '.join(child.name() for child in morph.children(sec))
print ' parent:', morph.parent(sec).name() if morph.parent(sec) is not None else 'None'
conns = morph.connections([s[i] for i in [2, 3, 4, 5, 6, 7, 9, 10, 11, 12]])
for p1, p2 in conns:
print '%s(%g) %s(%g)' % (p1[0].name(), p1[1], p2[0].name(), p2[1])
return 0
def run_step(amplitude=0.4):
parameters = {'scaling': 0.5,
'soma': {'Cm': 1., 'Ra': 100., 'El': -70., 'Rm': 10e3, 'area': 1500},
'proximal': {'Ra': 500., 'El': -70., 'L': 500., 'diam': 5.},
'distal': {'Ra': 500., 'El': -70., 'L': 200., 'area': 1500.},
'basal': {'Ra': 500., 'El': -70., 'L': 300., 'diam': 5.}}
# the passive properties of the axon are the same as the soma
parameters['axon'] = parameters['soma'].copy()
# fast sodium current
with_axon = True
#parameters['nat'] = {'gbar_soma': 50, 'gbar_distal': 5, 'lambda': 50, 'dend_mode': 'exponential'}
parameters['nat'] = {'gbar_soma': 100, 'dend_mode': 'linear'}
#parameters['nat'] = {'gbar_soma': 100, 'scaling_dend': 0.2, 'dend_mode': 'constant'}
#parameters['nat'] = {'gbar_soma': 100, 'dend_mode': 'passive'}
if with_axon:
parameters['nat']['gbar_hillock'] = 1000
parameters['nat']['gbar_ais'] = 2500
# delayed rectifier potassium
#parameters['kdr'] = {'gbar_soma': 50, 'gbar_distal': 20, 'lambda': 50, 'dend_mode': 'exponential'}
parameters['kdr'] = {'gbar_soma': 20, 'dend_mode': 'linear'}
# persistent sodium
#parameters['nap'] = {'gbar': 1e-4}
# muscarinic potassium
#parameters['km'] = {'gbar': 20}
# ahp potassium
#parameters['kahp'] = {'gbar': 300}
# K-D
#parameters['kd'] = {'gbar': 1e-4}
# A-type potassium
#parameters['kap'] = {'gbar': 10}
# Ih current
#parameters['ih'] = {'gbar_soma': 1e-2, 'scaling_dend': 10., 'half_dist': 100., 'lambda': 30., 'dend_mode': 'sigmoidal'}
n = SimplifiedNeuron(parameters,with_axon=with_axon)
#n = AThornyNeuron(parameters,with_axon=True)
#n = ThornyNeuron(parameters,with_axon=True)
#n = SWCNeuron(parameters,with_axon=False,convert_to_3pt_soma=False)
#n.save_properties()
h.topology()
rec = make_voltage_recorders(n)
stim = h.IClamp(n.soma[0](0.5))
stim.delay = 200
stim.amp = amplitude
stim.dur = 500
run(tend=1000,V0=-70)
import pylab as p
p.plot(rec['t'],rec['vsoma'],'k',label='Soma')
p.plot(rec['t'],rec['vproximal'],'r',label='Proximal')
p.plot(rec['t'],rec['vdistal'],'g',label='Distal')
p.plot(rec['t'],rec['vbasal'],'b',label='Basal')
if n.has_axon:
p.plot(rec['t'],rec['vhillock'],'c',label='Hillock')
p.plot(rec['t'],rec['vais'],'m',label='AIS')
p.plot(rec['t'],rec['vaxon'],'y',label='Axon')
p.xlabel('Time (ms)')
p.ylabel('Membrane voltage (mV)')
p.legend(loc='best')
p.show()
def show_topology(self, id):
if id < 0:
for i in range(int(self.pc.nhost())):
if(i==pc.id()):
h.topology()
sys.stdout.flush()
self.pc.barrier()
else:
if(id == self.pc.id()):
h.topology()
def run_synaptic_activation():
n = CellA(El=-70., Rm=10e3, dend_scaling=0.5, with_synapses=True)
rate = 2
tend = 1000
c = 0.5
spike_times_c = syn.generate_poisson_spike_times(rate*c, tend*1e-3)[0]
presynaptic_spike_times = []
for synapse in n.synapses:
if np.random.uniform() < 0.: # correlated
spike_times_i = syn.generate_poisson_spike_times(rate*(1-c), tend*1e-3)[0]
spike_times = np.sort(np.append(spike_times_i,spike_times_c))
else: # uncorrelated
spike_times = syn.generate_poisson_spike_times(rate, tend*1e-3)[0]
synapse.set_presynaptic_spike_times(spike_times*1e3)
presynaptic_spike_times.append(spike_times)
h.topology()
rec = make_recorders(n)
for lbl in 'gampa','iampa':
rec[lbl] = h.Vector()
rec['gampa'].record(n.synapses[0].syn._ref_g)
#rec['gnmda'].record(n.synapses[0].syn._ref_gnmda)
rec['iampa'].record(n.synapses[0].syn._ref_i)
#rec['inmda'].record(n.synapses[0].syn._ref_inmda)
nu,edges,count = psth(presynaptic_spike_times,0.05,[0,tend*1e-3])
run_model(tend)
#ax = p.subplot(3,1,1)
hndl = p.figure()
p.plot(rec['t'],rec['vsoma'],'k',label='Soma')
p.plot(1e3*(edges[:-1]+edges[1:])/2,nu*10,'m',label='Rate*10')
#p.plot([0,tend],[rate,rate],'m')
#p.plot([0,tend],[np.mean(nu)*10,np.mean(nu)*10],'b')
p.plot(rec['t'],rec['vapical'],'r',label='Apical')
p.plot(rec['t'],rec['vbasal'],'g',label='Basal')
p.ylabel('Membrane voltage (mV)')
p.legend(loc='best')
#p.subplot(3,1,2,sharex=ax)
#p.plot(rec['t'],np.array(rec['gampa'])*1e3,'k',label='AMPA')
#p.plot(rec['t'],np.array(rec['gnmda'])*1e3,'r',label='NMDA')
#p.legend(loc='best')
#p.ylabel('Conductance (nS)')
#p.subplot(3,1,3,sharex=ax)
#p.plot(rec['t'],np.array(rec['iampa']),'k',label='AMPA')
#p.plot(rec['t'],np.array(rec['inmda']),'r',label='NMDA')
#p.legend(loc='best')
p.xlabel('Time (ms)')
#p.ylabel('Current (nA)')
p.savefig('uncorrelated.pdf')
p.show()
def fix_singlets(self, h):
badsecs = []
for sec in h.allsec():
if sec.n3d() == 1:
badsecs.append(sec)
print(f'Fixing Singlet Section: {str(sec):s}')
# print(dir(sec))
parent = sec.trueparentseg()
print(f' Section has parent {str(parent):s} ')
nparentseg = parent.sec.n3d() - 1
x = parent.sec.x3d(nparentseg)
y = parent.sec.y3d(nparentseg)
z = parent.sec.z3d(nparentseg)
d = parent.sec.diam3d(nparentseg)
h.pt3dinsert(0, x, y, z, d, sec=sec)
# print(dir(sec)) # could also get a child section, but if there are multiple, what do you do?
# child = sec.children()
# print('children: ', child)
# print(f' Section has child: {str(child):s}')
# for c in child:
# print(c.x3d(0))
# exit()
# x = child.sec.x3d(0)
# y = child.sec.y3d(0)
# z = child.sec.z3d(0)
# d = child.sec.diam3d(0)
# h.pt3dadd(x, y, z, d, sec=sec)
badsecs = []
print('fn: ', self.fn)
for sec in h.allsec():
if sec.n3d() == 1:
badsecs.append(sec)
print('badsec: ', sec)
if len(badsecs) == 0:
print('no more bad sections')
h.topology()
def setup_sections(mc):
tree = mc.comp1.tree
pc = h.ParallelContext()
##############################################################
# show mechanisms
h.chk_mcomplex()
##############################################################
# set up sections
sections = []
section_def_template = 'create %s[%d]'
h(section_def_template % (mc.comp1.name, len(tree)))
for sec in h.allsec():
sections.append(sec)
#for i in range(len(tree)):
# sections.append(h.Section(mc.comp1.name+'['+str(i)+']'))
##############################################################
# connect sections
for i,sec in enumerate(sections):
parent = tree[i]
#print "%d to %d" % (i, tree[i])
if(parent != 0):
sec.connect(sections[parent-1], 1, 0)
##############################################################
# insert phisiology
for i,sec in enumerate(sections):
sec.insert('hh')
#print mc.comp1.domain(i)
#if(mc.comp1.mapping[1][i]==0):
# sec.insert('hh')
#else:
# sec.insert('pas')
##############################################################
# show topology
if(pc.id()==0):
print tree
h.topology()
##############################################################
# test split
cplx = h.multisplit()
num_cmp = 0
for sec in h.allsec():
num_cmp += 1
##############################################################
# show topology and result
setup_time = 0
calc_time = 0
if(pc.id()==0):
print "\n##############################################################"
print "# setup time = %.2f sec" % setup_time
print "# calc time = %.2f sec" % calc_time
print "#"
for i in range(int(pc.nhost())):
pc.barrier()
if(i==pc.id()):
print "# %d/%d : %d compartment (%d)" % (pc.id(), pc.nhost(), num_cmp, cplx)
print "#"
for i in range(int(pc.nhost())):
pc.barrier()
if(i==pc.id()):
h.topology()
nseg=80
axon1.nseg=nseg
ax1dia=500
L=10 #mm
axon1.L=L*1000 #um
axon1.diam=500 #um
axon1.insert('hhrx')
stim2=h.Ipulse2(0,sec=axon1)
h.topology()
temperature=np.genfromtxt('nerve_data_1_6s.csv',delimiter=' ')
vec={}
vec['t']=h.Vector()
vec['t'].record(h._ref_t)
for i in range(1,nseg): # recording voltage traces at each node
var='v%d'%i
vec[var]=h.Vector()
vec[var].record(axon1(i/nseg)._ref_v)
h.init()
h.dt=0.025
h.tstop=1600
post.L=10000
post.diam=dia
post.nseg=99
axon1.insert('hh') #control section having HH kinetics, no heat dependence
axon2.insert('hhrx') #modified section having modified HH kinetic for heat dependence
post.insert('hh')
#axon1.localtemp_hhrx=6.3
axon2.localtemp_hhrx=6.3 #in case the temperature is constant along axon2
#axon2.ek=-20 #changes potassium nernst potential of axon2
h.topology() #prints the topology of the geometry
#temp=np.genfromtxt('temp.csv',delimiter=',') #in case temperature of axon2 is custom defined- say gaussian
#for x in range(1,nseg):
# axon2(x/nseg).hhrx.localtemp=temp[x]
# stimulation current injection
stim1=h.IClamp(0,sec=axon1)
stim1.delay=0.1+20 #ms
stim1.dur=1 #ms
stim1.amp=2000 #nA
tstop=50
def print_report():
for sec in h.allsec():
print h.psection()
print h.topology()
