def load_morpho(self, filepath):
"""
:param filepath:
swc file path
"""
if not path.exists(filepath):
raise FileNotFoundError(filepath)
# SWC
fileformat = filepath.split('.')[-1]
if fileformat == 'swc':
morpho = h.Import3d_SWC_read()
# Neurolucida
elif fileformat == 'asc':
morpho = h.Import3d_Neurolucida3()
else:
raise Exception('file format `%s` not recognized' % filepath)
self.all = []
morpho.input(filepath)
i3d = h.Import3d_GUI(morpho, 0)
i3d.instantiate(self)
for hoc_sec in self.all:
name = hoc_sec.name().split('.')[-1] # eg. name="dend[19]"
if len(self.filter_secs(name)) > 0:
raise LookupError(
"The name '%s' is already taken by another section of the cell: '%s' of type: '%s'."
% (name, self.name, self.__class__.__name__))
sec = Sec(hoc_sec, cell=self, name=name)
self.secs.append(sec)
del self.all
def __init__(self, morph):
morph = str(morph)
self.axon = []
cell = h.Import3d_Neurolucida3()
cell.input(morph)
i3d = h.Import3d_GUI(cell, 0)
i3d.instantiate(self)
self.add_axon()
self.init_once()
def _load_geometry(self):
'''Load the morphology-file in NEURON'''
try:
h.sec_counted = 0
except LookupError:
h('sec_counted = 0')
#import the morphology, try and determine format
fileEnding = self.morphology.split('.')[-1]
if fileEnding == 'hoc' or fileEnding == 'HOC':
h.load_file(1, self.morphology)
elif fileEnding == 'py':
geom_func = imp.load_source('shape_3D', self.morphology)
geom_func.shape_3D(self)
else:
neuron.h('objref this')
if fileEnding == 'asc' or fileEnding == 'ASC':
Import = h.Import3d_Neurolucida3()
if not self.verbose:
Import.quiet = 1
elif fileEnding == 'swc' or fileEnding == 'SWC':
Import = h.Import3d_SWC_read()
elif fileEnding == 'xml' or fileEnding == 'XML':
Import = h.Import3d_MorphML()
else:
raise ValueError(
'%s is not a recognised morphology file format!'
).with_traceback('Should be either .hoc, .asc, .swc, .xml!' %
self.morphology)
#assuming now that morphologies file is the correct format
try:
Import.input(self.morphology)
except:
if not hasattr(neuron, 'neuroml'):
raise Exception('Can not import, try and copy the ' + \
'nrn/share/lib/python/neuron/neuroml ' + \
'folder into %s' % neuron.__path__[0])
else:
raise Exception('something wrong with file, see output')
try:
imprt = neuron.h.Import3d_GUI(Import, 0)
except:
raise Exception('See output, try to correct the file')
imprt.instantiate(neuron.h.this)
h.define_shape()
self._create_sectionlists()
def create_model(model_name, morph_file):
h("objref cell, tobj")
morph_file = "../morphs/" + morph_file
model_path = "../PassiveModels/"
h.load_file(model_path + model_name + ".hoc")
h.execute("cell = new " + model_name + "()") #replace?
nl = h.Import3d_Neurolucida3()
nl.quiet = 1
nl.input(morph_file)
imprt = h.Import3d_GUI(nl, 0)
imprt.instantiate(h.cell)
cell = h.cell
cell.geom_nseg()
cell.delete_axon()
cell.biophys()
return cell
def load_morpho(self, filepath, seg_per_L_um=1.0, add_const_segs=11):
"""
:param filepath:
swc file path
:param seg_per_L_um:
how many segments per single um of L, Length. Can be < 1. None is 0.
:param add_const_segs:
how many segments have each section by default.
With each um of L this number will be increased by seg_per_L_um
"""
if not path.exists(filepath):
raise FileNotFoundError()
# SWC
fileformat = filepath.split('.')[-1]
if fileformat == 'swc':
morpho = h.Import3d_SWC_read()
# Neurolucida
elif fileformat == 'asc':
morpho = h.Import3d_Neurolucida3()
else:
raise Exception('file format `%s` not recognized' % filepath)
morpho.input(filepath)
h.Import3d_GUI(morpho, 0)
i3d = h.Import3d_GUI(morpho, 0)
i3d.instantiate(self)
# add all SWC sections to self.secs; self.all is defined by SWC import
new_secs = {}
for sec in self.all:
name = sec.name().split('.')[-1] # eg. name="dend[19]"
new_secs[name] = sec
# change segment number based on seg_per_L_um and add_const_segs
for sec in new_secs.values():
add = int(sec.L * seg_per_L_um) if seg_per_L_um is not None else 0
sec.nseg = add_const_segs + add
self.secs.update(new_secs)
del self.all
def create_model(morphology_file, model_obj_name, instance_name, create_type):
'''Creates a full morphology cell instance from Neurolucida morphology model template filenames
This function is called to create the original instance to be reduced and
to create the control cell
TODO: To support non-Neurolucida morphology files, this function should be CHANGED.
'''
assert instance_name in ('original_cell', 'control_cell'), \
"name must be one of ('original_cell', 'control_cell')"
h("{objref %s}" % instance_name)
model = dict(instance_name=instance_name, model_obj_name=model_obj_name)
if create_type in ('basic', 'human'):
h("{instance_name} = new {model_obj_name}()".format(**model))
# instantiates according to morphology using import3d
nl = h.Import3d_Neurolucida3()
nl.quiet = 1
nl.input(morphology_file)
import_3d = h.Import3d_GUI(nl, 0)
instance_created = getattr(h, instance_name)
# associates cell instance with the morphology file
import_3d.instantiate(instance_created)
# this function should be included in the model.hoc file given as a parameter
instance_created.complete_full_model_creation()
elif create_type in ('bbp', 'bbpactive'):
h('{instance_name} = new {model_obj_name}(1, "{morphology_file}")'.format(
morphology_file=morphology_file, **model))
h('{instance_name} = {instance_name}.CellRef'.format(**model))
elif create_type in ('bbpnew', ):
with chdir(morphology_file[:morphology_file.rindex('/')]):
h('{instance_name} = new {model_obj_name}(0)'.format(**model))
elif create_type in ('hay', 'almog', 'allen'):
h('{instance_name} = new {model_obj_name}("{morphology_file}")'.format(
morphology_file=morphology_file, **model))
return getattr(h, instance_name)
def create_model(model_file,
morph_file,
model_path="../PassiveModels/",
morph_path="../morphs/"):
# creating the model
h.load_file("import3d.hoc")
h.load_file("nrngui.hoc")
h("objref cell, tobj")
h.load_file(model_path + model_file + ".hoc")
h.execute("cell = new " + model_file + "()")
nl = h.Import3d_Neurolucida3()
nl.quiet = 1
nl.input(morph_path + morph_file)
imprt = h.Import3d_GUI(nl, 0)
imprt.instantiate(h.cell)
HCell = h.cell
HCell.geom_nseg()
HCell.create_model()
HCell.biophys()
return HCell
NUMBER_OF_SYNAPSES = len(trees)
PARALLEL_ENV = 1
h.load_file("import3d.hoc")
h.load_file("nrngui.hoc")
h("objref cell, tobj")
path = "../"
morph_file = path + "Morphs/2013_03_06_cell08_876_H41_05_Cell2.ASC"
model_file = "cell0603_08_model_cm_0_45"
model_path = path + "PassiveModels/"
print os.getcwd()
h.load_file(model_path + model_file + ".hoc")
h.execute("cell = new " + model_file + "()") #replace?
nl = h.Import3d_Neurolucida3()
nl.quiet = 1
nl.input(morph_file)
imprt = h.Import3d_GUI(nl, 0)
imprt.instantiate(h.cell)
HCell = h.cell
HCell.geom_nseg()
HCell.create_model()
HCell.biophys()
T_DATA, V_DATA = read_epsp_file(PATH_exp + expname + ".dat")
h.dt = T_DATA[1] - T_DATA[0]
h.steps_per_ms = 1.0 / h.dt
h.tstop = T_DATA[-1]
V_DATA_Neuron = h.Vector(V_DATA.size)
def __init__(self, _id):
self._id = _id
h.load_file('stdlib.hoc')
h.load_file('import3d.hoc')
cell = h.Import3d_Neurolucida3()
cell.input('morphology/GrC2020.asc')
i3d = h.Import3d_GUI(cell, 0)
i3d.instantiate(self)
#Soma channels
self.soma[0].nseg = 1 + (2 * int(self.soma[0].L / 40))
self.soma[0].Ra = 100
self.soma[0].cm = 2
self.soma[0].insert('Leak')
self.soma[0].gmax_Leak = 0.00020821612897999999
self.soma[0].e_Leak = -60
self.soma[0].insert('Kv3_4')
self.soma[0].gkbar_Kv3_4 = 0.00053837153610999998
self.soma[0].insert('Kv4_3')
self.soma[0].gkbar_Kv4_3 = 0.0032501728450999999
self.soma[0].ek = -88
self.soma[0].insert('Kir2_3')
self.soma[0].gkbar_Kir2_3 = 0.00080747403035999997
self.soma[0].insert('GRC_CA')
self.soma[0].gcabar_GRC_CA = 0.00066384354030999998
self.soma[0].insert('Kv1_1')
self.soma[0].gbar_Kv1_1 = 0.0046520692281700003
self.soma[0].insert('Kv1_5')
self.soma[0].gKur_Kv1_5 = 0.00106988075956
self.soma[0].insert('Kv2_2_0010')
self.soma[0].gKv2_2bar_Kv2_2_0010 = 2.5949576899999998e-05
self.soma[0].insert('cdp5_CR')
self.soma[0].push()
self.soma[0].eca = 137.5
h.pop_section()
self.whatami = "GrC_2020_mild"
#DEND
for i in self.dend:
i.nseg = 1 + (2 * int(i.L / 40))
i.Ra = 100
i.cm = 2.5
i.insert('Leak')
i.gmax_Leak = 0.00020424219215
i.e_Leak = -60
i.insert('GRC_CA')
i.gcabar_GRC_CA = 0.01841833779253
i.insert('Kca1_1')
i.gbar_Kca1_1 = 0.02998872868395
i.ek = -88
i.insert('Kv1_1')
i.gbar_Kv1_1 = 0.00010675447184
i.insert('cdp5_CR')
i.push()
i.eca = 137.5
h.pop_section()
#Hilock
self.axon = h.Section(name='hilock', cell=self)
self.axon.L = 1
self.axon.nseg = 1
self.axon.diam = 1.5
self.axon.Ra = 100
self.axon.cm = 2
self.axon.insert('Leak')
self.axon.gmax_Leak = 0.00025295417368000002
self.axon.e_Leak = -60
self.axon.insert('GRC_NA_FHF')
self.axon.gnabar_GRC_NA_FHF = 0.011082499796400001
self.axon.ena = 87.39
self.axon.insert('Kv3_4')
self.axon.gkbar_Kv3_4 = 0.050732563882920002
self.axon.ek = -88
self.axon.insert('GRC_CA')
self.axon.gcabar_GRC_CA = 0.00028797253573000002
self.axon.insert('cdp5_CR')
self.axon.push()
self.axon.eca = 137.5
h.pt3dadd(0.0, 5.62232, 0.0, self.axon.diam)
h.pt3dadd(0.0, 6.62232, 0.0, self.axon.diam)
h.pop_section()
self.axon.connect(self.soma[0], 0, 0)
#AIS
self.ais = h.Section(name='ais', cell=self)
self.ais.L = 10
self.ais.nseg = 1
self.ais.diam = 0.7
self.ais.Ra = 100
self.ais.cm = 1
self.ais.insert('GRC_NA_FHF')
self.ais.gnabar_GRC_NA_FHF = 1.06883116205825
self.ais.ena = 87.39
self.ais.insert('Kv3_4')
self.ais.gkbar_Kv3_4 = 0.034592458064240002
self.ais.ek = -88
self.ais.insert('Leak')
self.ais.gmax_Leak = 0.00025011065810000001
self.ais.e_Leak = -60
self.ais.insert('GRC_CA')
self.ais.gcabar_GRC_CA = 0.00011630629281
self.ais.insert('GRC_KM')
self.ais.gkbar_GRC_KM = 0.00044764153078999998
self.ais.insert('cdp5_CR')
self.ais.push()
self.ais.eca = 137.5
h.pt3dadd(0.0, 6.62232, 0.0, self.ais.diam)
h.pt3dadd(0.0, 16.62232, 0.0, self.ais.diam)
h.pop_section()
lensec = 7
secnumber_aa = int(126 / lensec)
secnumber_pf = int(1000 / lensec)
self.ais.connect(self.axon, 1, 0)
self.HD_aa = [
h.Section(cell=self, name='aa_' + str(x))
for x in range(secnumber_aa)
]
for b in self.HD_aa:
b.L = lensec
b.nseg = 1
b.diam = 0.3
b.Ra = 100
b.cm = 1
b.insert('GRC_NA')
b.gnabar_GRC_NA = 0.029973709719629999
b.ena = 87.39
b.insert('Kv3_4')
b.gkbar_Kv3_4 = 0.0046029972380800003
b.ek = -88
b.insert('Leak')
b.gmax_Leak = 7.8963697590000003e-05
b.e_Leak = -60
b.insert('GRC_CA')
b.gcabar_GRC_CA = 0.00059214434259999998
b.insert('cdp5_CR')
b.push()
b.eca = 137.5
len_initial = 16.62232
len_ending = 7
h.pt3dadd(0.0, len_initial, 0.0, b.diam)
h.pt3dadd(0.0, len_initial + len_ending, 0.0, b.diam)
h.pop_section()
len_initial = len_initial + len_ending
self.HD_pf1 = [
h.Section(cell=self, name='pf_' + str(x))
for x in range(secnumber_pf)
]
for i in self.HD_pf1:
i.L = lensec
i.nseg = 1
i.diam = 0.15
i.Ra = 100
i.cm = 1
i.insert('GRC_NA')
i.gnabar_GRC_NA = 0.01896618618573
i.ena = 87.39
i.insert('Kv3_4')
i.gkbar_Kv3_4 = 0.0094015060525799998
i.ek = -88
i.insert('Leak')
i.gmax_Leak = 4.1272473000000001e-07
i.e_Leak = -60
i.insert('GRC_CA')
i.gcabar_GRC_CA = 0.00064742320254000001
i.insert('cdp5_CR')
i.push()
i.eca = 137.5
len_initial = 142.62232
len_ending = 7
h.pt3dadd(len_initial, len_initial, 0.0, i.diam)
h.pt3dadd(len_initial + len_ending, len_initial, 0.0, i.diam)
h.pop_section()
len_initial = len_initial + len_ending
self.HD_pf2 = [
h.Section(cell=self, name='pf_' + str(x))
for x in range(secnumber_pf)
]
for z in self.HD_pf2:
z.L = lensec
z.nseg = 1
z.diam = 0.15
z.Ra = 100
z.cm = 1
z.insert('GRC_NA')
z.gnabar_GRC_NA = 0.01896618618573
z.ena = 87.39
z.insert('Kv3_4')
z.gkbar_Kv3_4 = 0.0094015060525799998
z.ek = -88
z.insert('Leak')
z.gmax_Leak = 4.1272473000000001e-07
z.e_Leak = -60
z.insert('GRC_CA')
z.gcabar_GRC_CA = 0.00064742320254000001
z.insert('cdp5_CR')
z.push()
z.eca = 137.5
len_initial = 142.62232
len_ending = 7
h.pt3dadd(len_initial, len_initial, 0.0, i.diam)
h.pt3dadd(len_initial - len_ending, len_initial, 0.0, i.diam)
h.pop_section()
len_initial = len_initial - len_ending
#Connections
#AA
for j in range(secnumber_aa - 1):
l = j + 1
self.HD_aa[l].connect(self.HD_aa[j], 1, 0)
#PF
for i in range(secnumber_pf - 1):
l = i + 1
self.HD_pf1[l].connect(self.HD_pf1[i], 1, 0)
self.HD_pf2[l].connect(self.HD_pf2[i], 1, 0)
self.HD_pf1[0].connect(self.HD_aa[secnumber_aa - 1], 1, 0)
self.HD_pf2[0].connect(self.HD_aa[secnumber_aa - 1], 1, 0)
#Axon connection to the AIS
self.HD_aa[0].connect(self.ais, 1, 0)
#Time and Voltage vectors
self.time_vector = h.Vector()
self.time_vector.record(h._ref_t)
self.vm_soma = h.Vector()
self.vm_soma.record(self.soma[0](0.5)._ref_v)
def load(filename,
fileformat=None,
cell=None,
use_axon=True,
xshift=0,
yshift=0,
zshift=0):
"""
Load an SWC from filename and instantiate inside cell. Code kindly provided
by @ramcdougal.
Args:
filename = .swc file containing morphology
cell = Cell() object. (Default: None, creates new object)
filename = the filename of the SWC file
use_axon = include the axon? Default: True (yes)
xshift, yshift, zshift = use to position the cell
Returns:
Cell() object with populated soma, axon, dend, & apic fields
Minimal example:
# pull the morphology for the demo from NeuroMorpho.Org
from PyNeuronToolbox import neuromorphoorg
with open('c91662.swc', 'w') as f:
f.write(neuromorphoorg.morphology('c91662'))
cell = load_swc(filename)
"""
if cell is None:
cell = Cell(name=string.join(filename.split('.')[:-1]))
if fileformat is None:
fileformat = filename.split('.')[-1]
name_form = {1: 'soma[%d]', 2: 'axon[%d]', 3: 'dend[%d]', 4: 'apic[%d]'}
# load the data. Use Import3d_SWC_read for swc, Import3d_Neurolucida3 for
# Neurolucida V3, Import3d_MorphML for MorphML (level 1 of NeuroML), or
# Import3d_Eutectic_read for Eutectic.
if fileformat == 'swc':
morph = h.Import3d_SWC_read()
elif fileformat == 'asc':
morph = h.Import3d_Neurolucida3()
else:
raise Exception('file format `%s` not recognized' % (fileformat))
morph.input(filename)
# easiest to instantiate by passing the loaded morphology to the Import3d_GUI
# tool; with a second argument of 0, it won't display the GUI, but it will allow
# use of the GUI's features
i3d = h.Import3d_GUI(morph, 0)
# get a list of the swc section objects
swc_secs = i3d.swc.sections
swc_secs = [swc_secs.object(i) for i in xrange(int(swc_secs.count()))]
# initialize the lists of sections
sec_list = {1: cell.soma, 2: cell.axon, 3: cell.dend, 4: cell.apic}
# name and create the sections
real_secs = {}
for swc_sec in swc_secs:
cell_part = int(swc_sec.type)
# skip everything else if it's an axon and we're not supposed to
# use it... or if is_subsidiary
if (not (use_axon) and cell_part == 2) or swc_sec.is_subsidiary:
continue
# figure out the name of the new section
if cell_part not in name_form:
raise Exception('unsupported point type')
name = name_form[cell_part] % len(sec_list[cell_part])
# create the section
sec = h.Section(name=name)
# connect to parent, if any
if swc_sec.parentsec is not None:
sec.connect(real_secs[swc_sec.parentsec.hname()](swc_sec.parentx))
# define shape
if swc_sec.first == 1:
h.pt3dstyle(1,
swc_sec.raw.getval(0, 0),
swc_sec.raw.getval(1, 0),
swc_sec.raw.getval(2, 0),
sec=sec)
j = swc_sec.first
xx, yy, zz = [swc_sec.raw.getrow(i).c(j) for i in xrange(3)]
dd = swc_sec.d.c(j)
if swc_sec.iscontour_:
# never happens in SWC files, but can happen in other formats supported
# by NEURON's Import3D GUI
raise Exception('Unsupported section style: contour')
if dd.size() == 1:
# single point soma; treat as sphere
x, y, z, d = [dim.x[0] for dim in [xx, yy, zz, dd]]
for xprime in [x - d / 2., x, x + d / 2.]:
h.pt3dadd(xprime + xshift, y + yshift, z + zshift, d, sec=sec)
else:
for x, y, z, d in zip(xx, yy, zz, dd):
h.pt3dadd(x + xshift, y + yshift, z + zshift, d, sec=sec)
# store the section in the appropriate list in the cell and lookup table
sec_list[cell_part].append(sec)
real_secs[swc_sec.hname()] = sec
cell.all = cell.soma + cell.apic + cell.dend + cell.axon
return cell
def simulate_models_IF(cells_list,
models_dirs,
models_path="../ActiveModels/",
morphs_path="../Morphs/",
save_traces=False,
save_path='simulated_models_IF_Curve/',
thresh=0):
h.load_file("import3d.hoc")
h.steps_per_ms = 25
h.dt = 1.0 / h.steps_per_ms
h.tstop = 1500
h.celsius = 37
h.v_init = -86
for ix, cell in enumerate(cells_list):
model = models_dirs[cell]['model']
print "simulates model", model
h.load_file(models_path + model + ".hoc")
h("objref HCell")
h("HCell = new " + model + "()")
HCell = h.HCell
nl = h.Import3d_Neurolucida3()
# Creating the model
nl.quiet = 1
nl.input(morphs_path + models_dirs[cell]['morph'])
imprt = h.Import3d_GUI(nl, 0)
imprt.instantiate(HCell)
HCell.indexSections(imprt)
HCell.geom_nsec()
HCell.geom_nseg()
HCell.delete_axon()
HCell.insertChannel()
HCell.init_biophys()
HCell.biophys()
# The stimulus
icl = h.IClamp(0.5, sec=HCell.soma[0])
icl.dur = 1000
icl.delay = 120.33
icl.amp = 0
# Record the voltage at the soma
Vsoma = h.Vector()
Vsoma.record(HCell.soma[0](.5)._ref_v)
tvec = h.Vector()
tvec.record(h._ref_t)
HCell.soma[0].push()
MODEL_IF = []
range_amps = range(0, 3000, 100)
bar = progressbar.ProgressBar(max_value=len(range_amps),
widgets=[
' [',
progressbar.Timer(),
'] ',
progressbar.Bar(),
' (',
progressbar.ETA(),
') ',
])
for jx, amp1000 in enumerate(range_amps):
amp = amp1000 / 1000.0
icl.amp = amp
h.init(h.v_init)
h.run()
n = count_spikes(np.array(Vsoma), thresh)
MODEL_IF.append((amp, n))
bar.update(jx)
MODEL_IF = np.array(MODEL_IF)
models_dirs[cell]['I'] = MODEL_IF[:, 0]
models_dirs[cell]['F'] = MODEL_IF[:, 1]
if save_traces:
if not os.path.exists(save_path):
os.mkdir(save_path)
np.savetxt(save_path + model + "IF.txt", np.array(MODEL_IF))
def run_RTHW(WRITE_TO_FILE=1,
output_filename="rise_and_width_synapses_locations.txt"):
# creating the model
h.load_file("import3d.hoc")
h.load_file("nrngui.hoc")
h("objref cell, tobj")
morph_file = "../morphs/2013_03_06_cell08_876_H41_05_Cell2.ASC"
model_file = "cell0603_08_model_cm_0_45"
model_path = "../PassiveModels/"
h.load_file(model_path + model_file + ".hoc")
h.execute("cell = new " + model_file + "()")
nl = h.Import3d_Neurolucida3()
nl.quiet = 1
nl.input(morph_file)
imprt = h.Import3d_GUI(nl, 0)
imprt.instantiate(h.cell)
HCell = h.cell
HCell.geom_nseg()
HCell.create_model()
HCell.biophys()
PLOT_MODE = 0
TAU_1 = 0.3
TAU_2 = 1.8
E_SYN = 0
WEIGHT = 0.0003
E_PAS = -86
Spike_time = 10
DELAY = 0
NUM_OF_SYNAPSES = 1
DT = 0.01
h.tstop = 100
h.v_init = E_PAS
h.steps_per_ms = 1.0 / DT
h.dt = DT
if WRITE_TO_FILE:
f1 = open(output_filename, 'w+')
Stim1 = h.NetStim()
Stim1.interval = 10000
Stim1.start = Spike_time
Stim1.noise = 0
Stim1.number = 1
# for a given synapse, this function run the simulation and calculate its shape index
def calc_rise_and_width(PLOT_MODE=0):
Vvec = h.Vector()
Vvec.record(HCell.soma[0](0.5)._ref_v)
h.init(h.v_init)
h.run()
np_v = np.array(Vvec)
max_idx = np.argmax(np_v)
rise_time = max_idx * DT - Stim1.start
half_v = E_PAS + (np_v[max_idx] - E_PAS) / 2.0
for i in range(max_idx):
if np_v[i] > half_v:
rise_half = i * h.dt
break
for i in range(max_idx, np_v.size):
if np_v[i] < half_v:
decay_half = i * h.dt
break
half_width = decay_half - rise_half
if PLOT_MODE:
print "rise ,", rise_time, " half width ", half_width
np_t = np.arange(0, h.tstop + h.dt, h.dt)
print np_v.size, np_t.size
plt.plot(np_t, np_v, 'b')
plt.plot(np_t[max_idx], np_v[max_idx], 'b*')
plt.plot(np.array([rise_half, decay_half]),
np.array([half_v, half_v]), 'r')
plt.show()
return rise_time, half_width
output_txt = "secanme\tx\tsoma_distance\trise_time\thalf_width\n"
h.distance(sec=HCell.soma[0])
# run over all the electrical segments in the model.
# in each one of them put a synapse and run the simulation.
print "re-creating the theoretical_RTHW"
num_secs = len([sec for sec in HCell.all])
bar = progressbar.ProgressBar(max_value=num_secs,
widgets=[
' [',
progressbar.Timer(),
'] ',
progressbar.Bar(),
' (',
progressbar.ETA(),
') ',
])
for ix, sec in enumerate(HCell.all):
for seg in list(sec) + [sec(1)]:
Syn1 = h.Exp2Syn(seg.x, sec=sec)
Syn1.e = E_SYN
Syn1.tau1 = TAU_1
Syn1.tau2 = TAU_2
Con1 = h.NetCon(Stim1, Syn1)
Con1.weight[0] = WEIGHT
Con1.delay = DELAY
rise_time, half_width = calc_rise_and_width()
output_txt += sec.name() + '\t' + str(seg.x) + '\t' + str(
h.distance(seg.x, sec=sec)) + '\t'
output_txt += str(rise_time) + '\t' + str(half_width) + '\n'
bar.update(ix)
if WRITE_TO_FILE:
f1.write(output_txt)
f1.close()
return output_txt.strip().split("\n")
def __init__(self, _id):
self._id = _id
h.load_file('stdlib.hoc')
h.load_file('import3d.hoc')
cell = h.Import3d_Neurolucida3()
cell.input('morphology/GrC2020.asc')
i3d = h.Import3d_GUI(cell, 0)
i3d.instantiate(self)
#Soma channels
self.soma[0].nseg = 1 + (2 * int(self.soma[0].L / 40))
self.soma[0].Ra = 100
self.soma[0].cm = 2
self.soma[0].insert('Leak')
self.soma[0].gmax_Leak = 0.00027672909671000001
self.soma[0].e_Leak = -60
self.soma[0].insert('Kv3_4')
self.soma[0].gkbar_Kv3_4 = 0.00373151328841
self.soma[0].insert('Kv4_3')
self.soma[0].gkbar_Kv4_3 = 0.0027313162972600002
self.soma[0].ek = -88
self.soma[0].insert('Kir2_3')
self.soma[0].gkbar_Kir2_3 = 0.00094360184424999995
self.soma[0].insert('GRC_CA')
self.soma[0].gcabar_GRC_CA = 0.00029165028328999998
self.soma[0].insert('Kv1_1')
self.soma[0].gbar_Kv1_1 = 0.0031675812802999998
self.soma[0].insert('Kv1_5')
self.soma[0].gKur_Kv1_5 = 0.00107176612352
self.soma[0].insert('Kv2_2_0010')
self.soma[0].gKv2_2bar_Kv2_2_0010 = 6.710092624e-05
self.soma[0].insert('cdp5_CR')
self.soma[0].push()
self.soma[0].eca = 137.5
h.pop_section()
self.whatami = "GrC_2020_mild"
#DEND
for i in self.dend:
i.nseg = 1 + (2 * int(i.L / 40))
i.Ra = 100
i.cm = 2.5
i.insert('Leak')
i.gmax_Leak = 0.00029871180381000001
i.e_Leak = -60
i.insert('GRC_CA')
i.gcabar_GRC_CA = 0.024687091736070001
i.insert('Kca1_1')
i.gbar_Kca1_1 = 0.01185742892862
i.ek = -88
i.insert('Kv1_1')
i.gbar_Kv1_1 = 0.00015853886699000001
i.insert('cdp5_CR')
i.push()
i.eca = 137.5
h.pop_section()
#Hilock
self.axon = h.Section(name='hilock', cell=self)
self.axon.L = 1
self.axon.nseg = 1
self.axon.diam = 1.5
self.axon.Ra = 100
self.axon.cm = 2
self.axon.insert('Leak')
self.axon.gmax_Leak = 0.00031475453130000002
self.axon.e_Leak = -60
self.axon.insert('GRC_NA_FHF')
self.axon.gnabar_GRC_NA_FHF = 0.020910983616370001
self.axon.ena = 87.39
self.axon.insert('Kv3_4')
self.axon.gkbar_Kv3_4 = 0.03097630887484
self.axon.ek = -88
self.axon.insert('GRC_CA')
self.axon.gcabar_GRC_CA = 0.00019803691988000001
self.axon.insert('cdp5_CR')
self.axon.push()
self.axon.eca = 137.5
h.pt3dadd(0.0, 5.62232, 0.0, self.axon.diam)
h.pt3dadd(0.0, 6.62232, 0.0, self.axon.diam)
h.pop_section()
self.axon.connect(self.soma[0], 0, 0)
#AIS
self.ais = h.Section(name='ais', cell=self)
self.ais.L = 10
self.ais.nseg = 1
self.ais.diam = 0.7
self.ais.Ra = 100
self.ais.cm = 1
self.ais.insert('GRC_NA_FHF')
self.ais.gnabar_GRC_NA_FHF = 1.5810107836409499
self.ais.ena = 87.39
self.ais.insert('Kv3_4')
self.ais.gkbar_Kv3_4 = 0.039582385081389997
self.ais.ek = -88
self.ais.insert('Leak')
self.ais.gmax_Leak = 0.00025512657995000002
self.ais.e_Leak = -60
self.ais.insert('GRC_CA')
self.ais.gcabar_GRC_CA = 0.00038160760886000002
self.ais.insert('GRC_KM')
self.ais.gkbar_GRC_KM = 0.00049717923887
self.ais.insert('cdp5_CR')
self.ais.push()
self.ais.eca = 137.5
h.pt3dadd(0.0, 6.62232, 0.0, self.ais.diam)
h.pt3dadd(0.0, 16.62232, 0.0, self.ais.diam)
h.pop_section()
lensec = 7
secnumber_aa = int(126 / lensec)
secnumber_pf = int(1000 / lensec)
self.ais.connect(self.axon, 1, 0)
self.HD_aa = [
h.Section(cell=self, name='aa_' + str(x))
for x in range(secnumber_aa)
]
for b in self.HD_aa:
b.L = lensec
b.nseg = 1
b.diam = 0.3
b.Ra = 100
b.cm = 1
b.insert('GRC_NA')
b.gnabar_GRC_NA = 0.025441894508310001
b.ena = 87.39
b.insert('Kv3_4')
b.gkbar_Kv3_4 = 0.0046504514953399998
b.ek = -88
b.insert('Leak')
b.gmax_Leak = 5.3037170669999997e-05
b.e_Leak = -60
b.insert('GRC_CA')
b.gcabar_GRC_CA = 0.00031374692347000001
b.insert('cdp5_CR')
b.push()
b.eca = 137.5
len_initial = 16.62232
len_ending = 7
h.pt3dadd(0.0, len_initial, 0.0, b.diam)
h.pt3dadd(0.0, len_initial + len_ending, 0.0, b.diam)
h.pop_section()
len_initial = len_initial + len_ending
self.HD_pf1 = [
h.Section(cell=self, name='pf_' + str(x))
for x in range(secnumber_pf)
]
for i in self.HD_pf1:
i.L = lensec
i.nseg = 1
i.diam = 0.15
i.Ra = 100
i.cm = 1
i.insert('GRC_NA')
i.gnabar_GRC_NA = 0.0142518259615
i.ena = 87.39
i.insert('Kv3_4')
i.gkbar_Kv3_4 = 0.0098649550733799999
i.ek = -88
i.insert('Leak')
i.gmax_Leak = 1.4118927999999999e-07
i.e_Leak = -60
i.insert('GRC_CA')
i.gcabar_GRC_CA = 0.00024821458382999999
i.insert('cdp5_CR')
i.push()
i.eca = 137.5
len_initial = 142.62232
len_ending = 7
h.pt3dadd(len_initial, len_initial, 0.0, i.diam)
h.pt3dadd(len_initial + len_ending, len_initial, 0.0, i.diam)
h.pop_section()
len_initial = len_initial + len_ending
self.HD_pf2 = [
h.Section(cell=self, name='pf_' + str(x))
for x in range(secnumber_pf)
]
for z in self.HD_pf2:
z.L = lensec
z.nseg = 1
z.diam = 0.15
z.Ra = 100
z.cm = 1
z.insert('GRC_NA')
z.gnabar_GRC_NA = 0.0142518259615
z.ena = 87.39
z.insert('Kv3_4')
z.gkbar_Kv3_4 = 0.0098649550733799999
z.ek = -88
z.insert('Leak')
z.gmax_Leak = 1.4118927999999999e-07
z.e_Leak = -60
z.insert('GRC_CA')
z.gcabar_GRC_CA = 0.00024821458382999999
z.insert('cdp5_CR')
z.push()
z.eca = 137.5
len_initial = 142.62232
len_ending = 7
h.pt3dadd(len_initial, len_initial, 0.0, i.diam)
h.pt3dadd(len_initial - len_ending, len_initial, 0.0, i.diam)
h.pop_section()
len_initial = len_initial - len_ending
#Connections
#AA
for j in range(secnumber_aa - 1):
l = j + 1
self.HD_aa[l].connect(self.HD_aa[j], 1, 0)
#PF
for i in range(secnumber_pf - 1):
l = i + 1
self.HD_pf1[l].connect(self.HD_pf1[i], 1, 0)
self.HD_pf2[l].connect(self.HD_pf2[i], 1, 0)
self.HD_pf1[0].connect(self.HD_aa[secnumber_aa - 1], 1, 0)
self.HD_pf2[0].connect(self.HD_aa[secnumber_aa - 1], 1, 0)
#Axon connection to the AIS
self.HD_aa[0].connect(self.ais, 1, 0)
#Time and Voltage vectors
self.time_vector = h.Vector()
self.time_vector.record(h._ref_t)
self.vm_soma = h.Vector()
self.vm_soma.record(self.soma[0](0.5)._ref_v)
def __init__(self, _id):
self._id = _id
h.load_file('stdlib.hoc')
h.load_file('import3d.hoc')
cell = h.Import3d_Neurolucida3()
cell.input('morphology/GrC2020.asc')
i3d = h.Import3d_GUI(cell, 0)
i3d.instantiate(self)
#Soma channels
self.soma[0].nseg = 1 + (2 * int(self.soma[0].L / 40))
self.soma[0].Ra = 100
self.soma[0].cm = 2
self.soma[0].insert('Leak')
self.soma[0].gmax_Leak = 0.00029038073716
self.soma[0].e_Leak = -60
self.soma[0].insert('Kv3_4')
self.soma[0].gkbar_Kv3_4 = 0.00076192450951999995
self.soma[0].insert('Kv4_3')
self.soma[0].gkbar_Kv4_3 = 0.0028149683906099998
self.soma[0].ek = -88
self.soma[0].insert('Kir2_3')
self.soma[0].gkbar_Kir2_3 = 0.00074725514701999996
self.soma[0].insert('GRC_CA')
self.soma[0].gcabar_GRC_CA = 0.00060938071783999998
self.soma[0].insert('Kv1_1')
self.soma[0].gbar_Kv1_1 = 0.0056973826455499997
self.soma[0].insert('Kv1_5')
self.soma[0].gKur_Kv1_5 = 0.00083407556713999999
self.soma[0].insert('Kv2_2_0010')
self.soma[0].gKv2_2bar_Kv2_2_0010 = 1.203410852e-05
self.soma[0].insert('cdp5_CR_CAM')
self.soma[0].push()
self.soma[0].eca = 137.5
h.pop_section()
self.whatami = "GrC_2020_mild"
#DEND
for i in self.dend:
i.nseg = 1 + (2 * int(i.L / 40))
i.Ra = 100
i.cm = 2.5
i.insert('Leak')
i.gmax_Leak = 0.00025029700736999997
i.e_Leak = -60
i.insert('GRC_CA')
i.gcabar_GRC_CA = 0.0050012800845900002
i.insert('Kca1_1')
i.gbar_Kca1_1 = 0.010018074546510001
i.ek = -88
i.insert('Kv1_1')
i.gbar_Kv1_1 = 0.00381819207934
i.insert('Ubc_TRP')
i.gtrp_Ubc_TRP = 0.0005
i.insert('cdp5_CR_CAM')
i.push()
i.eca = 137.5
h.pop_section()
#Hilock
self.axon = h.Section(name='hilock', cell=self)
self.axon.L = 1
self.axon.nseg = 1
self.axon.diam = 1.5
self.axon.Ra = 100
self.axon.cm = 2
self.axon.insert('Leak')
self.axon.gmax_Leak = 0.00036958189720000001
self.axon.e_Leak = -60
self.axon.insert('GRC_NA_FHF')
self.axon.gnabar_GRC_NA_FHF = 0.0092880585146199995
self.axon.ena = 87.39
self.axon.insert('Kv3_4')
self.axon.gkbar_Kv3_4 = 0.020373463109149999
self.axon.ek = -88
self.axon.insert('GRC_CA')
self.axon.gcabar_GRC_CA = 0.00057726155447
self.axon.insert('cdp5_CR_CAM')
self.axon.push()
self.axon.eca = 137.5
h.pt3dadd(0.0, 5.62232, 0.0, self.axon.diam)
h.pt3dadd(0.0, 6.62232, 0.0, self.axon.diam)
h.pop_section()
self.axon.connect(self.soma[0], 0, 0)
#AIS
self.ais = h.Section(name='ais', cell=self)
self.ais.L = 10
self.ais.nseg = 1
self.ais.diam = 0.7
self.ais.Ra = 100
self.ais.cm = 1
self.ais.insert('GRC_NA_FHF')
self.ais.gnabar_GRC_NA_FHF = 1.28725006737226
self.ais.ena = 87.39
self.ais.insert('Kv3_4')
self.ais.gkbar_Kv3_4 = 0.0064959534065400001
self.ais.ek = -88
self.ais.insert('Leak')
self.ais.gmax_Leak = 0.00029276697557000002
self.ais.e_Leak = -60
self.ais.insert('GRC_CA')
self.ais.gcabar_GRC_CA = 0.00031198539471999999
self.ais.insert('GRC_KM')
self.ais.gkbar_GRC_KM = 0.00056671971737000002
self.ais.insert('cdp5_CR_CAM')
self.ais.push()
self.ais.eca = 137.5
h.pt3dadd(0.0, 6.62232, 0.0, self.ais.diam)
h.pt3dadd(0.0, 16.62232, 0.0, self.ais.diam)
h.pop_section()
lensec = 7
secnumber_aa = int(126 / lensec)
secnumber_pf = int(1000 / lensec)
self.ais.connect(self.axon, 1, 0)
self.HD_aa = [
h.Section(cell=self, name='aa_' + str(x))
for x in range(secnumber_aa)
]
for b in self.HD_aa:
b.L = lensec
b.nseg = 1
b.diam = 0.3
b.Ra = 100
b.cm = 1
b.insert('GRC_NA')
b.gnabar_GRC_NA = 0.026301636815019999
b.ena = 87.39
b.insert('Kv3_4')
b.gkbar_Kv3_4 = 0.00237386061632
b.ek = -88
b.insert('Leak')
b.gmax_Leak = 9.3640921249999996e-05
b.e_Leak = -60
b.insert('GRC_CA')
b.gcabar_GRC_CA = 0.00068197420273000001
b.insert('cdp5_CR_CAM')
b.push()
b.eca = 137.5
len_initial = 16.62232
len_ending = 7
h.pt3dadd(0.0, len_initial, 0.0, b.diam)
h.pt3dadd(0.0, len_initial + len_ending, 0.0, b.diam)
h.pop_section()
len_initial = len_initial + len_ending
self.HD_pf1 = [
h.Section(cell=self, name='pf_' + str(x))
for x in range(secnumber_pf)
]
for i in self.HD_pf1:
i.L = lensec
i.nseg = 1
i.diam = 0.15
i.Ra = 100
i.cm = 1
i.insert('GRC_NA')
i.gnabar_GRC_NA = 0.017718484492610001
i.ena = 87.39
i.insert('Kv3_4')
i.gkbar_Kv3_4 = 0.0081756804703699993
i.ek = -88
i.insert('Leak')
i.gmax_Leak = 3.5301616000000001e-07
i.e_Leak = -60
i.insert('GRC_CA')
i.gcabar_GRC_CA = 0.00020856833529999999
i.insert('cdp5_CR_CAM')
i.push()
i.eca = 137.5
len_initial = 142.62232
len_ending = 7
h.pt3dadd(len_initial, len_initial, 0.0, i.diam)
h.pt3dadd(len_initial + len_ending, len_initial, 0.0, i.diam)
h.pop_section()
len_initial = len_initial + len_ending
self.HD_pf2 = [
h.Section(cell=self, name='pf_' + str(x))
for x in range(secnumber_pf)
]
for z in self.HD_pf2:
z.L = lensec
z.nseg = 1
z.diam = 0.15
z.Ra = 100
z.cm = 1
z.insert('GRC_NA')
z.gnabar_GRC_NA = 0.017718484492610001
z.ena = 87.39
z.insert('Kv3_4')
z.gkbar_Kv3_4 = 0.0081756804703699993
z.ek = -88
z.insert('Leak')
z.gmax_Leak = 1.4118927999999999e-07
z.e_Leak = -60
z.insert('GRC_CA')
z.gcabar_GRC_CA = 0.00020856833529999999
z.insert('cdp5_CR_CAM')
z.push()
z.eca = 137.5
len_initial = 142.62232
len_ending = 7
h.pt3dadd(len_initial, len_initial, 0.0, i.diam)
h.pt3dadd(len_initial - len_ending, len_initial, 0.0, i.diam)
h.pop_section()
len_initial = len_initial - len_ending
#Connections
#AA
for j in range(secnumber_aa - 1):
l = j + 1
self.HD_aa[l].connect(self.HD_aa[j], 1, 0)
#PF
for i in range(secnumber_pf - 1):
l = i + 1
self.HD_pf1[l].connect(self.HD_pf1[i], 1, 0)
self.HD_pf2[l].connect(self.HD_pf2[i], 1, 0)
self.HD_pf1[0].connect(self.HD_aa[secnumber_aa - 1], 1, 0)
self.HD_pf2[0].connect(self.HD_aa[secnumber_aa - 1], 1, 0)
#Axon connection to the AIS
self.HD_aa[0].connect(self.ais, 1, 0)
#Time and Voltage vectors
self.time_vector = h.Vector()
self.time_vector.record(h._ref_t)
self.vm_soma = h.Vector()
self.vm_soma.record(self.soma[0](0.5)._ref_v)
