def fix_axon_perisomatic_directed(hobj):
# io.log_info('Fixing Axon like perisomatic')
all_sec_names = []
for sec in hobj.all:
all_sec_names.append(sec.name().split(".")[1][:4])
if 'axon' not in all_sec_names:
io.log_exception('There is no axonal recostruction in swc file.')
else:
beg1, end1, beg2, end2 = get_axon_direction(hobj)
for sec in hobj.axon:
h.delete_section(sec=sec)
h.execute('create axon[2]', hobj)
h.pt3dadd(beg1[0], beg1[1], beg1[2], 1, sec=hobj.axon[0])
h.pt3dadd(end1[0], end1[1], end1[2], 1, sec=hobj.axon[0])
hobj.all.append(sec=hobj.axon[0])
h.pt3dadd(beg2[0], beg2[1], beg2[2], 1, sec=hobj.axon[1])
h.pt3dadd(end2[0], end2[1], end2[2], 1, sec=hobj.axon[1])
hobj.all.append(sec=hobj.axon[1])
hobj.axon[0].connect(hobj.soma[0], 0.5, 0)
hobj.axon[1].connect(hobj.axon[0], 1.0, 0)
hobj.axon[0].L = 30.0
hobj.axon[1].L = 30.0
h.define_shape()
for sec in hobj.axon:
# print "sec.L:", sec.L
if np.abs(30 - sec.L) > 0.0001:
io.log_exception('Axon stub L is less than 30')
def _fix_axon(self):
"""Removes and refixes axon"""
axon_diams = [self._hobj.axon[0].diam, self._hobj.axon[0].diam]
axon_indices = []
for i, sec in enumerate(self._hobj.all):
section_name = sec.name().split(".")[1][:4]
if section_name == 'axon':
axon_diams[1] = sec.diam
axon_indices.append(i)
for sec in self._hobj.axon:
h.delete_section(sec=sec)
h.execute('create axon[2]', self._hobj)
for index, sec in enumerate(self._hobj.axon):
sec.L = 30
sec.diam = 1
self._hobj.axonal.append(sec=sec)
self._hobj.all.append(sec=sec) # need to remove this comment
self._hobj.axon[0].connect(self._hobj.soma[0], 1.0, 0)
self._hobj.axon[1].connect(self._hobj.axon[0], 1.0, 0)
h.define_shape()
def fix_axon_allactive(hobj):
"""Replace reconstructed axon with a stub
Parameters
----------
hobj: instance of a Biophysical template
NEURON's cell object
"""
# find the start and end diameter of the original axon, this is different from the perisomatic cell model
# where diameter == 1.
axon_diams = [hobj.axon[0].diam, hobj.axon[0].diam]
for sec in hobj.all:
section_name = sec.name().split(".")[1][:4]
if section_name == 'axon':
axon_diams[1] = sec.diam
for sec in hobj.axon:
h.delete_section(sec=sec)
h.execute('create axon[2]', hobj)
for index, sec in enumerate(hobj.axon):
sec.L = 30
print axon_diams[index]
sec.diam = axon_diams[index] # 1
hobj.axonal.append(sec=sec)
hobj.all.append(sec=sec) # need to remove this comment
# original allen sdk: hobj.soma[0], 0.5, 0
hobj.axon[0].connect(hobj.soma[0], 1.0, 0)
hobj.axon[1].connect(hobj.axon[0], 1, 0)
h.define_shape()
def fix_axon(hobj):
'''
Replace reconstructed axon with a stub
Parameters
----------
hobj: instance of a Biophysical template
NEURON's cell object
'''
for sec in hobj.axon:
h.delete_section(sec=sec)
h.execute('create axon[2]', hobj)
for sec in hobj.axon:
sec.L = 30
sec.diam = 1
hobj.axonal.append(sec=sec)
hobj.all.append(sec=sec) # need to remove this comment
hobj.axon[0].connect(hobj.soma[0], 0.5, 0)
hobj.axon[1].connect(hobj.axon[0], 1, 0)
h.define_shape()
def fix_axon_allactive_granule(hobj):
"""Replace reconstructed axon with a stub
Parameters
----------
hobj: instance of a Biophysical template
NEURON's cell object
"""
# find the start and end diameter of the original axon, this is different from the perisomatic cell model
# where diameter == 1.
axon_diams = [hobj.axon[0].diam, hobj.axon[0].diam]
h.distance(sec=hobj.soma[0]) # need this to set all distances relative to soma (not sure if from center?)
for sec in hobj.all:
section_name = sec.name().split(".")[1][:4]
if section_name == 'axon':
for seg in sec:
if h.distance(seg.x) > 60:
axon_diams[1] = sec.diam
#if h.distance(0.5, sec) > 60:
# axon_diams[1] = sec.diam
for sec in hobj.axon:
h.delete_section(sec=sec)
h.execute('create axon[2]', hobj)
for index, sec in enumerate(hobj.axon):
sec.L = 30
sec.diam = axon_diams[index] # 1
hobj.axonal.append(sec=sec)
hobj.all.append(sec=sec) # need to remove this comment
hobj.axon[0].connect(hobj.soma[0], 1.0, 0)
hobj.axon[1].connect(hobj.axon[0], 1.0, 0)
h.define_shape()
def fill(self, channels, seed, n_trajectory):
self.seed = seed
self.channels = channels
xmd = self.fitfun.xdat.c()
ymd = h.List()
for i in range(n_trajectory):
seed2 = i
ymd.append(self.datagen(self.channels, self.seed, seed2, xmd))
self.fitfun.set_data(xmd, ymd)
h.execute("setxy()", self.fitfun)
def find_vrest(h, section_name):
h.load_file("stdrun.hoc")
tstop = 100
h.dt = dt = 0.1
soma, sec = fetch_soma_sec(section_name)
h.init()
h.cvode.re_init()
t_vec, soma_vm, sec_vm = record(soma, sec)
h.execute('tstop = 100')
h.run()
vrest = np.array(sec_vm)[-1]
return vrest
def create_morph(swc_file, template):
h.load_file("import3d.hoc")
h.load_file("nrngui.hoc")
h("objref cell, tobj")
h.load_file(template + ".hoc")
h.execute("cell = new " + template + "()") #replace
nl = h.Import3d_SWC_read()
nl.quiet = 1
nl.input(swc_file)
imprt = h.Import3d_GUI(nl, 0)
imprt.instantiate(h.cell)
return h.cell
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 fix_axon(hobj):
"""Replace reconstructed axon with a stub
:param hobj: NEURON's cell object
"""
for sec in hobj.axon:
h.delete_section(sec=sec)
h.execute('create axon[2]', hobj)
for sec in hobj.axon:
sec.L = 30
sec.diam = 1
hobj.axonal.append(sec=sec)
hobj.all.append(sec=sec) # need to remove this comment
hobj.axon[0].connect(hobj.soma[0], 0.5, 0)
hobj.axon[1].connect(hobj.axon[0], 1, 0)
h.define_shape()
def run_sim(h, section_name, v_peak, tau_raise, tau_fall, onset=100):
tstop = 500
h.dt = dt = 0.1
h.load_file("stdrun.hoc")
soma, sec = fetch_soma_sec(section_name)
v_rest = -75.711 # find_vrest(h, section_name)
h.init()
h.cvode.re_init()
s_v, a_v = fetch_soma_apic_pots()
vv = voltage_clamp(tstop, dt, v_rest, v_peak, tau_raise, tau_fall, onset)
vc = h.SEClamp(sec(0.5))
vc.rs = 0.001
vc.dur1 = tstop
vamp = h.Vector(vv)
vamp.play(vc._ref_amp1, h.dt)
t_vec, soma_vm, sec_vm = record(soma, sec)
h.execute('tstop = ' + str(tstop))
h.run()
diff_v = np.array(a_v) - np.array(s_v)
return t_vec, soma_vm, sec_vm, diff_v, vv
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
def fix_axon_allactive_directed(hobj):
all_sec_names = []
for sec in hobj.all:
all_sec_names.append(sec.name().split(".")[1][:4])
if 'axon' not in all_sec_names:
io.log_exception('There is no axonal recostruction in swc file.')
else:
beg1, end1, beg2, end2 = get_axon_direction(hobj)
axon_diams = [hobj.axon[0].diam, hobj.axon[0].diam]
for sec in hobj.all:
section_name = sec.name().split(".")[1][:4]
if section_name == 'axon':
axon_diams[1] = sec.diam
for sec in hobj.axon:
h.delete_section(sec=sec)
h.execute('create axon[2]', hobj)
hobj.axon[0].connect(hobj.soma[0], 1.0, 0)
hobj.axon[1].connect(hobj.axon[0], 1.0, 0)
h.pt3dadd(beg1[0], beg1[1], beg1[2], axon_diams[0], sec=hobj.axon[0])
h.pt3dadd(end1[0], end1[1], end1[2], axon_diams[0], sec=hobj.axon[0])
hobj.all.append(sec=hobj.axon[0])
h.pt3dadd(beg2[0], beg2[1], beg2[2], axon_diams[1], sec=hobj.axon[1])
h.pt3dadd(end2[0], end2[1], end2[2], axon_diams[1], sec=hobj.axon[1])
hobj.all.append(sec=hobj.axon[1])
hobj.axon[0].L = 30.0
hobj.axon[1].L = 30.0
h.define_shape()
for sec in hobj.axon:
# io.log_info('sec.L: {}'.format(sec.L))
if np.abs(30 - sec.L) > 0.0001:
io.log_exception('Axon stub L is less than 30')
segs.append(float(sys.argv[5 + 3 * i]))
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]
import scipy.optimize
import pdb
import math
import progressbar
import glob
import peak_AMPA_cond_per_syn
# 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()
PATH_EPSP = 'ExpEPSP/Dat_Files/'
EPSP_SUFFIX = '.dat'
FIT_PREFIX_1_syn = 'fit_1_synapse/fit_1_syn_'
FIT_PREFIX_5_syn = 'fit_5_synapses/fit_5_syn_'
FIT_SUFFIX = '.txt'
import numpy as np
import matplotlib.pyplot as plt
import sys
import math
h.load_file("import3d.hoc")
h.load_file("nrngui.hoc")
h("objref cell, tobj")
# create the model:
morph_file = "../../morphs/2013_03_06_cell08_876_H41_05_Cell2.ASC"
model_path = "../../ActiveModels/"
model_name = 'model_0603_cell08_cm045'
h.load_file(model_path + model_name + ".hoc")
h.execute("cell = new " + model_name + "()")
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.create_axon()
cell.biophys()
cell.active_biophys()
# Measure distances along the axon
L0 = cell.soma[0].L / 2
L1 = cell.axon[0].L
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")
