def draw(self):
""" Draw a visual representation of the optrode """
from neuron import gui
if not hasattr(self,'gOpt'):
from numpy import pi
self.gOpt=h.Shape(0)
self.gOpt.view(-2100, -2100, 4200, 4200, 230, 450, 200.64, 200.32)
self.gOpt.rotate(0, 0, 0, pi/2, 0, 0)
h.pt3dclear(sec = self.sec)
h.pt3dadd(self.x[0],
self.y[0],
self.z[0],
self.diameter,
sec = self.sec)
h.pt3dadd(self.x[1],
self.y[1],
self.z[1],
self.diameter,
sec = self.sec)
self.pOpt0 = h.IClamp(0,
sec = self.sec)
self.pOpt1 = h.IClamp(1,
sec = self.sec)
self.gOpt.point_mark(self.pOpt0, 1) # make start black
self.gOpt.point_mark(self.pOpt1, 3) # make output blue
self.gOpt.exec_menu("Show Diam")
self.gOpt.exec_menu("3D Rotate")
def __init__(self, pts, master=True):
self.master = master
diam = p.cell_diameter
self.pts = pts
self.sections = {}
self.sl = h.SectionList()
for pt in pts:
gid = org2gid(*pt)
sec = h.Section(name=str(gid))
sec.pt3dclear()
#draw the line a little shorter so we can see the junctions
p1 = xyz(*pt)
p2 = xyz(pt[0], pt[1], pt[2] + 1)
dp = (p2[0] - p1[0], p2[1] - p1[1], p2[2] - p1[2])
x, y, z = p1[0] + .05 * dp[0], p1[1] + .05 * dp[1], p1[
2] + .05 * dp[2]
sec.pt3dadd(x, y, z, diam - 1)
x, y, z = p2[0] - .05 * dp[0], p2[1] - .05 * dp[1], p2[
2] - .05 * dp[2]
sec.pt3dadd(x, y, z, diam - 1)
self.sections[sec] = gid
self.sl.append(sec=sec)
self.sh = h.Shape(self.sl)
self.sh.menu_tool("print info", self.callback)
if not master:
for sec in self.sections:
self.sh.color(gid2org(self.sections[sec])[0] + 1, sec=sec)
def show_synapses_PP_LTP(self):
"""Shows a box containing the model shape plot with marked recording sites and synapses."""
# build dummy current clamps and demarcate locations
dum_CC = [
h.IClamp(self.CA1.lm_medium1(0.1)),
h.IClamp(self.CA1.radTdist2(0.1))
]
for cc in dum_CC:
cc.dur = 0
cc.amp = 0
self.vBoxShape = h.VBox()
self.vBoxShape.intercept(True)
self.shplot = h.Shape()
for sec in self.synapses:
for syn in self.synapses[sec]:
if syn.type == 'AMPA':
self.shplot.point_mark(syn.synapse, 1, 'O', 6)
self.shplot.point_mark(dum_CC[0], 2, 'O', 12)
self.shplot.point_mark(dum_CC[1], 3, 'O', 12)
self.shplot.label("Large circles: recording sites")
self.shplot.label("Black: Synapses")
self.shplot.exec_menu("Whole Scene")
self.shplot.flush()
self.vBoxShape.intercept(0)
self.vBoxShape.map("Spatial distribution of point processes", 1200, 0,
500, 900)
self.shplot.exec_menu("View = plot")
self.shplot.exec_menu("Show Diam")
def present_synapses(SynList, c=2, STYLE=4, SIZE=8):
shp = h.Shape()
shp.rotate(0, 0, 0, 0, 0, 5.25)
shp.show(0)
for syn in SynList:
shp.point_mark(syn, c, STYLE, SIZE)
shp.view(-322, -600, 620, 1500, 800, 0, 800, 1800)
def present_synapses(SynList, c=2, STYLE=4, SIZE=8):
shp_list.append(h.Shape())
shp_list[-1].rotate(0, 0, 0, 0, 0, 5.25)
shp_list[-1].show(0)
for syn in SynList:
shp_list[-1].point_mark(syn, c, STYLE, SIZE)
shp_list[-1].view(-322, -600, 620, 1500, 0 + 100 * len(shp_list), 0, 200,
450)
table_dir = project_dir + 'analysis/tables/'
noise_dir = project_dir + 'scaled_wns_500/'
noise_filename = noise_dir + 'wns_9520_forscaled.txt'
downsample_dir = project_dir + 'bhalla_scaled_9520_downsampled/'
os.chdir(project_dir)
from neuron import h, gui
h.load_file('stdgui.hoc')
h.load_file('stdrun.hoc')
h.load_file('cell.hoc')
# create a Shape plot object
morph = h.Shape()
# 3d plot shape options
morph.show(0) # show as filled volume
morph.show(1) # show as lines through centroids
morph.show(2) # show as linear schematic
# unsure how to programmatically change the size, but it is very to do in the
# gui, so just do that, and make it "whole scene" option
# translate 90 degrees over each axis
morph.rotate(0, 0, 0, 1.5708, 0, 0) # over x axis
morph.rotate(0, 0, 0, 0, 1.5708, 0) # over y axis
morph.rotate(0, 0, 0, 0, 0, 1.5708) # over z axis
# save current view to file as a post script image
def plotShape(axis=None,
includePre=['all'],
includePost=['all'],
showSyns=False,
showElectrodes=False,
synStyle='.',
synSize=3,
dist=0.6,
elev=90,
azim=-90,
cvar=None,
cvals=None,
clim=None,
iv=False,
ivprops=None,
includeAxon=True,
bkgColor=None,
aspect='auto',
axisLabels=False,
kind='shape',
returnPlotter=False,
**kwargs):
"""
Function to plot morphology of network>
"""
from .. import sim
from neuron import h
print('Plotting 3D cell shape ...')
cellsPreGids = [c.gid for c in sim.getCellsList(includePre)
] if includePre else []
cellsPost = sim.getCellsList(includePost)
if not hasattr(sim.net, 'compartCells'):
sim.net.compartCells = [
c for c in cellsPost if type(c) is sim.CompartCell
]
try:
sim.net.defineCellShapes(
) # in case some cells had stylized morphologies without 3d pts
except:
pass
saveFig = False
if 'saveFig' in kwargs:
saveFig = kwargs['saveFig']
fontSize = None
if 'fontSize' in kwargs:
fontSize = kwargs['fontSize']
if not iv: # plot using Python instead of interviews
from mpl_toolkits.mplot3d import Axes3D
from netpyne.support import morphology as morph # code adapted from https://github.com/ahwillia/PyNeuron-Toolbox
# create secList from include
secs = None
# Set cvals and secs
if not cvals and cvar:
cvals = []
secs = []
# weighNorm
if cvar == 'weightNorm':
for cellPost in cellsPost:
cellSecs = list(
cellPost.secs.values()) if includeAxon else [
s for s in list(cellPost.secs.values())
if 'axon' not in s['hObj'].hname()
]
for sec in cellSecs:
if 'weightNorm' in sec:
secs.append(sec['hObj'])
cvals.extend(sec['weightNorm'])
cvals = np.array(cvals)
cvals = cvals / min(cvals)
# numSyns
elif cvar == 'numSyns':
for cellPost in cellsPost:
cellSecs = cellPost.secs if includeAxon else {
k: s
for k, s in cellPost.secs.items()
if 'axon' not in s['hObj'].hname()
}
for secLabel, sec in cellSecs.items():
nseg = sec['hObj'].nseg
nsyns = [0] * nseg
secs.append(sec['hObj'])
conns = [
conn for conn in cellPost.conns
if conn['sec'] == secLabel
and conn['preGid'] in cellsPreGids
]
for conn in conns:
nsyns[int(ceil(conn['loc'] * nseg)) - 1] += 1
cvals.extend(nsyns)
cvals = np.array(cvals)
# voltage
elif cvar == 'voltage':
for cellPost in cellsPost:
cellSecs = cellPost.secs if includeAxon else {
k: s
for k, s in cellPost.secs.items()
if 'axon' not in s['hObj'].hname()
}
for secLabel, sec in cellSecs.items():
for seg in sec['hObj']:
cvals.append(seg.v)
cvals = np.array(cvals)
if not isinstance(cellsPost[0].secs, dict):
print('Error: Cell sections not available')
return -1
if not secs:
secs = [
s['hObj'] for cellPost in cellsPost
for s in list(cellPost.secs.values())
]
if not includeAxon:
secs = [sec for sec in secs if 'axon' not in sec.hname()]
# Plot shapeplot
cbLabels = {
'numSyns': 'Number of synapses per segment',
'weightNorm': 'Weight scaling',
'voltage': 'Voltage (mV)'
}
#plt.rcParams.update({'font.size': fontSize})
if axis is None:
metaFig = MetaFigure(kind=kind, subplots=None, **kwargs)
fig = metaFig.fig
metaFig.ax.remove()
shapeax = fig.add_subplot(111, projection='3d')
metaFig.ax = shapeax
else:
shapeax = axis
fig = axis.figure
metafig = fig.metafig
numRows = np.shape(metafig.ax)[0]
numCols = np.shape(metafig.ax)[1]
axisLoc = np.where(metafig.ax.ravel() == shapeax)[0][0] + 1
plt.delaxes(shapeax)
figIndex = int(str(numRows) + str(numCols) + str(axisLoc))
shapeax = fig.add_subplot(figIndex, projection='3d')
shapeax.elev = elev # 90
shapeax.azim = azim # -90
shapeax.dist = dist * shapeax.dist
plt.axis(aspect)
cmap = plt.cm.viridis #plt.cm.jet #plt.cm.rainbow #plt.cm.jet #YlOrBr_r
morph.shapeplot(h,
shapeax,
sections=secs,
cvals=cvals,
cmap=cmap,
clim=clim)
# fix so that axes can be scaled
ax = plt.gca()
def set_axes_equal(ax):
"""Set 3D plot axes to equal scale.
Make axes of 3D plot have equal scale so that spheres appear as
spheres and cubes as cubes. Required since `ax.axis('equal')`
and `ax.set_aspect('equal')` don't work on 3D.
"""
limits = np.array([
ax.get_xlim3d(),
ax.get_ylim3d(),
ax.get_zlim3d(),
])
origin = np.mean(limits, axis=1)
radius = 0.5 * np.max(np.abs(limits[:, 1] - limits[:, 0]))
_set_axes_radius(ax, origin, radius)
def _set_axes_radius(ax, origin, radius):
x, y, z = origin
ax.set_xlim3d([x - radius, x + radius])
ax.set_ylim3d([y - radius, y + radius])
ax.set_zlim3d([z - radius, z + radius])
ax.set_box_aspect([1, 1, 1]) # IMPORTANT - this is the new, key line
set_axes_equal(ax)
fig.subplots_adjust(left=0, right=1, bottom=0, top=1)
if cvals is not None and len(cvals) > 0:
vmin = np.min(cvals)
vmax = np.max(cvals)
if clim is not None:
vmin = np.min(clim)
vmax = np.max(clim)
sm = plt.cm.ScalarMappable(cmap=cmap,
norm=plt.Normalize(vmin=vmin,
vmax=vmax))
sm._A = [] # fake up the array of the scalar mappable
cb = plt.colorbar(sm,
fraction=0.15,
shrink=0.5,
pad=0.05,
aspect=20)
if cvar:
cb.set_label(cbLabels[cvar], rotation=90, fontsize=fontSize)
if saveFig == 'movie':
cb.ax.set_title('Time = ' + str(round(h.t, 1)),
fontsize=fontSize)
if bkgColor:
shapeax.w_xaxis.set_pane_color(bkgColor)
shapeax.w_yaxis.set_pane_color(bkgColor)
shapeax.w_zaxis.set_pane_color(bkgColor)
#shapeax.grid(False)
# Synapses
if showSyns:
synColor = 'red'
for cellPost in cellsPost:
for sec in list(cellPost.secs.values()):
for synMech in sec['synMechs']:
morph.mark_locations(h,
sec['hObj'],
synMech['loc'],
markspec=synStyle,
color=synColor,
markersize=synSize)
# Electrodes
if showElectrodes:
ax = plt.gca()
colorOffset = 0
if 'avg' in showElectrodes:
showElectrodes.remove('avg')
colorOffset = 1
coords = sim.net.recXElectrode.pos.T[
np.array(showElectrodes).astype(int), :]
ax.scatter(coords[:, 0],
coords[:, 1],
coords[:, 2],
s=150,
c=colorList[colorOffset:len(coords) + colorOffset],
marker='v',
depthshade=False,
edgecolors='k',
linewidth=2)
for i in range(coords.shape[0]):
ax.text(coords[i, 0],
coords[i, 1],
coords[i, 2],
' ' + str(showElectrodes[i]),
fontweight='bold')
cb.set_label(
'Segment total transfer resistance to electrodes (kiloohm)',
rotation=90,
fontsize=fontSize)
if axisLabels:
shapeax.set_xlabel('x (um)')
shapeax.set_ylabel('y (um)')
shapeax.set_zlabel('z (um)')
else:
shapeax.set_xticklabels([])
shapeax.set_yticklabels([])
shapeax.set_zticklabels([])
if axis is None:
metaFig.finishFig(**kwargs)
else:
if saveFig:
if isinstance(saveFig, basestring):
if saveFig == 'movie':
filename = sim.cfg.filename + '_shape_movie_' + str(
round(h.t, 1)) + '.png'
else:
filename = saveFig
else:
filename = sim.cfg.filename + '_shape.png'
plt.savefig(filename, dpi=dpi)
# show fig
# if showFig: _showFigure()
else: # Plot using Interviews
# colors: 0 white, 1 black, 2 red, 3 blue, 4 green, 5 orange, 6 brown, 7 violet, 8 yellow, 9 gray
from neuron import gui
fig = h.Shape()
secList = h.SectionList()
if not ivprops:
ivprops = {'colorSecs': 1, 'colorSyns': 2, 'style': 'O', 'siz': 5}
for cell in [c for c in cellsPost]:
for sec in list(cell.secs.values()):
if 'axon' in sec['hObj'].hname() and not includeAxon: continue
sec['hObj'].push()
secList.append()
h.pop_section()
if showSyns:
for synMech in sec['synMechs']:
if synMech['hObj']:
# find pre pop using conn[preGid]
# create dict with color for each pre pop; check if exists; increase color counter
# colorsPre[prePop] = colorCounter
# find synMech using conn['loc'], conn['sec'] and conn['synMech']
fig.point_mark(synMech['hObj'],
ivprops['colorSyns'],
ivprops['style'], ivprops['siz'])
fig.observe(secList)
fig.color_list(secList, ivprops['colorSecs'])
fig.flush()
fig.show(0) # show real diam
# save figure
if saveFig:
if isinstance(saveFig, basestring):
filename = saveFig
else:
filename = sim.cfg.filename + '_' + 'shape.ps'
fig.printfile(filename)
if not iv:
if axis is None:
if returnPlotter:
return metaFig
else:
return fig, {}
else:
return fig, {}
else:
return fig, {}
Vsoma = h.Vector()
Vsoma.record(HCell.soma[0](.5)._ref_v)
tvec = h.Vector()
tvec.record(h._ref_t)
h.init(h.v_init)
h.run()
np_t = np.array(tvec)
np_v = np.array(Vsoma)
plt.plot(np_t,np_v,c='g')
plt.xlim(100,250)
plt.ylim(-100,50)
shp = h.Shape()
shp.rotate(0,0,0,0,0,3.4)
shp.show(0)
for i in range(NUMBER_OF_SYNAPSES):
shp.point_mark(SynList[i], 4,4,6)
shp.view(-400,-600,800,1600,800,0,400,800)
ps = h.PlotShape()
ps.exec_menu("View = plot")
ps.rotate(0,0,0,0,0,3.4)
ps.view(-400,-600,800,1600,800,0,400,800)
ps.variable("v")
h.load_file("../NEURON_color_maps/TColorMap.hoc")
# c(1)->b(1): green->blue
# d(0)->b(1): yellow->blue
# e(0)->a(0): black->red
a, b, c, d, e = [h.Section(name=n) for n in ['a', 'b', 'c', 'd', 'e']]
b.connect(a)
c.connect(b(1), 1) # connect the 1 end of c to the 1 end of b
d.connect(b)
e.connect(a(0)) # connect the 0 end of e to the 0 end of a
for sec in h.allsec():
sec.nseg = 20
sec.L = 100
for seg in sec:
seg.diam = numpy.interp(seg.x, [0, 1], [10, 40])
s = h.Shape()
s.show(False)
s.color(2, sec=a) # a - red
s.color(3, sec=b) # b - blue
s.color(4, sec=c) # c - green
s.color(5, sec=d) # d - yellow
s.color(1, sec=e) # e - black
h.topology()
h.finitialize()
for sec in h.allsec():
print(sec)
for i in range(sec.n3d()):
print('%d: (%g, %g, %g; %g)' %
(i, sec.x3d(i), sec.y3d(i), sec.z3d(i), sec.diam3d(i)))
if __name__ == '__main__': #this is extremely important for python scripts in windows
filePath = 'C:/Users/mach5/OneDrive/Desktop/SM_granule_golgi_models/data.hdf5'
file2 = 'C:/Users/mach5/OneDrive/Desktop/SM_granule_golgi_models/dataPurk.hdf5'
if os.path.exists(filePath):
os.remove(filePath)
else:
print 'cannot delete file as it is not there'
if os.path.exists(file2):
os.remove(file2)
else:
print 'file 2 is not there'
#del data.hdf5
#del dataPurk.hdf5
start = timeit.default_timer()
pool = mp.Pool(8)
#grcs=[pool.apply_async(setup_grcmorphology,args=(i, grcpositions[i,2], grcpositions[i,4], grcpositions[i,3])) for i in range(100)]
#pcs=[pool.apply_async(setup_pcmorphology,args=(i,pcpositions[i,2],pcpositions[i,4],pcpositions[i,3])) for i in range(10)]
pcs = [
setup_pcmorphology(i, pcpositions[i, 2], pcpositions[i, 4],
pcpositions[i, 3]) for i in range(1)
]
stop = timeit.default_timer()
print('Time:', stop - start)
#pool.close()
#pool.join()
#grcs=[setup_grcmorphology(i,grcpositions[i,2],grcpositions[i,4],grcpositions[i,3]) for i in range(100)]
slt = h.Shape()
slt.view()
plot_synapses(shp,putative_dict,"110426_Sl4_Cl2_4to6",Synlist,COLOR_MAGENTA)
def plot_fig1C2(putative_dict,Synlist):
plot_synapses(shp2,putative_dict,"110426_Sl4_Cl2_6to4",Synlist,COLOR_BLUE)
plot_synapses(shp2,putative_dict,"110322_Sl2_Cl2_6to4",Synlist,COLOR_ORANGE)
Synlist = []
for sec in HCell.basal:
sec.diam = sec.diam*ThickFactor
for sec in HCell.apical:
sec.diam = sec.diam*ThickFactor
putative_dict = putative_synapses()
shp = h.Shape()
shp.show(0)
shp.rotate(0,0,0,0,0,2.8)
shp.view(-400,-600,1000,1700,0,0,800,800)
plot_synapses(shp,putative_dict,"081212_1to5",Synlist,COLOR_RED)
plot_synapses(shp,putative_dict,"110426_Sl4_Cl2_4to6",Synlist,COLOR_MAGENTA)
shp2 = h.Shape()
shp2.show(0)
shp2.rotate(0,0,0,0,0,2.8)
shp2.view(-400,-600,1000,1700,400,0,800,800)
plot_synapses(shp2,putative_dict,"110426_Sl4_Cl2_6to4",Synlist,COLOR_BLUE)
plot_synapses(shp2,putative_dict,"110322_Sl2_Cl2_6to4",Synlist,COLOR_ORANGE)
def SE_3D(
S,
Simdur=400.0,
dt=0.01,
G_EoH=0.055,
StochasticEoH=True,
N=32,
G_Dend=0.032,
tau_Dend=2.0,
gKLT_d=0.0085,
gIH_d=0.001,
gLEAK_d=0.001, # here was 0.0001, 10x the Rm of the stylized? That seemed wrong (TK)
gKLT_s=0.017,
gIH_s=0.002,
cell=1,
debug=False,
EoHseed=-1,
somaticsynapsesonly=0,
impedancetesting=0,
impstim=(0.1, 50.0, 0.0),
Ra=150.0,
impdendtype=0,
impdendid=-1,
listinfo=False,
minD=1.0,
):
"""
Build one of the reconstructed gerbil SBC and give them biophysics.
0: The "Ycell" (cell 0) is a dummy cell.
1: SBC 1220
2: SBC 0119
3: SBC 1220b
4: SBC 0119 simplified (was used for debugging the 3D geometry)
Input Spiketimes are generated outside. Remember, you need N+1 spiketrains
for the synapses (N x dendritic + endbulb)
If you pass somaticsynapsesonly=1 then all the small synapses will be located on
the soma (as requested by reviewer 2 for the JNP submission)
If you pass impedancetesting=1, than a single current source will be attached to
the distal end of a distal dendrite and a sinusoidal current of
impstim[0] Amplitude,impstim[1] frequency and impstim[2] phase will be played.
No synaptic events will occur in this case.
If you pass impedancetesting=-1, than the same as above but a chirp stim is used
which will defined by impfreq[0]->impfreq[1].
impdendtype (default = 0): 0-> connect distal dendrite, 1->proximal dendrite
imdendid (default = -1): which dendrite of type impdendtype to connect
Modified from original code by Elisabeth Koert, 2018
created by Thomas Künzel, 2019-06-28
modified by Thomas Künzel, sept 2020
"""
pretime = 100.0 # ms
#
# Check for impedancetesting and if the user wants it, blank all synaptic inputs
if impedancetesting == 1 or impedancetesting == -1:
G_Dend = 0.0
G_EoH = 0.0
from scipy.signal import chirp
#
if cell == 0:
cellname = "Ycell"
elif cell == 1:
cellname = "1220"
elif cell == 2:
cellname = "0119"
elif cell == 3:
cellname = "1220b"
elif cell == 4:
cellname = "0119simple"
#
# create the model
# soma, ais, axon, proximal, distal = load3D(
# cell=cellname, debug=debug
# ) # see function above
soma, ais, axon, proximal, distal = load3Dnew(
cell=cellname,
debug=debug,
minD=minD,
)
if listinfo:
nprox = len(proximal)
ndist = len(distal)
return nprox, ndist
# axial Resistance and cm
for sec in h.allsec():
sec.Ra = Ra
sec.insert("extracellular")
axon.cm = 0.1
# na
soma.insert("na_fast")
soma.ena = 50
soma.gnabar_na_fast = 0.01 # old ball and stick value was 1e-9
ais.insert("na_fast")
ais.ena = 50
ais.gnabar_na_fast = 0.53
# klt
soma.insert("klt")
soma.ek = -70
soma.gkltbar_klt = gKLT_s
ais.insert("klt")
ais.ek = -70
ais.gkltbar_klt = gKLT_s
# kht
soma.insert("kht")
soma.gkhtbar_kht = 0.013
ais.insert("kht")
ais.ek = -70
ais.gkhtbar_kht = 0.013
# ih
soma.insert("ih")
h.eh_ih = -43
soma.ghbar_ih = gIH_s
for i in range(len(proximal)):
proximal[i].insert("ih")
proximal[i].ghbar_ih = gIH_d
proximal[i].insert("klt")
proximal[i].gkltbar_klt = gKLT_d
# leak
for sec in h.allsec():
sec.insert("leak")
sec.erev_leak = -65
sec.g_leak = gLEAK_d
axon.g_leak = 0.0001
# GENERAL SETTINGS
h.dt = dt # simulation (or "sampling") rate
h.celsius = 35 # simulation global temperature
# shape the endbulb input
EoH_Template = seh.g_template(dt=dt, gmax=G_EoH)
g_exc = seh.make_g_trace(
Simdur + pretime, dt, S[-1] + pretime, EoH_Template, StochasticEoH, 0.1, EoHseed
) # Last spiketimes are for endbulbs
gv_exc = h.Vector(g_exc)
endbulb = h.GClamp(0.4, sec=soma)
endbulb.e = 0
gv_exc.play(endbulb._ref_g, h.dt)
# sloc file containing locations for "randomly" placed synapses -- so they
# are more reproducible
slocfilename = "./cells/" + cellname + "/" + cellname + ".npy"
if os.path.isfile(slocfilename):
has_sloc = True
try:
sloc = np.load(slocfilename, allow_pickle=True)
except:
has_sloc = False
if sloc.shape[0] != N:
has_sloc = False
else:
has_sloc = False
if not has_sloc:
sloc = np.zeros((N, 4))
# shape the small synapses
if N != 0:
vstim = []
syna = []
netcon = []
G_i = G_Dend / N # individual conductance
pos = np.linspace(0.05, 0.95, N)
for a in range(N):
if somaticsynapsesonly == 1:
syna.append(h.ExpSyn(soma(pos[a])))
syna[a].tau = tau_Dend
else:
# define synapse position randomly or from file:
if has_sloc:
onproximal = bool(sloc[a, 0])
thispos = sloc[a, 1]
whichdistal = int(sloc[a, 2])
whichproximal = int(sloc[a, 3])
else:
if np.random.random() > 0.66:
onproximal = True
else:
onproximal = False
thispos = np.random.random() # random position on dendrite
whichdistal = np.random.randint(
0, len(distal) - 1
) # random distal dendrite
whichproximal = np.random.randint(
0, len(proximal) - 1
) # random distal dendrite
sloc[a, 0] = float(onproximal)
sloc[a, 1] = thispos
sloc[a, 2] = float(whichdistal)
sloc[a, 3] = float(whichproximal)
if onproximal:
syna.append(h.ExpSyn(proximal[whichproximal](thispos)))
syna[a].tau = tau_Dend
else:
syna.append(h.ExpSyn(distal[whichdistal](thispos)))
syna[a].tau = tau_Dend
# synapse stimulieren
vstim.append(h.VecStim())
netcon.append(h.NetCon(vstim[a], syna[a])) # connect stimulus and synapse
netcon[a].weight[0] = G_i
spt = h.Vector(S[a] + pretime)
vstim[a].play(spt)
if not has_sloc:
if not somaticsynapsesonly == 1:
np.save(slocfilename, sloc)
#
# Input for impedance testing (as requested by reviewer)
if impedancetesting == 1 or impedancetesting == -1:
if impdendtype == 0:
impclamp = h.IClamp(distal[impdendid](0.99))
impathlen = h.distance(soma(0.5), distal[impdendid](0.99))
else:
impclamp = h.IClamp(proximal[impdendid](0.99))
impathlen = h.distance(soma(0.5), proximal[impdendid](0.99))
impclamp.delay = 0.0
impclamp.dur = 1e9
impclamp.amp = 0.0
if impedancetesting == -1:
t = np.linspace(0.0, Simdur - dt, int(Simdur / dt))
tsec = t / 1000.0
impwave = impstim[0] * chirp(
tsec,
f0=impstim[1][0],
f1=impstim[1][1],
t1=tsec[-1],
method="linear",
)
else:
t = np.linspace(0.0, Simdur - dt, int(Simdur / dt))
f = impstim[1] / 1000.0
impwave = impstim[0] * np.sin(2.0 * np.pi * f * t + impstim[2])
prezeros = np.zeros(int(round(pretime / dt)))
impwave = np.concatenate((prezeros, impwave))
impwave = h.Vector(impwave)
tvec = np.linspace(0.0, (Simdur + pretime) - dt, (Simdur + pretime) / dt)
tvec = h.Vector(tvec)
impwave.play(impclamp._ref_amp, tvec)
else:
impathlen = -1
#
if debug:
from neuron import gui
basename = "figs/shape"
s = h.Shape()
s.show(0)
s.color(2, sec=soma)
s.color(2, sec=axon)
s.color(2, sec=ais)
for pcount, thisprox in enumerate(proximal):
s.color(3 + pcount, sec=thisprox)
if impedancetesting == 1:
s.point_mark(impclamp, 3)
s.label(0, 0, str(impathlen))
varname = "_c" + str(cell) + "_t" + str(impdendtype) + "_d" + str(impdendid)
filename = basename + varname + ".ps"
else:
varname = "_c" + str(cell) + "_syn"
filename = basename + varname + ".ps"
for a in range(N):
s.point_mark(syna[a], 3)
# input("Press Enter to continue...")###uncomment if you want interactive plot
s.printfile(filename)
#
# INFRASTRUCTURE
Vm = h.Vector()
Vm.record(soma(0.5)._ref_v)
#
# ----------SIMULATE--------------
h.finitialize(-63.79)
h.fcurrent()
while h.t < Simdur + pretime:
h.fadvance()
#
# PACK AND EXPORT DATA
Result = {}
tempres_vm = np.array(Vm)
Result["Vm"] = tempres_vm[
int(pretime / dt) : int((Simdur + pretime) / dt)
] # remove "additional" samples
Result["impathlen"] = impathlen
#
# ---------SAY GOODBYE-------------
return Result
