def runFor(self, time, early_test=None):
from neuron import h
h.cvode_active(self.config.cvode_active)
if not hasattr(self, "time_flag"):
raise Exception("self.time_flag must be set to the parameter passed into NeuronRunner call")
self.time_flag.value = h.t
h.tstop = h.t + time
t = h.t
while t < h.tstop and h.t < h.tstop:
t += 1.0
h.continuerun(t)
if early_test is not None:
# Notify sim is paused during test eval
self.time_flag.value = -1
t_np, v_np = self.get_tv()
if early_test(t_np, v_np):
return (t_np, v_np)
# Notify change in sim time
self.time_flag.value = h.t
# -1 indicates simulation stopped
self.time_flag.value = -1
# Get the waveform
return self.get_tv()
def _common_config(fit_window_start, fit_window_end, up_data, down_data):
h.v_init = 0
h.tstop = 100
h.cvode_active(1)
v_rec = h.Vector()
t_rec = h.Vector()
v_rec.record(h.soma[0](0.5)._ref_v)
t_rec.record(h._ref_t)
mrf = h.MulRunFitter[0]
gen0 = mrf.p.pf.generatorlist.object(0)
gen0.toggle()
fit0 = gen0.gen.fitnesslist.object(0)
up_t = h.Vector(up_data[:, 0])
up_v = h.Vector(up_data[:, 1])
fit0.set_data(up_t, up_v)
fit0.boundary.x[0] = fit_window_start
fit0.boundary.x[1] = fit_window_end
fit0.set_w()
gen1 = mrf.p.pf.generatorlist.object(1)
gen1.toggle()
fit1 = gen1.gen.fitnesslist.object(0)
down_t = h.Vector(down_data[:, 0])
down_v = h.Vector(down_data[:, 1])
fit1.set_data(down_t, down_v)
fit1.boundary.x[0] = fit_window_start
fit1.boundary.x[1] = fit_window_end
fit1.set_w()
return mrf
def get_number_of_model_state_variables(self, h):
print("Turning on CVODE to compute number of equations...")
h.cvode_active(1)
result = h.Vector()
h.cvode.spike_stat(result)
return int(result[0])
def restore_state(self, state_file='state.bin', keep_events=False):
from neuron import h
ns = h.SaveState()
sf = h.File(os.path.join(self.get_permanent_model_directory(), state_file))
ns.fread(sf)
h.stdinit()
if keep_events:
ns.restore(1)
# Workaround - without the fixed step cycle, NEURON crashes with same error as in:
# https://www.neuron.yale.edu/phpBB/viewtopic.php?f=2&t=3845&p=16542#p16542
# Only happens when there is a vector.play added after a state restore
# Running one cycle using the fixed integration method addresses the problem
h.cvode_active(0)
prev_dt = h.dt
h.dt = 0.000001
h.steprun()
h.dt = prev_dt
# END Workaround
else:
ns.restore()
h.cvode_active(self.config.cvode_active)
def passive_fit_2(info, up_data, down_data):
h.v_init = 0
h.tstop = 100
h.cvode_active(1)
fit_start = 4.0025
v_rec = h.Vector()
t_rec = h.Vector()
v_rec.record(h.soma[0](0.5)._ref_v)
t_rec.record(h._ref_t)
mrf = h.MulRunFitter[0]
gen0 = mrf.p.pf.generatorlist.object(0)
gen0.toggle()
fit0 = gen0.gen.fitnesslist.object(0)
up_t = h.Vector(up_data[:, 0])
up_v = h.Vector(up_data[:, 1])
fit0.set_data(up_t, up_v)
fit0.boundary.x[0] = fit_start
fit0.boundary.x[1] = info["limit"]
fit0.set_w()
gen1 = mrf.p.pf.generatorlist.object(1)
gen1.toggle()
fit1 = gen1.gen.fitnesslist.object(0)
down_t = h.Vector(down_data[:, 0])
down_v = h.Vector(down_data[:, 1])
fit1.set_data(down_t, down_v)
fit1.boundary.x[0] = fit_start
fit1.boundary.x[1] = info["limit"]
fit1.set_w()
minerr = 1e12
for i in range(3):
# Need to re-initialize the internal MRF variables, not top-level proxies
# for randomize() to work
mrf.p.pf.parmlist.object(0).val = 1
mrf.p.pf.parmlist.object(1).val = 10000
mrf.randomize()
mrf.prun()
if mrf.opt.minerr < minerr:
fit_Ri = h.Ri
fit_Cm = h.Cm
fit_Rm = h.Rm
minerr = mrf.opt.minerr
results = {
"ra": fit_Ri,
"cm": fit_Cm,
"rm": fit_Rm,
"err": minerr,
"a1": h.somaaxon_area(),
"a2": h.alldend_area(),
}
return results
def set_abs_tolerance(self, abs_tol):
from neuron import h
# NRN will ignore this using cvode
h.steps_per_ms = 1.0/self.config.collection_period_ms
h.dt = self.config.dt
h.cvode_active(self.config.cvode_active)
h.cvode.condition_order(2)
h.cvode.atol(abs_tol)
def run_simulation():
from neuron import h, gui
h.tstop = setting['simulation']['TSTOP']
h.dt = setting['simulation']['DT']
h.v_init = -65
h.celsius = 35
h.finitialize(-65)
h.fcurrent()
h.cvode_active(0)
h.run()
del h, gui
def test_fastimem():
cells = [Cell(id, 10) for id in range(2)]
# h.topology()
cvode = h.CVode()
ics = h.List("IClamp")
syns = h.List("ExpSyn")
cvode.use_fast_imem(1)
h.finitialize(-65)
run(1.0, ics, 1e-13)
total_syn_g(syns)
h.cvode_active(1)
run(1.0, ics, 1e-12)
cvode.use_fast_imem(0)
h.cvode_active(0)
def simulate(v_init, mainlength, prelength=0, cvode=True):
"""
:param h:
:param v_init:
:param prelength:
:param mainlength:
:param cvode:
"""
h.cvode_active(1 if cvode else 0)
h.finitialize(v_init)
h.tstop = prelength + mainlength
h.fadvance()
h.continuerun(h.tstop)
def simulate(v_init, mainlength, prelength=0, use_cvode=True):
"""
:param h:
:param v_init:
:param prelength:
:param mainlength:
:param cvode:
"""
h.cvode_active(1 if use_cvode else 0)
h.cvode.use_fast_imem(1)
h.cvode.cache_efficient(1)
h.secondorder = 2
h.finitialize(v_init)
h.tstop = prelength + mainlength
h.fadvance()
h.continuerun(h.tstop)
def test():
global s, h
fitglobals.debugon()
h.load_file('nrngui.hoc')
from math import pi
h('create soma')
s = h.soma
s.diam = 10
s.L = 100 / pi / s.diam
s.insert('pas')
s.e_pas = 0
s.g_pas = .5 * .001
h.tstop = 10
h.dt = 0.1
h.v_init = 1
h.load_file('pasfit.ses')
h.cvode_active(1)
def fig6b(cell, voltage_graph, concentration_graph):
"""setup to run Fig 6B
The model is the same as in Fig 3A except some of the sections are
different lengths and the discretization is different.
The cell receives a different stimulus and different time series are
plotted. An additional graph plots sodium concentration as a function of
position."""
# differences to the morphology and discretization from Fig 3A
cell.myelin[0].L = 51
cell.myelin[1].L = 60
cell.myelin[0].nseg = 255
cell.myelin[1].nseg = 300
cell.AIS.L = 48
cell.AIS.nseg = 240
h.cvode_active(True)
# setup the graphs
voltage_graph.view(0, -80, 50, 140, 528, 440, 549, 267.4)
voltage_graph.addvar('soma(0.5).v', cell.soma(0.5)._ref_v)
voltage_graph.addvar('AIS(0.5).v', cell.AIS(0.5)._ref_v, 2, 1)
voltage_graph.addvar('node(0.5).v', cell.node(0.5)._ref_v, 4, 1)
concentration_graph.view(-1, 4, 51, 0.58, 528, 870, 548.1, 238.6)
concentration_graph.addvar('soma(0.5).nai', cell.soma(0.5)._ref_nai)
concentration_graph.addvar('AIS(0.25).nai', cell.AIS(0.25)._ref_nai, 2, 1)
concentration_graph.addvar('node(0.5).nai', cell.node(0.5)._ref_nai, 4, 1)
# additional graph plotting sodium concentration along a path
rvp = h.Graph(False)
rvp.size(-40, 160, 4, 4.58)
rvp.view(-40, 4, 200, 0.58, 534, 18, 544.5, 262)
h.flush_list.append(rvp)
rvp_ = h.RangeVarPlot("nai")
rvp_.begin(cell.ApD(0))
rvp_.end(cell.myelin[1](1))
rvp.addobject(rvp_, 1, 1, 0.8, 0.9)
# start time of current pulse
cell.ic.delay = 10
# default simulation length
h.tstop = 50
def fig3a(cell, voltage_graph, concentration_graph):
"""setup to run Fig 3A"""
h.cvode_active(True)
# setup the graphs
voltage_graph.view(0, -90, 3000, 150, 518, 27, 549, 267.4)
voltage_graph.addvar('soma(0.5).v', cell.soma(0.5)._ref_v)
concentration_graph.view(0, 4, 3000, 0.4, 517, 457, 548.1, 238.6)
concentration_graph.addvar('soma(0.5).nai', cell.soma(0.5)._ref_nai)
concentration_graph.addvar('AIS(0.4).nai', cell.AIS(0.4)._ref_nai, 2, 1)
concentration_graph.addvar('myelin[0](0.2).nai', cell.myelin[0](0.2)._ref_nai, 3, 1)
concentration_graph.addvar('AIS(0.8).nai', cell.AIS(0.8)._ref_nai, 4, 1)
# start time of current pulse
cell.ic.delay = 1000
# default simulation length
h.tstop = 3000
def run_from_steady_state(self, tstop):
self._update_current_sources(tstop)
self._pre_run()
self.parallel_context.set_maxstep(self.default_maxstep)
self.tstop = tstop
h.stdinit()
ns = h.SaveState()
sf = h.File('steady_state.bin')
ns.fread(sf)
#print("Time before restore = %g ms" % h.t)
ns.restore(0)
h.cvode_active(0)
#print("Time after restore = %g ms" % h.t)
#logger.info("Running the simulation until %g ms" % tstop)
if self.tstop > self.t:
self.parallel_context.psolve(self.tstop)
def get_sim_params(self):
params = {}
params["t"] = h.t
params["tstop"] = h.tstop
params["dt"] = h.dt
params["atol"] = h.cvode.atol()
params["celsius"] = h.celsius
params["cvode"] = str(h.cvode_active())
return params
def simple():
soma = h.Section()
soma.insert('pas')
soma.e_pas = -65
#soma.g_pas = 1./200e3
synapse = syn.AMPANMDASynapse(soma, 0.5, 0, 10000)
synapse.set_presynaptic_spike_times([100])
h.nmdafactor_AmpaNmda = 0
rec = {}
for lbl in 't','vsoma','vapical','vbasal','gampa','gnmda','iampa','inmda':
rec[lbl] = h.Vector()
rec['t'].record(h._ref_t)
rec['vsoma'].record(soma(0.5)._ref_v)
rec['gampa'].record(synapse.syn._ref_gampa)
rec['gnmda'].record(synapse.syn._ref_gnmda)
rec['iampa'].record(synapse.syn._ref_iampa)
rec['inmda'].record(synapse.syn._ref_inmda)
h.load_file('stdrun.hoc')
h.celsius = 35
h.cvode_active(1)
h.cvode.maxstep(10)
h.tstop = 500
h.finitialize(soma.e_pas)
h.run()
p.subplot(3,1,1)
p.plot(rec['t'],rec['vsoma'],'k',label='Soma')
p.subplot(3,1,2)
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)
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.show()
def test():
from L5_pyramidal import L5Pyr
cell = L5Pyr()
h.load_file("stdgui.hoc")
h.cvode_active(1)
ns = h.NetStim()
ns.number = 10
ns.start = 100
ns.interval = 50.0
nc = h.NetCon(ns, cell.apicaltuft_ampa)
nc.weight[0] = 0.001
h.tstop = 2000.0
elec = LFPElectrode([0, 100.0, 100.0], pc=h.ParallelContext())
elec.setup()
elec.LFPinit()
h.run()
elec.lfp_final()
ion()
plot(elec.lfp_t, elec.lfp_v)
def play(self, fileroot='cell', show_colorbar=True, show_title=False):
''' Step through cell response over time '''
dt = self.play_dt
tstop = self.play_tstop
nrn.init()
nrn.cvode_active(0)
img_counter=0
f = mlab.gcf()
if show_colorbar: mlab.colorbar(self.mlab_cell)
nrn.initPlot()
nrn.init()
nrn.initPlot()
nrn.init()
for x in xrange(0, int(tstop/dt)):
timestamp = "TIME: %.1f" % (x*dt)
print timestamp
if show_title:
try:
ftitle.text = timestamp
except:
ftitle = mlab.title(timestamp)
nrn.continuerun(x*dt)
dataset = self.mlab_cell.mlab_source.dataset
v = array(self.calculate_voltage())
dataset.point_data.remove_array('data')
array_id = dataset.point_data.add_array(v.T.ravel())
dataset.point_data.get_array(array_id).name = 'data'
dataset.point_data.update()
self.mlab_cell.update_data()
self.mlab_cell.update_pipeline()
if self.save_img:
f.scene.save_png('img/%s_%03d.png' % (fileroot, img_counter))
img_counter += 1
def test_ste():
m1 = model()
# one state ste with two self transitions
var = m1["s"](0.5)._ref_v
thresh1 = h.ref(-50)
thresh2 = h.ref(-10)
result = []
ste = h.StateTransitionEvent(1)
ste.transition(0, 0, var, thresh1, (act, (1, m1, thresh1, result)))
ste.transition(0, 0, var, thresh2, (act, (2, m1, thresh2, result)))
fih = h.FInitializeHandler((on_finit, (ste, result)))
run(5)
print("final v=%g" % m1["s"](0.5).v)
chk_result(2, result, m1)
h.cvode_active(1)
run(20)
chk_result(2, result, m1)
h.cvode_active(1)
h.cvode.condition_order(2)
run(5)
chk_result(2, result, m1)
h.cvode.condition_order(1)
h.cvode_active(0)
# ste associated with point process
del fih, ste
ste = h.StateTransitionEvent(2, m1["ic"])
fih = h.FInitializeHandler((on_finit, (ste, result)))
run(5)
# transition with hoc callback
h("""proc foo() { printf("foo called at t=%g\\n", t) }""")
thresh3 = h.ref(-30)
ste.transition(0, 0, var, thresh3, "foo()")
run(5)
# transition with hoc callback in hoc object
h("""
begintemplate FooSTEtest
objref this
proc foo() { printf("foo in %s called at t=%g\\n", this, t) }
endtemplate FooSTEtest
""")
thresh4 = h.ref(-20)
obj = h.FooSTEtest()
ste.transition(0, 0, var, thresh4, "foo()", obj)
run(5)
del ste, fih
assert h.List("StateTransitionEvent").count() == 0
assert h.List("FInitializeHandler").count() == 0
name = name[:-3]
h.load_file("stdrun.hoc")
dend1 = h.Section()
dend1.diam = 2
dend1.nseg = 101
dend1.L = 50
dend2 = h.Section()
dend2.diam = 2
dend2.nseg = 101
dend2.L = 50
dend2.connect(dend1)
diff_constant = 1
h.cvode_active(True)
r = rxd.Region(h.allsec(), dx=0.25)
rxd.set_solve_type([dend1], dimension=3)
ca = rxd.Species(
r,
d=diff_constant,
atolscale=0.1,
initial=lambda node: 1 if (0.8 < node.x and node.segment in dend1) or
(node.x < 0.2 and node.segment in dend2) else 0,
)
bistable_reaction = rxd.Rate(ca, -ca * (1 - ca) * (0.01 - ca))
h.finitialize()
for i in range(2):
def iclamp_adaptive(cell, sec, i_inj, v_init, tstop, dt, celsius=35, pos_i=0.5, pos_v=0.5, atol=1e-2, continuous=True,
discontinuities=None, interpolate=True):
"""
Runs a NEURON simulation of the cell for the given parameters using adaptive integration.
:param sec: List with 1st entry the name of the section and 2nd entry the index (or None in case of soma)
:type sec: list[str, int]
:param i_inj: Amplitude of the injected current for all times t.
:type i_inj: array_like
:param v_init: Initial membrane potential of the cell.
:type v_init: float
:param tstop: Duration of a whole run.
:type tstop: float
:param dt: Time step.
:type dt: float
:param celsius: Temperature during the simulation (affects ion channel kinetics).
:type celsius: float
:param pos_i: Position of the IClamp on the Section (number between 0 and 1).
:type pos_i: float
:param pos_v: Position of the recording electrode on the Section (number between 0 and 1).
:type pos_v: float
:param atol: Absolute tolerance of the integration.
:type atol: float
:param: continuous: If true, linear interpolation is used to define the values between time points of i_inj.
:type: continuous: bool
:param: discontinuities: Indices where jumps in i_inj occur. This will insert a new point before each discontinuity
in t with the time at the discontinuity and in i_inj with the value from i_inj before the discontinuity.
:type discontinuities: array[int]
:param interpolate: If true, the recorded values for v and t will be linearly interpolated to match dt.
:type interpolate: bool
:return: Membrane potential of the cell and time recorded at each time step.
:rtype: tuple of three ndarrays
"""
# turn on adaptive integration and set tolerance (only works when stdrun.hoc already loaded)
h.cvode_active(1)
h.cvode.atol(1e-8)
section = cell.substitute_section(sec[0], sec[1])
# time
t = np.arange(0, tstop + dt, dt)
# adapt i_vec to cope with discontinuities
if discontinuities is not None:
discontinuities = np.sort(discontinuities)
discontinuities = discontinuities + np.arange(len(discontinuities)) # index shifts by the amount of already inserted values
for discontinuity in discontinuities:
i_inj = np.insert(i_inj, discontinuity, i_inj[discontinuity-1])
t = np.insert(t, discontinuity, t[discontinuity])
# insert an IClamp with the current trace from the experiment
stim, i_vec, t_vec = section.play_current(i_inj, t, pos_i, continuous=continuous)
# record the membrane potential
v_rec = section.record('v', pos_v)
t_rec = h.Vector()
t_rec.record(h._ref_t)
i_rec = h.Vector()
i_rec.record(stim._ref_amp)
# run simulation
h.celsius = celsius
h.v_init = v_init
h.tstop = tstop
h.steps_per_ms = 1 / dt # change steps_per_ms before dt, otherwise dt not changed properly
h.dt = dt
h.run()
v_rec = np.array(v_rec)
t_rec = np.array(t_rec)
#i_rec = np.array(i_rec)
if interpolate:
#i_rec = np.interp(t, t_rec, i_rec)
v_rec = np.interp(t, t_rec, v_rec)
t_rec = np.interp(t, t_rec, t_rec)
t_rec, unique_indices = np.unique(t_rec, return_index=True) # remove double values (from discontinuities)
v_rec = v_rec[unique_indices] # second value is the right one
#i_rec = i_rec[unique_indices] # second value is the right one
return v_rec, t_rec # , i_rec
def run_step_full(amplitude=0.1,passive=True):
parameters = {'swc_filename': '../../SWCs/FINAL/thorny/DH070813-.Edit.scaled.swc',
'proximal_limit': 35., 'scaling': 1,
'soma': {'Cm': 1., 'Ra': 100., 'El': -70., 'Rm': 20e3},
'proximal': {'Ra': 100., 'El': -70.},
'distal': {'Ra': 100., 'El': -70.},
'basal': {'Ra': 100., 'El': -70.}}
# the passive properties of the axon are the same as the soma
parameters['axon'] = parameters['soma'].copy()
cell = SWCNeuron(parameters,with_axon=True,convert_to_3pt_soma=True)
if DEBUG:
for i,sec in enumerate(cell.sections):
print('[%03d] %11s: L = %5.1f um, nseg = %2d, L/nseg = %5.1f um.' % \
(i+1,sec.name(),sec.L,sec.nseg,sec.L/sec.nseg))
stim = h.IClamp(cell.soma[0](0.5))
stim.amp = amplitude
stim.dur = 1000
stim.delay = 250
# the recorders
rec = {'t': h.Vector()}
rec['t'].record(h._ref_t)
for sec in cell.sections:
for seg in sec:
k = '%s_%.3f' % (sec.name(),seg.x)
rec[k] = h.Vector()
rec[k].record(seg._ref_v)
h.load_file('stdrun.hoc')
# run the simulation
h.cvode_active(1)
h.tstop = stim.dur + 2*stim.delay
h.t = 0
h.v_init = -70
h.run()
if 'DH070813' in parameters['swc_filename']:
k = 'apical_6923_0.500'
coeff = 60.
min_v = 65.5
elif 'DH070313' in parameters['swc_filename']:
k = 'apical_5206_0.900'
coeff = 60.
min_v = 62.
nseg = 0
x = []
y = []
v = []
import matplotlib.pyplot as plt
plt.subplot(121)
for sec in cell.sections:
nseg += sec.nseg
for seg in sec:
k = '%s_%.3f' % (sec.name(),seg.x)
v.append(np.max(np.array(rec[k])))
n = np.floor(h.n3d(sec=sec)*seg.x)
x.append(h.x3d(n,sec=sec))
y.append(h.y3d(n,sec=sec))
z = (255-coeff*(min_v+v[-1]))/255
plt.plot(x[-1],y[-1],'.',color=[1-z,1-z,z])
print('There are %d sections for a total of %d segments.' % (len(cell.sections),nseg))
plt.subplot(122)
plt.plot(rec['t'],rec[k],'r')
plt.plot(rec['t'],rec['soma_1_0.500'],'k')
plt.show()
elif method in (8,9):
args = (pc.dt(tid), pc.t(tid), pd1_array, pd2_array)
for c in call:
if c[method] is not None:
c[method](*args)
except:
traceback.print_exc()
rval = -1
return rval
_callback = nonvint_block_prototype(nonvint_block)
set_nonvint_block(_callback)
if __name__ == '__main__':
exec(test) # see above string
s = h.Section()
print "fixed step finitialize"
h.finitialize(0)
print "fixed step fadvance"
h.fadvance()
h.load_file('stdgui.hoc')
print "cvode active"
h.cvode_active(1)
print "cvode step finitialize"
h.finitialize(0)
print "cvode fadvance"
h.fadvance()
def pyrun(ratio=0, loc=0, nsyn=0, sec=h.a10_11, icamps=[], dendrec=True):
print '\n%s starting run' % (gethostname())
iotim = 0
tic = time()
h.tstop = 500
h.tsamp = h.tstop / h.dt + 1
h.synrec = 1
tsamp = int(h.tsamp)
r = h.Random(h.luckyoffset)
r.negexp(h.meanisi)
# stimulated branch
syn = h.synsuper(.5, r, sec=sec)
syndict = dict(sloc=loc, ratio=ratio, e1flag=0)
for name, value in syndict.iteritems():
setattr(syn.syn, name, value)
# initialize nseg with 'active' branches
seclist = [h.a1_111, h.a10_11]
sl2 = h.SectionList()
for sec in seclist:
sl2.append(sec=sec)
poppedsecs = sl2.unique()
h.refreshnseg(h.makeactivelist(sl2))
print 'nseg: %d' % (h.nsegcnt())
h.cvode.cache_efficient(1)
h.cvode_active(0)
# somatic voltage recordings, after nseg initialization
v = h.Vector(tsamp)
v.record(h.soma(.5)._ref_v)
trash = v.label(h.soma.name())
# voltage recording dictionary
vd = {'s': v}
if dendrec:
# dendritic voltage recording
h.distance(0, h.soma_con_pt, sec=h.soma)
d = h.distance(0, sec=sec)
locx = (loc - d) / sec.L
v = h.Vector(tsamp)
v.record(sec(locx)._ref_v)
trash = v.label(sec.name())
vd.update({'d': v})
h.poisson = 1
# 'background' current injection
ic = h.IClamp(0.5, sec=h.soma)
ic.dur = h.tstop
p_tstart = 100 # plot tstart, crop initial rising phase of current injection
ind_tstart = p_tstart / h.dt
t = npy.arange(0, h.tstop - p_tstart + h.dt, h.dt)
fh = newfig(figsize=(4, 8))
for runcnt, icamp in enumerate(icamps):
ic.amp = icamp
syn.syn.nsyn = nsyn
seed1 = float(686)
r1 = h.Random(seed1)
r1.negexp(h.meanisi)
syn.setrand(r1)
# run simulation
h.run()
postrunrecgather(vd)
# plot voltage and synaptic current
ax = fh.add_subplot(6, 1, runcnt + 3)
ax.plot(t, npy.array(vd['d'])[ind_tstart:], c='k', lw=1)
ax = fh.add_subplot(6, 1, runcnt + 5)
ax.plot(t,
-(vd['nis'].sum(0) + vd['ais'].sum(0))[ind_tstart:],
c='r',
lw=1)
ax = fh.add_subplot(6, 1, 1)
for ind, ras in enumerate(vd['rasind']):
ticks = ras[ras >= p_tstart]
ax.scatter(ticks - p_tstart,
npy.ones(ticks.shape) * ind,
marker=[[[0, 0], [0, 1]], 0])
ax.axis((0, h.tstop - p_tstart, 0, ind + 1))
zero = npy.zeros(t.size)
ax = fh.add_subplot(6, 1, 2)
ax.fill_between(t,
zero, (vd['ngs'] / vd['nbs']).sum(0)[ind_tstart:],
lw=2,
color='k')
ax.fill_between(t, zero, vd['ags'].sum(0)[ind_tstart:], lw=2, color='r')
ax.set_xlim([0, h.tstop - p_tstart])
fh.savefig('figs/Figure4.png')
print '%s running %d runs took %d seconds' % (gethostname(), runcnt + 1,
time() - tic)
def steps(step_number, parameters):
# instantiate the class
cell = GrCmono(parameters)
# no table are in use from the mod files
h.usetable_GrG_Na = 0
h.usetable_GrC_pNa = 0
h.usetable_GrC_CaHVA = 0
h.usetable_GrG_KV = 0
h.usetable_GrC_KA = 0
h.usetable_GrC_Kir = 0
h.usetable_GrC_KCa = 0
h.usetable_GrG_KM = 0
# load graph for the membrane voltage in mV
# h('load_file("vm.ses")')
# h.nrncontrolmenu()
# Stimulation data. Del = delay in ms, dur = duration in ms, amp =
# amplitude in nA
stimdata = dict()
if step_number == 0:
stimdata['stim0del'] = 100
stimdata['stim0dur'] = 500
stimdata['stim0amp'] = 0.010
elif step_number == 1:
stimdata['stim1del'] = 100
stimdata['stim1dur'] = 500
stimdata['stim1amp'] = 0.016
elif step_number == 2:
stimdata['stim2del'] = 100
stimdata['stim2dur'] = 500
stimdata['stim2amp'] = 0.022
# totale time of the simulation
stimdata['timeglobal'] = 700
# integration step
h.dt = 0.025
h.cvode_active(1)
# temperature
h.celsius = 30
# time at which the simulation have to end
h.tstop = stimdata['timeglobal']
# initial voltage
h.v_init = -80
if step_number == 0:
# actual current injections, one for each current step.
stim = [h.IClamp(0.5, sec=cell.soma)]
stim[0].delay = stimdata['stim0del']
stim[0].dur = stimdata['stim0dur']
stim[0].amp = stimdata['stim0amp']
elif step_number == 1:
stim2 = [h.IClamp(0.5, sec=cell.soma)]
stim2[0].delay = stimdata['stim1del']
stim2[0].dur = stimdata['stim1dur']
stim2[0].amp = stimdata['stim1amp']
elif step_number == 2:
stim3 = [h.IClamp(0.5, sec=cell.soma)]
stim3[0].delay = stimdata['stim2del']
stim3[0].dur = stimdata['stim2dur']
stim3[0].amp = stimdata['stim2amp']
# Code to record everything.
time = h.Vector()
time.record(h._ref_t)
vm = h.Vector()
vm.record(cell.soma(0.5)._ref_v)
# function to initialize the simulation.
def initialize():
h.finitialize()
h.run()
initialize()
#np.savetxt('trace_%d.txt' % step_number, np.column_stack((np.array(time),np.array(vm))), delimiter = ' ')
return np.array(time), np.array(vm)
def network_func(arr):
""" Evaluates the simulation of the pacemaker nucleus
with the parameters defined in LoParameters and returns
the frequency of oscillations.
@LoParameters: [EK, gKp, gKr]
@returns: frequency of oscillations either 0 or freq > 0
"""
LoParameters = arr[0]
results_index = arr[1]
start = time.time()
# NEURON utilities
h.cvode_active(1)
h.finitialize(-65)
h.celsius = 27
# Biophysical parameters
ek = LoParameters[0]
ena = 50
# Pacemaker cell soma specific
ps_EL = - 70
ps_gNa = 1.0
ps_gK = LoParameters[1]
ps_gL = 0.0001
J = 30
# Pacemaker cell axon specific
pa_EL = - 70
pa_gNa = 0.5
pa_gK = 0.02
pa_gL = 0.001
M = 45
# Relay cell soma specific
rs_EL = -70
rs_gNa = 0.75
rs_gK = LoParameters[2]
rs_gL = 0.0003
K = 60
# Relay cell axon specific
ra_EL = -70
ra_gNa = 0.5
ra_gK = 0.05
ra_gL = 0.001
N = 40
# Synapse Parameters
conduct_rng = [0.5, 10] # nS
# Topology Parameters
n_pacemakers = 87
n_relays = 20
n_p2p_projections = 6
n_p2r_projections = 7
# Duration Parameters
T_STOP = 100 # (ms)
# Object storage
pacemaker_cells = []
relay_cells = []
pace_network_graph = pnm.Graph()
# Build Synapse Adjacency List and initialize cell objects.
for i in range(n_pacemakers + n_relays):
# num vertices = num cells in network
pace_network_graph.add_vertex(i)
if i < n_pacemakers: # LOOKING AT A PACEMAKER CELL
p_p_projections = rdm.sample(range(0, n_pacemakers),
n_p2p_projections)
p_r_projections = rdm.sample(range(n_pacemakers,
n_relays + n_pacemakers),
n_p2r_projections)
while i in p_p_projections: # DON'T WANT TO SYNAPSE ONTO SELF
p_p_projections = rdm.sample(range(0, n_pacemakers),
n_p2p_projections)
# Now have 2 unique lists of all cells that cell #i synapses to.
# Add cell i's projections to graph
all_projections = p_p_projections + p_r_projections
for proj in all_projections:
pace_network_graph.add_edge([i, proj])
pacemaker_cells.append(
pnm.PacemakerCell([ek, ena, ps_EL, ps_gNa, ps_gK, ps_gL, J],
[ek, ena, pa_EL, pa_gNa, pa_gK, pa_gL, M],
i))
else: # LOOKING AT A RELAY CELL
# Relay cells don't synapse to anything.
relay_cells.append(
pnm.RelayCell([ek, ena, rs_EL, rs_gNa, rs_gK, rs_gL, K],
[ek, ena, ra_EL, ra_gNa, ra_gK, ra_gL, N],
i))
all_cells = pacemaker_cells + relay_cells
"""
Orient objects in 3D-space with polar coordinates (position, rotation)
where the center of the coordinate system corresponds to
the center of the pacemaker nucleus cell network.
Default neuronal orientation before repositioning and rotation
y z y
^ ^> ^
| / | _______
|/ |( )_______________________________________
|------> (0,0)+(-So>ma-)______________Axon_____________________----> x
| |(_______)
| |
V v
"""
t_pace = pacemaker_cells[0]
t_relay = relay_cells[0]
len_pace = t_pace.give_len("soma") + t_pace.give_len("axon")
len_relay = t_relay.give_len("soma") + t_relay.give_len("axon")
dt_pace = 2 * pi / n_pacemakers
dt_relay = 2 * pi / n_relays
# First for relay cells (position, rotation)
for relay, cell in enumerate(relay_cells):
cell.set_position((len_relay + 10) * cos(pi + (dt_relay * relay)),
(len_relay + 10) * sin(pi + (dt_relay * relay)),
0)
cell.rotateZ(relay * (2 * pi / n_relays))
# Second for pacemaker cells (position, rotation)
for pace, cell in enumerate(pacemaker_cells):
cell.set_position(
(len_pace + len_relay + 10.001) * cos(pi + (dt_pace * pace)),
(len_pace + len_relay + 10.001) * sin(pi + (dt_pace * pace)),
0)
cell.rotateZ(pace * (2 * pi / n_pacemakers))
# For dict entries, create a Synapse b/t the key cell & each payload cell.
for key in pace_network_graph.vertices():
pre_syn_cell = all_cells[key]
for ident in pace_network_graph.edges_of_vertex(key):
post_syn_cell = all_cells[ident]
pre_syn_cell.add_synapse(post_syn_cell, conduct_rng)
# Begin simulation of model
#print(f"Starting simulation {list(LoParameters)} on pc={pc.id()}")
h.tstop = T_STOP
h.run()
# Simulation analysis
time_spikes_pace_somas = []
time_spikes_pace_axons = []
time_spikes_relay_somas = []
time_spikes_relay_axons = []
soma_frequencies = []
axon_frequencies = []
# Determine if oscillating spontaneously
last_ps_t = None
last_rs_t = None
for i, cell in enumerate(all_cells):
soma_v, axon_v, t_v = cell.give_spikes() # time vectors
soma_f = len(list(soma_v)) / (T_STOP * 0.001)
axon_f = len(list(axon_v)) / (T_STOP * 0.001)
if i < 87:
time_spikes_pace_somas.append(len(list(soma_v)))
time_spikes_pace_axons.append(len(list(axon_v)))
if time_spikes_pace_somas[-1] > 0:
last_ps_t = list(soma_v)[-1]
elif i < 107:
time_spikes_relay_somas.append(len(list(soma_v)))
time_spikes_relay_axons.append(len(list(axon_v)))
if time_spikes_relay_somas[-1] > 0:
last_rs_t = list(soma_v)[-1]
soma_frequencies.append(soma_f)
axon_frequencies.append(axon_f)
# Store raw cellular spike data for this simulation.
try:
frequencies = [mode(soma_frequencies[0:87]),
mode(axon_frequencies[0:87]),
mode(soma_frequencies[87:]),
mode(axon_frequencies[87:])]
except StatisticsError:
frequencies = None
if frequencies is not None \
and last_ps_t is not None \
and last_rs_t is not None \
and (len(set(frequencies)) <=2) \
and (np.isclose(frequencies[0], frequencies[3], rtol=0.15)) \
and (np.isclose(frequencies[0], frequencies[2], rtol=0.15)) \
and (np.isclose(frequencies[0], frequencies[1], rtol=0.15)) \
and (frequencies[0] > 40) \
and (frequencies[2] > 40) \
and ((T_STOP - last_ps_t) < (T_STOP / 3 + 7)) \
and ((T_STOP - last_rs_t) < (T_STOP / 3 + 7)):
try:
freq = mode(soma_frequencies)
"""pnm.raster(
f"/Users/daniel/Desktop/Development/PacemakerNucleus/laptop"
f"/sims",
LoParameters, all_cells, frequencies)"""
except StatisticsError:
freq = -1e-15
# print("stats error")
else:
freq = -1e-15
#print(freq)
"""pnm.raster(f"/scratch/hartman.da/scratch_3D_resim_code/rasters",
LoParameters, all_cells, [p_s_f, p_a_f, r_s_f, r_a_f])
pnm.cellular_potentials(f"/scratch/hartman.da/scratch_3D_resim_code"
f"/mem_potentials", LoParameters, all_cells,
[p_s_f, r_s_f])"""
end = time.time()
"""simulation = Sim.Sim(LoParameters, freq, [], end - start, results_index)
with open(f"{pickled_sims_dir}/Sim_{list(LoParameters)}.pkl", "wb") as \
sim_file:
pkl.dump(simulation, sim_file)"""
print(end-start)
return freq, results_index
def steps(step_number, parameters):
# instantiate the class
cell = GrCmono(parameters)
# no table are in use from the mod files
h.usetable_GrG_Na = 0
h.usetable_GrC_pNa = 0
h.usetable_GrC_CaHVA = 0
h.usetable_GrG_KV = 0
h.usetable_GrC_KA = 0
h.usetable_GrC_Kir = 0
h.usetable_GrC_KCa = 0
h.usetable_GrG_KM = 0
# load graph for the membrane voltage in mV
# h('load_file("vm.ses")')
# h.nrncontrolmenu()
# Stimulation data. Del = delay in ms, dur = duration in ms, amp =
# amplitude in nA
stimdata = dict()
if step_number == 0:
stimdata['stim0del'] = 100
stimdata['stim0dur'] = 500
stimdata['stim0amp'] = 0.010
elif step_number == 1:
stimdata['stim1del'] = 100
stimdata['stim1dur'] = 500
stimdata['stim1amp'] = 0.016
elif step_number == 2:
stimdata['stim2del'] = 100
stimdata['stim2dur'] = 500
stimdata['stim2amp'] = 0.022
# totale time of the simulation
stimdata['timeglobal'] = 700
# integration step
h.dt = 0.025
h.cvode_active(1)
# temperature
h.celsius = 30
# time at which the simulation have to end
h.tstop = stimdata['timeglobal']
# initial voltage
h.v_init = -80
if step_number == 0:
# actual current injections, one for each current step.
stim = [h.IClamp(0.5, sec=cell.soma)]
stim[0].delay = stimdata['stim0del']
stim[0].dur = stimdata['stim0dur']
stim[0].amp = stimdata['stim0amp']
elif step_number == 1:
stim2 = [h.IClamp(0.5, sec=cell.soma)]
stim2[0].delay = stimdata['stim1del']
stim2[0].dur = stimdata['stim1dur']
stim2[0].amp = stimdata['stim1amp']
elif step_number == 2:
stim3 = [h.IClamp(0.5, sec=cell.soma)]
stim3[0].delay = stimdata['stim2del']
stim3[0].dur = stimdata['stim2dur']
stim3[0].amp = stimdata['stim2amp']
# Code to record everything.
time = h.Vector()
time.record(h._ref_t)
vm = h.Vector()
vm.record(cell.soma(0.5)._ref_v)
# function to initialize the simulation.
def initialize():
h.finitialize()
h.run()
initialize()
#np.savetxt('trace_%d.txt' % step_number, np.column_stack((np.array(time),np.array(vm))), delimiter = ' ')
return np.array(time), np.array(vm)
def model(Duration,Freq,DFreq,Amps,YY):
fs = 1000*50*4 # Hz, sampling rate
h.cvode_active(0) # turn off variable time step
h.steps_per_ms = fs*1e-3 # steps per ms for NEURON simulation
h.dt = 1/(fs*1e-3) # ms, sample spacing
h.celsius = h.celsius # temperature at which to run simulation
h.v_init = -80. # mV, initial voltage to begin simulations
ZZ=0
MyTraj = np.array([[-50e-3,YY,ZZ], [+51e-3,YY,ZZ]])
MyPath = os.getcwd() + '/'
CELL_DIR = MyPath+'cells/'
CELL_FILE_NAME = 'NewMRGaxon_for_Python.hoc'
MyD = 8.7
MyCell = MRG(axon_trajectory=MyTraj, fiberD=MyD,
CELL_FILE_NAME=CELL_FILE_NAME, CELL_DIR=CELL_DIR)
I = 1. # current source amplitude in A
# find xyz coordinates of center of each axon compartment
nrn_xyz = list()
for sec in MyCell.get_secs():
nrn_xyz.append(MyCell.center_3Dcoords(sec))
nrn_xyz = np.array(nrn_xyz)*1e-6 # m, convert from um to m
sigmaXX=0.6
sigmaYY=0.083
sigmaZZ=0.083
VS1=Voltages(nrn_xyz[:,0],nrn_xyz[:,1],nrn_xyz[:,2],-5e-3,+5e-3,+0e-3,+I,sigmaXX,sigmaYY,sigmaZZ,'V')['V']
VS2=Voltages(nrn_xyz[:,0],nrn_xyz[:,1],nrn_xyz[:,2],+5e-3,+5e-3,+0e-3,-I,sigmaXX,sigmaYY,sigmaZZ,'V')['V']
VS3=Voltages(nrn_xyz[:,0],nrn_xyz[:,1],nrn_xyz[:,2],-5e-3,-5e-3,+0e-3,+I,sigmaXX,sigmaYY,sigmaZZ,'V')['V']
VS4=Voltages(nrn_xyz[:,0],nrn_xyz[:,1],nrn_xyz[:,2],+5e-3,-5e-3,+0e-3,-I,sigmaXX,sigmaYY,sigmaZZ,'V')['V']
V1 = VS1+VS2 # voltage of first pair
V2 = VS3+VS4 # voltage of second pair
recordingDict = dict()
location = 0.5
variable = {'v'}
iSec=0
# MyPoints = list()
for Sec in MyCell.get_secs():
Sec.rx1_xtra=V1[iSec]
Sec.rx2_xtra=V2[iSec]
iSec=iSec+1
if ("node" in Sec.name()):
for vars in variable:
rec_dict(recordingDict, Sec, location, vars)
MyCell.record(recordingDict)
h.load_file("stdrun.hoc")
h.init()
h.finitialize(h.v_init)
h.fcurrent()
h.tstop = Duration
Times=np.arange(0,h.tstop+h.dt,h.dt)
Sim1=Amps*np.sin(2*np.pi*Freq/1000.0*Times)
Sim2=Amps*np.sin(2*np.pi*(Freq+DFreq)/1000.0*Times)
t = h.Vector(Times)
Stim1=h.Vector(Sim1*1e-3)
Stim2=h.Vector(Sim2*1e-3)
Stim1.play(h._ref_is1_xtra, t, 0)
Stim2.play(h._ref_is2_xtra, t, 0)
h.run()
return MyCell
def run(tstop): h.cvode_active(1) h.cvode.atol(1e-7) h.tstop = tstop h.run() h.cvode_active(0)
Implementation by: Salvador Dura-Bernal, Cliff Kerr, Bill Lytton ([email protected]; [email protected]; [email protected]) """ # adapted from /u/billl/nrniv/sync/izh.hoc import os, sys, collections import numpy as np from neuron import h, gui h.load_file('stdrun.hoc') import izhi2007Figs as iz07fig import izhi2007Wrapper as izh07 import __main__ py = __main__ h.tstop=500 h.cvode_active(0) h.dt=0.1 izh, cell07 = None, None # must be declared here since needs to be imported elsewhere type2003 = collections.OrderedDict([ # a b c d vviv tstop ('regular spiking (RS)' , (0.02 , 0.2 , -65.0 , 8.0 , -63.0 , 150.0)) , ('intrinsically bursting (IB)' , (0.02 , 0.2 , -55.0 , 4.0 , -70.0 , 150.0)) , ('chattering (CH)' , (0.02 , 0.2 , -50.0 , 2.0 , -70.0 , 150.0)) , ('fast spiking (FS)' , (0.1 , 0.2 , -65.0 , 2.0 , -70.0 , 150.0)) , ('thalamo-cortical (TC)' , (0.02 , 0.25, -65.0 , 0.05 , -63.0 , 150.0)) , ('thalamo-cortical burst (TC)' , (0.02 , 0.25, -65.0 , 0.05 , -87.0 , 150.0)) , ('resonator (RZ)' , (0.1 , 0.26 , -65.0 , 2.0 , -70.0 , 100.0)) , ('low-threshold spiking (LTS)' , (0.02 , 0.25 , -65.0 , 2.0 , -63.0 , 250.0))])
def set_sim_params(self, params):
h.tstop = params["tstop"]
h.dt = params["dt"]
h.cvode.atol(params["atol"])
h.celsius = params["celsius"]
h.cvode_active(int(params["cvode"]))
def run_current_steps_protocol(cell, amplitudes, ttran, tstep, dt=0.025, temperature=37., use_cvode=False):
h.load_file('stdrun.hoc')
print(timestamp() + '>> Inserting the stimulus...')
stim = h.IClamp(cell.soma[0](0.5))
stim.dur = tstep
stim.delay = ttran
print(timestamp() + '>> Setting up the recorders...')
rec = {}
for lbl in 't','vsoma','spikes':
rec[lbl] = h.Vector()
rec['t'].record(h._ref_t)
rec['vsoma'].record(cell.soma[0](0.5)._ref_v)
apc = h.APCount(cell.soma[0](0.5))
apc.record(rec['spikes'])
h.celsius = temperature
h.dt = dt
if use_cvode:
h.tstop = stim.delay + stim.dur + 500
h.cvode_active(1)
h.cvode.atol(1e-6)
h.cvode.rtol(1e-6)
h.cvode.maxstep(dt)
else:
h.tstop = stim.delay - 20
print(timestamp() + '>> Evolving the model until %g ms...' % h.tstop)
sys.stdout.flush()
h.run()
print(timestamp() + '>> Saving the state...')
ss = h.SaveState()
ss.save()
t = []
V = []
spike_times = []
for i,amp in enumerate(amplitudes):
sys.stdout.write('\r' + timestamp() + '>> Trial [%02d/%02d] ' % (i+1,len(amplitudes)))
sys.stdout.flush()
if not use_cvode:
ss.restore()
stim.amp = amp
apc.n = 0
rec['t'].resize(0)
rec['vsoma'].resize(0)
rec['spikes'].resize(0)
if use_cvode:
h.t = 0
h.run()
else:
h.continuerun(stim.delay + stim.dur + 500.)
t.append(np.array(rec['t']))
V.append(np.array(rec['vsoma']))
spike_times.append(np.array(rec['spikes']))
sys.stdout.write('\n')
if use_cvode:
return np.array(t), np.array(V), np.array(spike_times)
else:
return np.array(V), np.array(spike_times)
def pyloop(ratio1=1.1, ratio2=2.2, loc1=128, loc2=128, simiter=0, subth=False):
print '\n%s starting run' % (gethostname())
iotim = 0
tic = time()
if subth:
h5fn = 'paper-subth-%dum_%dum_run%d.h5' % (l1, l2, simiter)
h.tstop = 200
h.maxsyn2 = 20
else:
h5fn = 'paper-supth-%dum_%dum_run%d.h5' % (l1, l2, simiter)
h.tstop = 500
savd = './data/'
filters = pyt.Filters(complevel=9,
complib='zlib',
shuffle=True,
fletcher32=True)
h5f = pyt.openFile(os.path.join(savd, h5fn), 'w', filters=filters)
maxsyn1 = int(h.maxsyn1) + 1
maxsyn2 = int(h.maxsyn2) + 1
h.tsamp = h.tstop / h.dt + 1
tsamp = int(h.tsamp)
shape = (maxsyn1, maxsyn2, tsamp)
cshape = (1, 1, shape[-1])
r = h.Random(simiter + h.luckyoffset)
r.negexp(h.meanisi)
# stimulated branch
sec = h.a10_11
syns = [[], []]
for ind, loc in enumerate([loc1, loc2]):
if subth:
for delay, ratio in zip([2, 22], [ratio1, ratio2]):
syn = h.synsuper(.5, r, sec=sec)
syns[ind].append(syn)
syndict = dict(sloc=loc, ratio=ratio, e1del=delay, e1flag=1)
for name, value in syndict.iteritems():
setattr(syn.syn, name, value)
else:
syn = h.synsuper(.5, r, sec=sec)
syns[ind].append(syn)
syndict = dict(sloc=loc, ratio=ratio1, e1flag=0)
for name, value in syndict.iteritems():
setattr(syn.syn, name, value)
# initialize nseg with two 'active' branches
theseclist = [h.a10_11, h.a1_111]
sl2 = h.SectionList()
for sec in theseclist:
sl2.append(sec=sec)
poppedsecs = sl2.unique()
h.refreshnseg(h.makeactivelist(sl2))
print 'nseg: %d' % (h.nsegcnt())
h.cvode.cache_efficient(1)
h.cvode_active(0)
# somatic voltage recordings, initialize after nseg is set
v = h.Vector(tsamp)
v.record(h.soma(.5)._ref_v)
trash = v.label(h.soma.name())
# voltage recording dictionary
vd = {'s': v}
# dendritic voltage recording
h.distance(0, h.soma_con_pt, sec=h.soma)
for n, sec in enumerate(theseclist):
d = h.distance(0, sec=sec)
# location 1 synapses
locx1 = (loc1 - d) / sec.L
v = h.Vector(tsamp)
v.record(sec(locx1)._ref_v)
trash = v.label(sec.name())
vd.update({'dp' + str(n): v})
# location 2 synapses
locx2 = (loc2 - d) / sec.L
v = h.Vector(tsamp)
v.record(sec(locx2)._ref_v)
trash = v.label(sec.name())
vd.update({'dd' + str(n): v})
if subth:
h.poisson = 0
else:
h.poisson = 1
# 'background' guassian current injection
npy.random.seed(1)
ic = h.IClamp(0.5, sec=h.soma)
ic.dur = h.tstop
icmean = .75
icstd = 1
icvals = icmean + icstd * npy.random.randn(h.tstop / h.dt + 1)
icrand = h.Vector(tsamp)
for i in xrange(tsamp):
icrand.x[i] = icvals[i]
icrand.play(ic._ref_amp, h.dt)
h5d = {}
for key in vd.keys():
h5d.update({
key:
h5f.createCArray(h5f.root,
key,
shape=shape,
atom=pyt.Float64Atom(),
title=vd[key].label(),
chunkshape=cshape)
})
synpairs = [(i, j) for i in xrange(maxsyn1) for j in xrange(maxsyn2)]
if not subth:
# subsample stimulus configuration space to speed up simulations
oddpairs = [(i, j) for i in npy.mgrid[1:maxsyn1:2]
for j in npy.mgrid[1:maxsyn2:2]]
synpairs = list(set(synpairs) - set(oddpairs))
# subset of input configurations needed for figure 7 traces
realrunsdist = [3, 6, 9]
realrunsprox = [17, 21, 25]
iisi = simiter * (maxsyn1 + maxsyn2) # inter-iteration seed interval
for runcnt, (nsyn1, nsyn2) in enumerate(synpairs):
# configure s(t)imulation
if not subth:
if ((nsyn1 in realrunsprox and nsyn2 == 0)
or (nsyn2 in realrunsdist and nsyn1 == 0)):
h.fakerun = 0
else:
h.fakerun = 1
for ssyn in syns[0]:
ssyn.syn.nsyn = nsyn1
seed1 = float(iisi + nsyn1 + h.luckyoffset)
r1 = h.Random(seed1)
r1.negexp(h.meanisi)
ssyn.setrand(r1)
for ssyn in syns[1]:
ssyn.syn.nsyn = nsyn2
seed2 = float(iisi + maxsyn1 + nsyn2 + h.luckyoffset)
r2 = h.Random(seed2)
r2.negexp(h.meanisi)
ssyn.setrand(r2)
# run simulation
h.run()
# get results
iotic = time()
if not h.fakerun:
for key in h5d.keys():
h5d[key][nsyn1, nsyn2] = npy.array(vd[key]).reshape(cshape)
iotim += time() - iotic
iotic = time()
h5f.close()
iotim += time() - iotic
print '%s running %d runs took %d seconds, of which %d seconds was I/O' % (
gethostname(), runcnt + 1, time() - tic, iotim)
axon.nseg = nseg
axon.Ra = Ra
axon.insert('hh')
asyns = [h.AlphaSynapse(axon(0)), h.AlphaSynapse(axon(0))]
for ix,t in enumerate(t_on):
asyns[ix].onset = t
asyns[ix].gmax = g_max
h.tstop = sim_length
t_vec = h.Vector() # Time stamp vector
t_vec.record(h._ref_t)
axon_segments = list(axon)
v_vec = []
for axon_segment in axon_segments:
v_vec.append(h.Vector())
v_vec[-1].record(axon_segment._ref_v)
h.cvode_active(1)
h.cvode.atol(1e-5)
h.run()
results = np.zeros((len(t_vec), len(axon_segments) + 1))
results[:, 0] = t_vec.as_numpy()
for ix, v in enumerate(v_vec):
results[:, ix + 1] = v_vec[ix]
np.savetxt(save_dir+"/modelResult/" + model_name + ".csv", results, delimiter=",")
def run(self):
self.gui()
self.g.label(0.1, 0.9, self.mechname, 2)
self.g.label("secondorder=%g" % h.secondorder)
self.g.label("dt=%g" % h.dt)
vvec = h.Vector()
tvec = h.Vector()
if use_voltage_clamp:
vvec.label("ic")
vvec.record(self.syn, self.vc._ref_i, 0.1)
else:
vvec.label("v")
#vvec.record(self.syn, self.soma(.5)._ref_v, 0.1)
vvec.record(self.syn, self.syn._ref_A_AMPA, 0.1)
tvec.record(self.syn, h._ref_t, 0.1)
h.cvode.atol(1e-10)
# cvode is standard but use first result to change weight so that
# there is a substantial synaptic change
dtsav = h.dt
try:
h.cvode_active(1)
h.run()
except:
h.cvode_active(0)
h.dt /= 1000
print("cvode failed so try fixed step dt=%g as standard" % h.dt)
h.run()
sz = 10 * h.tstop
scale = (vvec.max(10, sz) - vvec.min(10, sz)) / 10.
print("scale=%g" % scale)
if abs(scale) > 1e-6:
self.nc.weight[0] /= scale
elif math.isnan(scale) or abs(scale) > 1e6:
print("numerical problem")
else:
print("No response")
h.run()
h.dt = dtsav
vvec.resize(sz)
tvec.resize(sz)
vvec.line(self.g, tvec, 1, 3)
vstd = vvec.c()
tstd = tvec.c()
vmag = vvec.c().sub(vvec.x[10]).abs().sum()
result = []
h.cvode_active(0)
for adjust in [0, 1]:
h.nrn_netrec_state_adjust = adjust
color = 3 if adjust is 1 else 2
for dt in [h.dt / 8, h.dt / 4, h.dt / 2, h.dt]:
h.dt = dt
h.run()
vvec.resize(sz)
tvec.resize(sz)
vvec.line(self.g, tvec, color, 1)
vdif = vvec.c().sub(vstd).abs().sum()
tdif = tvec.c().sub(tstd).abs().sum()
result.append((self.mechname, h.secondorder, adjust, dt, vdif,
tdif, vmag))
print("%s %g %g %g %g %g" % result[-1][:-1])
h.nrn_netrec_state_adjust = 0
self.g.exec_menu("View = plot")
return result
data1 = numpy.loadtxt(os.path.join(Exp_Dir, files_list1[0]))
data2 = numpy.loadtxt(os.path.join(Exp_Dir, files_list2[0]))
data3 = numpy.loadtxt(os.path.join(Exp_Dir, files_list3[0]))
data0 = data0[1:80000, :]
data1 = data1[1:80000, :]
data2 = data2[1:80000, :]
data3 = data3[1:80000, :]
### Parameters ###
Nsyns = numpy.array([0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60])
Nsyns_PYR = 3
freqs = numpy.array([1, 5, 8, 10, 20])
### Setup holding currents ###
h.cvode_active(1) # i.e. to make it run faster for this portion
h.cvode.atol(1e-05)
h.tstop = 5000
spikerate_prev = 0
if find7HzHolding:
for stepcurr in range(40, 101, 1):
h.ic_hold.amp = stepcurr * 0.001 # holding current
h.f(0, 0, 0)
apctimes = numpy.array(h.apctimes)
apctimes = apctimes[apctimes > 1000] # Only spike times > 1s
spikerate = len(apctimes) / 4
print('Spike Rate = ' + str(spikerate) + ' Hz at ' +
str(stepcurr * 0.001) + ' nA')
if (spikerate > 7) & (spikerate < 7.6):
print('Holding Current = ' + str(stepcurr * 0.001) + ' nA')
def iclamp_adaptive(cell,
sec,
i_inj,
v_init,
tstop,
dt,
celsius=35,
pos_i=0.5,
pos_v=0.5,
atol=1e-2,
continuous=True,
discontinuities=None,
interpolate=True):
"""
Runs a NEURON simulation of the cell for the given parameters using adaptive integration.
:param sec: List with 1st entry the name of the section and 2nd entry the index (or None in case of soma)
:type sec: list[str, int]
:param i_inj: Amplitude of the injected current for all times t.
:type i_inj: array_like
:param v_init: Initial membrane potential of the cell.
:type v_init: float
:param tstop: Duration of a whole run.
:type tstop: float
:param dt: Time step.
:type dt: float
:param celsius: Temperature during the simulation (affects ion channel kinetics).
:type celsius: float
:param pos_i: Position of the IClamp on the Section (number between 0 and 1).
:type pos_i: float
:param pos_v: Position of the recording electrode on the Section (number between 0 and 1).
:type pos_v: float
:param atol: Absolute tolerance of the integration.
:type atol: float
:param: continuous: If true, linear interpolation is used to define the values between time points of i_inj.
:type: continuous: bool
:param: discontinuities: Indices where jumps in i_inj occur. This will insert a new point before each discontinuity
in t with the time at the discontinuity and in i_inj with the value from i_inj before the discontinuity.
:type discontinuities: array[int]
:param interpolate: If true, the recorded values for v and t will be linearly interpolated to match dt.
:type interpolate: bool
:return: Membrane potential of the cell and time recorded at each time step.
:rtype: tuple of three ndarrays
"""
# turn on adaptive integration and set tolerance (only works when stdrun.hoc already loaded)
h.cvode_active(1)
h.cvode.atol(1e-8)
section = cell.substitute_section(sec[0], sec[1])
# time
t = np.arange(0, tstop + dt, dt)
# adapt i_vec to cope with discontinuities
if discontinuities is not None:
discontinuities = np.sort(discontinuities)
discontinuities = discontinuities + np.arange(
len(discontinuities
)) # index shifts by the amount of already inserted values
for discontinuity in discontinuities:
i_inj = np.insert(i_inj, discontinuity, i_inj[discontinuity - 1])
t = np.insert(t, discontinuity, t[discontinuity])
# insert an IClamp with the current trace from the experiment
stim, i_vec, t_vec = section.play_current(i_inj,
t,
pos_i,
continuous=continuous)
# record the membrane potential
v_rec = section.record('v', pos_v)
t_rec = h.Vector()
t_rec.record(h._ref_t)
i_rec = h.Vector()
i_rec.record(stim._ref_amp)
# run simulation
h.celsius = celsius
h.v_init = v_init
h.tstop = tstop
h.steps_per_ms = 1 / dt # change steps_per_ms before dt, otherwise dt not changed properly
h.dt = dt
h.run()
v_rec = np.array(v_rec)
t_rec = np.array(t_rec)
#i_rec = np.array(i_rec)
if interpolate:
#i_rec = np.interp(t, t_rec, i_rec)
v_rec = np.interp(t, t_rec, v_rec)
t_rec = np.interp(t, t_rec, t_rec)
t_rec, unique_indices = np.unique(
t_rec,
return_index=True) # remove double values (from discontinuities)
v_rec = v_rec[unique_indices] # second value is the right one
#i_rec = i_rec[unique_indices] # second value is the right one
return v_rec, t_rec # , i_rec
