def simulate(self, current_base, current_rebound):
sim = self.env.Simulation(**self.sim_kwargs)
cell = self.cell_functor(sim=sim)
soma_loc = cell.get_location('soma')
cc1 = sim.create_currentclamp(name="cclamp", amp=current_base, dur="100:ms", delay="50:ms", cell_location=soma_loc)
cc2 = sim.create_currentclamp(name="cclamp2", amp=-1*current_rebound, dur="5:ms", delay="80:ms", cell_location=soma_loc)
cc3 = sim.create_currentclamp(name="cclamp3", amp=-1*current_rebound, dur="5:ms", delay="120:ms", cell_location=soma_loc)
sim.record(cc1, name="Current1", what=CurrentClamp.Recordables.Current, description="CurrentClampCurrent")
sim.record(cc2, name="Current2", what=CurrentClamp.Recordables.Current, description="CurrentClampCurrent")
sim.record(cc3, name="Current3", what=CurrentClamp.Recordables.Current, description="CurrentClampCurrent")
sim.record(cell, name="SomaVoltage", cell_location=soma_loc, what=Cell.Recordables.MembraneVoltage, description="Response to iInj1=%s iInj2=%s"%(current_base, current_rebound))
res = sim.run()
#SimulationSummariser(res, "/home/michael/Desktop/ForRoman.pdf")
i = res.get_trace('Current1').convert_to_fixed(qty("0.5:ms")) + res.get_trace('Current2').convert_to_fixed(qty("0.5:ms")) + res.get_trace('Current3').convert_to_fixed(qty("0.5:ms"))
i = TraceConverter.rebase_to_fixed_dt(res.get_trace('Current1'
), dt=qty('0.5:ms')) \
+ TraceConverter.rebase_to_fixed_dt(res.get_trace('Current2'
), dt=qty('0.5:ms')) \
+ TraceConverter.rebase_to_fixed_dt(res.get_trace('Current3'
), dt=qty('0.5:ms'))
i.tags = [StandardTags.Current]
return (res.get_trace('SomaVoltage'), i)
def make_cell(sim, cell_name, cell_chl_functor):
m1 = mf.MorphologyBuilder.get_single_section_soma(area=mf.qty("1:um2"))
cell = sim.create_cell(name=cell_name, morphology=m1)
for chl in cell_chl_functor():
cell.apply_channel( chl, parameter_multipliers={'gmax':random.uniform(0.9, 1.1)})
cell.set_passive( mf.PassiveProperty.SpecificCapacitance, mf.qty('4:pF/um2'))
return cell
def build_trigger( env, cell):
return env.SynapticTrigger(
mfc.SynapticTriggerByVoltageThreshold,
cell_location = cell.soma,
voltage_threshold = mf.qty("0:mV"),
delay = mf.qty("1:ms"),
#,
)
def load_ka_channel():
ka_param_names = """
ka_gmax ka_erev
ka_m_a1 ka_m_a2 ka_m_a3 ka_m_a4 ka_m_a5
ka_m_b1 ka_m_b2 ka_m_b3 ka_m_b4 ka_m_b5
ka_h_a1 ka_h_a2 ka_h_a3 ka_h_a4 ka_h_a5
ka_h_b1 ka_h_b2 ka_h_b3 ka_h_b4 ka_h_b5
"""
ka_param_units = """
nS/um2; mV;
ms-1; mV-1 ms-1; ;mV; mV;
ms-1; mV-1 ms-1; ;mV; mV;
ms-1; mV-1 ms-1; ;mV; mV;
ms-1; mV-1 ms-1; ;mV; mV;
"""
ka_param_str = """
30 -80
12.025 0 0.5 -10.01 -12.56
14.325 0 1 -8.01 9.69
0.0001 0 1 15.88 26.0
10.000 0 500 -22.09 -10.21
"""
nrn_params = dict([(p, mf.qty("%s:%s"%(v, u.strip()))) for (p, u, v) in zip(ka_param_names.split(), ka_param_units.split(';'), ka_param_str.split())])
nrn_params_ka = extract_params(nrn_params, prefix='ka_')
print nrn_params_ka
eqnsetka = mf.neurounits.NeuroUnitParser.Parse9MLFile(ka_eqnset_txt.replace("sautois", "saut2")).get_component()
kaChls = mfc.Neuron_NeuroUnitEqnsetMechanism(name="Chl5", eqnset=eqnsetka, default_parameters = nrn_params_ka)
return kaChls
def test_cell_current(cell_name, cell_chl_functor, current):
sim = mf.NEURONEnvironment().Simulation()
m1 = mf.MorphologyBuilder.get_single_section_soma(area=mf.qty("1:um2"))
cell = sim.create_cell(name=cell_name, morphology=m1)
cc = sim.create_currentclamp(name="CC1", delay=100*mf.ms, dur=400*mf.ms, amp=current * mf.pA, cell_location=cell.soma)
for chl in cell_chl_functor():
mf.cell.apply_channel( chl)
mf.cell.set_passive( mf.PassiveProperty.SpecificCapacitance, mf.qty('4:pF/um2'))
sim.record(cell, what=mf.Cell.Recordables.MembraneVoltage)
sim.record(cc, what=mf.CurrentClamp.Recordables.Current)
res =sim.run()
return res
def plot_iv_curve(self, ax=None):
# pylint: disable=E1103
title = '%s: IV Curve' % (self.cell_description or None)
if not ax:
f = QuantitiesFigure()
f.suptitle(title)
ax = f.add_subplot(1, 1, 1)
ax.set_xlabel('Injected Current')
ax.set_ylabel('SteadyStateVoltage')
V_in_mV = [self.get_iv_point_steaddy_state(c).rescale('mV').magnitude for c in self.currents]
v = np.array(V_in_mV) * units.mV
i = morphforge.units.factorise_units_from_list(self.currents)
low_v = V_in_mV < self.v_regressor_limit if self.v_regressor_limit else range( len(V_in_mV))
print 'i[low_v]', i[low_v]
print 'v[low_v]', v[low_v]
ax.plot(i[low_v], v[low_v], )
ax.plot(i[np.logical_not(low_v)], v[np.logical_not(low_v)], )
ax.plot(i[np.logical_not(low_v)], v[np.logical_not(low_v)], )
# Plot the regressor:
i_units = qty('1:pA').units
v_units = qty('1:mV').units
iv = np.vstack((i.rescale(i_units).magnitude,
v.rescale(v_units).magnitude)).T
if not len(iv[low_v, 0]):
return
import scipy.stats as stats
(a_s, b_s, r, tt, stderr) = stats.linregress(iv[low_v, 0], iv[low_v, 1])
input_resistance = (a_s * (v_units / i_units)).rescale('MOhm')
reversal_potential = b_s * v_units
self.input_resistance = input_resistance
self.reversal_potential = reversal_potential
ax.plot(i, i*input_resistance + reversal_potential,'o-', label = "Fit: [V(mV) = %2.3f * I(pA) + %2.3f]"%(a_s, b_s) + " \n[Input Resistance: %2.2fMOhm Reversal Potential: %2.2f mV"%(input_resistance, reversal_potential) )
ax.legend()
PM.save_figure(figname=title)
def get_cell(self, env, sim):
m1 = mf.MorphologyBuilder.get_single_section_soma(area=float(self.area) * mf.units.um2)
morph_functor = mf.MorphologyLibrary.get_morphology_functor(
modelsrc=Model.Hull12SWithAxon, celltype=CellType.dIN
)
m1 = morph_functor(axonDiam=0.4)
myCell = sim.create_cell(name="Cell1", morphology=m1)
myCell.set_passive(mf.PassiveProperty.SpecificCapacitance, mf.qty("%f:uF/cm2" % self.capacitance))
return myCell
def __init__(self, cell_functor, currents, cell_description=None, sim_functor=None, v_regressor_limit=None, sim_kwargs=None, plot_all=False):
self.cell_functor = cell_functor
self.v_regressor_limit = v_regressor_limit
self.fig=None
#Previously = qty("-30:mV")
self.sim_kwargs = sim_kwargs or {}
self.tCurrentInjStart = qty('50:ms')
self.tCurrentInjStop = qty('200:ms')
self.tSteaddyStateStart = qty('100:ms')
self.tSteaddyStateStop = qty('151:ms')
self.traces = {}
self.currents = currents
self.cell_description = cell_description or 'Unknown Cell'
self.input_resistance = qty('-1:MOhm')
if plot_all:
self.plot_all()
def load_std_channels(param_str):
nrn_params = dict([(p, mf.qty("%s:%s"%(v, u.strip()))) for (p, u, v) in zip(param_names.split(), param_units.split(';'), param_str.split())])
nrn_params_na = extract_params(nrn_params, prefix='na_')
nrn_params_lk = extract_params(nrn_params, prefix='lk_')
nrn_params_ks = extract_params(nrn_params, prefix='ks_', replace_prefix='k_')
nrn_params_kf = extract_params(nrn_params, prefix='kf_', replace_prefix='k_')
nrn_params_ks = remap_keys(nrn_params_ks, {'k_gmax':'gmax', 'k_erev':'erev'})
nrn_params_kf = remap_keys(nrn_params_kf, {'k_gmax':'gmax', 'k_erev':'erev'})
eqnsetna = mf.neurounits.NeuroUnitParser.Parse9MLFile(na_eqnset_txt).get_component()
eqnsetlk = mf.neurounits.NeuroUnitParser.Parse9MLFile(lk_eqnset_txt).get_component()
eqnsetk = mf.neurounits.NeuroUnitParser.Parse9MLFile(k_eqnset_txt).get_component()
na_chl = mfc.Neuron_NeuroUnitEqnsetMechanism(name="Chl1", eqnset=eqnsetna, default_parameters = nrn_params_na)
lk_chl = mfc.Neuron_NeuroUnitEqnsetMechanism(name="Chl2", eqnset=eqnsetlk, default_parameters = nrn_params_lk)
ksChls = mfc.Neuron_NeuroUnitEqnsetMechanism(name="Chl3", eqnset=eqnsetk, default_parameters = nrn_params_ks)
kfChls = mfc.Neuron_NeuroUnitEqnsetMechanism(name="Chl4", eqnset=eqnsetk, default_parameters = nrn_params_kf)
chls = [na_chl, lk_chl, ksChls, kfChls]
return chls
def getInputStimulus(self, sim, cell):
somaLoc = cell.get_location("soma")
s1 = sim.create_currentclamp(
name="Stim1",
amp=mf.qty("%2.2f:pA" % self.amp1),
dur=mf.qty("%f:ms" % self.dur1),
delay=mf.qty("%f:ms" % self.delay1),
cell_location=somaLoc,
)
s2 = sim.create_currentclamp(
name="Stim2",
amp=mf.qty("%2.2f:pA" % self.amp2),
dur=mf.qty("%f:ms" % self.dur2),
delay=mf.qty("%f:ms" % self.delay2),
cell_location=somaLoc,
)
return None
import morphforge.stdimports as mf
from mhlibs.quantities_plot import QuantitiesFigure
import pylab
from morphforge.stdimports import qty, reduce_to_variable_dt_trace
t1 = mf.TraceGenerator.generate_flat(value='1:mV')
#t2 = mf.TraceGenerator.generate_flat(value='0.0015:V')
t2 = mf.TraceGenerator.generate_ramp(value_start='0.0015:V', value_stop='2.5:mV', npoints=20)
f = QuantitiesFigure()
ax = f.add_subplot(111)
ax.plotTrace(t1, marker='x')
ax.plotTrace(t2, marker='o')
ax.plotTrace(t1+t2, marker='<')
ax.plotTrace((t2+t2)*3.0+qty('0.03:V'), marker='<')
t1 = mf.fixed_to_variable_dt_trace(t1, '0.01:mV')
ax.plotTrace(t1, marker='>')
ax.legend()
pylab.show()
