def __init__(self, mechanism_name, **parameters):
self.source = getattr(h, mechanism_name)()
for name, value in parameters.items():
setattr(self.source, name, value)
dummy = nrn.Section()
# needed for PyNN
self.source_section = dummy # todo: only need a single dummy for entire network, not one per cell
self.parameter_names = ('tau', 'refrac')
self.traces = {}
self.spike_times = h.Vector(0)
self.rec = h.NetCon(self.source, None)
self.recording_time = False
self.default = self.source
def __init__(self, **parameters):
print("Created instance of RS with params: %s" % parameters)
self.soma = nrn.Section('sec_RS')
#h(" forall psection() ")
self.soma.L = 10.0
self.soma(0.5).diam = 10.0
# Todo: work this out here from area etc.
# See https://github.com/NeuroML/org.neuroml.export/issues/60
self.soma(0.5).cm = (318310 *
parameters['C'] if 'C' in parameters else 318.31)
self.soma.push()
self.mechanism = h.RS(0.5, sec=self.soma)
self.mechanism.v0 = parameters['v0']
self.mechanism.k = parameters['k']
self.mechanism.vr = parameters['vr']
self.mechanism.vt = parameters['vt']
self.mechanism.vpeak = parameters['vpeak']
self.mechanism.a = parameters['a']
self.mechanism.b = parameters['b']
self.mechanism.c = parameters['c']
self.mechanism.d = parameters['d']
self.mechanism.C = parameters['C']
self.source = self.soma(0.5)._ref_v
# needed for PyNN
self.source_section = self.soma
self.parameter_names = (
'v0',
'k',
'vr',
'vt',
'vpeak',
'a',
'b',
'c',
'd',
'C',
)
self.traces = {}
self.recording_time = False
def __init__(self, **parameters):
print("Created instance of RS with params: %s" % parameters)
self.soma = nrn.Section('sec_RS')
#h(" forall psection() ")
self.soma.L = 10.0
self.soma(0.5).diam = 10.0
# Todo: work this out here from area etc.
self.soma(0.5).cm = (318309 * parameters['C']
if parameters.has_key('C') else 318.31927)
self.soma.push()
self.mechanism = h.RS(0.5, sec=self.soma)
self.mechanism.v0 = parameters['v0']
self.mechanism.k = parameters['k']
self.mechanism.vr = parameters['vr']
self.mechanism.vt = parameters['vt']
self.mechanism.vpeak = parameters['vpeak']
self.mechanism.a = parameters['a']
self.mechanism.b = parameters['b']
self.mechanism.c = parameters['c']
self.mechanism.d = parameters['d']
self.mechanism.C = parameters['C']
self.source = self.soma(0.5)._ref_v
# needed for PyNN
self.source_section = self.soma
self.parameter_names = (
'v0',
'k',
'vr',
'vt',
'vpeak',
'a',
'b',
'c',
'd',
'C',
)
self.traces = {}
self.recording_time = False
AREA = 1e-8 * pi * LENGTH * DIAMETER # membrane area in cm2
TAU = 20 # time constant in ms
CAPACITANCE = 1 # capacitance in muF/cm2
G_L = 1e-3 * CAPACITANCE / TAU # leak conductance in S/cm2
V_REST = -60 # resting potential
#V_REST = -65 # figure1A
TAU_W = 600
DELTA = 2.5
# Spike parameters
VTR = -50 # threshold in mV
VTOP = 40 # top voltage during spike in mV
VBOT = -60 # reset voltage in mV
REFRACTORY = 5.0 / 2 # refractory period in ms (correction for a bug in IF_CG4)
# Destexhe2009 figure 1 cells
soma = nrn.Section()
soma.insert('pas')
soma.insert('IF_BG4')
#nclist = []
soma.L = LENGTH # 20000 um^2
soma.diam = DIAMETER
soma.e_pas = V_REST
soma.g_pas = G_L
soma.Vtr_IF_BG4 = VTR
soma.Ref_IF_BG4 = REFRACTORY
soma.Vtop_IF_BG4 = VTOP
soma.Vbot_IF_BG4 = VBOT
soma.tau_w_IF_BG4 = 600
soma.EL_IF_BG4 = soma.e_pas