def _create_NEURON(self, neuron_name):
# -----------------------------------------------------------------
# Set up NEURON section
# -----------------------------------------------------------------
self.nrn_cell_sec = neuron.h.Section()
try:
self.nrn_cell = eval(
'neuron.h.{}(0.5, sec=self.nrn_cell_sec)'.format(neuron_name))
except TypeError:
self.nrn_cell_sec.insert(neuron_name)
self.nrn_cell = getattr(self.nrn_cell_sec(0.5), neuron_name)
self.nrn_cell_sec.L = 10
self.nrn_cell_sec.diam = 10 / numpy.pi
self.nrn_cell_sec.cm = 1.0
for mech_name in self.extra_mechanisms:
self.nrn_cell_sec.insert(mech_name)
if self.extra_point_process is not None:
MechClass = getattr(neuron.h, self.extra_point_process)
self.extra_point_process = MechClass(self.nrn_cell_sec(0.5))
for prop in self.properties.properties:
name = prop.name
value = prop.value
try:
varname, scale = self.neuron_translations[name]
value = value * scale
except (ValueError, KeyError):
varname = self.neuron_translations.get(name, name)
if varname in self.specific_params:
specific_value = UnitHandlerNEURON.to_pq_quantity(
Quantity(value, prop.units)) / (100 * (pq.um ** 2))
value = UnitHandlerNEURON.scale_value(specific_value)
else:
value = UnitHandlerNEURON.scale_value(
Quantity(value, prop.units))
if varname is not None:
if '.' in varname:
mech_name, vname = varname.split('.')
try:
setattr(getattr(self.nrn_cell_sec(0.5), mech_name),
vname, value)
except AttributeError:
setattr(self.extra_point_process, vname, value)
elif varname == 'cm':
self.nrn_cell_sec.cm = value
else:
try:
setattr(self.nrn_cell, varname, value)
except AttributeError:
setattr(self.nrn_cell_sec, varname, value)
for name, value in self.initial_states.iteritems():
try:
varname, scale = self.neuron_translations[name]
value = value * scale
except (ValueError, KeyError):
varname = self.neuron_translations.get(name, name)
value = UnitHandlerNEURON.scale_value(
UnitHandlerNEURON.from_pq_quantity(value))
if varname is not None:
if '.' in varname:
try:
setattr(getattr(self.nrn_cell_sec(0.5), mech_name),
vname, value)
except AttributeError:
setattr(self.point_process, vname, value)
else:
try:
setattr(self.nrn_cell, varname, value)
except (AttributeError, LookupError):
setattr(self.nrn_cell_sec, varname, value)
# Specify current injection
if self.input_signal is not None:
_, signal = self.input_signal
self._nrn_iclamp = neuron.h.IClamp(0.5, sec=self.nrn_cell_sec)
self._nrn_iclamp.delay = 0.0
self._nrn_iclamp.dur = 1e12
self._nrn_iclamp.amp = 0.0
self._nrn_iclamp_amps = neuron.h.Vector(pq.Quantity(signal, 'nA'))
self._nrn_iclamp_times = neuron.h.Vector(pq.Quantity(signal.times,
'ms'))
self._nrn_iclamp_amps.play(self._nrn_iclamp._ref_amp,
self._nrn_iclamp_times)
if self.input_train is not None:
port_name, train, connection_properties = self.input_train
try:
_, scale = self.neuron_translations[port_name]
except KeyError:
scale = 1.0
# FIXME: Should scale units
weight = connection_properties[0].value * scale
self._vstim = neuron.h.VecStim()
self._vstim_times = neuron.h.Vector(pq.Quantity(train, 'ms'))
self._vstim.play(self._vstim_times)
target = (self.extra_point_process
if self.extra_point_process is not None
else self.nrn_cell)
self._vstim_con = neuron.h.NetCon(
self._vstim, target, sec=self.nrn_cell_sec)
self._vstim_con.weight[0] = weight
# Record Time from NEURON (neuron.h.._ref_t)
self._nrn_rec = self.NEURONRecorder(self.nrn_cell_sec, self.nrn_cell)
self._nrn_rec.record(self.neuron_state_variable)
def __init__(self, *properties, **kwprops):
"""
`propertes/kwprops` -- Can accept a single parameter, which is a
dictionary of parameters or kwarg parameters,
or a list of nineml.Property objects
"""
self._flag_created(False)
# Construct all the NEURON structures
self._sec = h.Section() # @UndefinedVariable
# Insert dynamics mechanism (the built component class)
HocClass = getattr(h, self.__class__.name)
self._hoc = HocClass(0.5, sec=self._sec)
# A recordable of 'spikes' is needed for PyNN compatibility
self.recordable = {'spikes': None}
# Add a recordable entry for each event send ports
# TODO: These ports aren't able to be recorded from at present because
# different event ports are not distinguishable in Neuron (well
# not easily anyway). Users should use 'spikes' instead for now
for port in chain(self.component_class.event_send_ports):
self.recordable[port.name] = None
for port in chain(self.component_class.analog_send_ports,
self.component_class.state_variables):
if port.name != self.component_class.annotations.get(
PYPE9_NS, BUILD_TRANS, MEMBRANE_VOLTAGE, default=None):
self.recordable[port.name] = getattr(
self._hoc, '_ref_' + port.name)
# Get the membrane capacitance property if not an artificial cell
if self.build_component_class.annotations[
PYPE9_NS][BUILD_TRANS][MECH_TYPE] == ARTIFICIAL_CELL_MECH:
self.cm_prop_name = None
else:
# In order to scale the distributed current to the same units as
# point process current, i.e. mA/cm^2 -> nA the surface area needs
# to be 100um. mA/cm^2 = -3-(-2^2) = 10^1, 100um^2 = 2 + -6^2 =
# 10^(-10), nA = 10^(-9). 1 - 10 = - 9. (see PyNN Izhikevich neuron
# implementation)
self._sec.L = 10.0
self._sec.diam = 10.0 / pi
self.cm_prop_name = self.build_component_class.annotations[
PYPE9_NS][BUILD_TRANS][MEMBRANE_CAPACITANCE]
cm_prop = None
try:
try:
cm_prop = properties[0][self.cm_prop_name]
except IndexError:
cm_prop = kwprops[self.cm_prop_name] * un.nF
except KeyError:
if self.build_properties is not None:
cm_prop = self.build_properties.property(self.cm_prop_name)
if cm_prop is not None:
cm = pq.Quantity(UnitHandler.to_pq_quantity(cm_prop), 'nF')
else:
cm = 1.0 * pq.nF
# Set capacitance in mechanism
setattr(self._hoc, self.cm_prop_name, float(cm))
# Set capacitance in hoc
specific_cm = pq.Quantity(cm / self.surface_area, 'uF/cm^2')
self._sec.cm = float(specific_cm)
self.recordable[
self.component_class.annotations[PYPE9_NS][BUILD_TRANS][
MEMBRANE_VOLTAGE]] = self.source_section(0.5)._ref_v
# Set up members required for PyNN
self.spike_times = h.Vector(0)
self.traces = {}
self.gsyn_trace = {}
self.recording_time = 0
self.rec = h.NetCon(self.source, None, sec=self._sec)
self._inputs = {}
self._input_auxs = []
# self.initial_v = self.V_INIT_DEFAULT
# Get a mapping of receptor names to NMODL indices for PyNN projection
# connection
assert (set(self.build_component_class.event_receive_port_names) ==
set(self.component_class.event_receive_port_names))
# Need to use the build_component_class to get the same index as was
# used to construct the indices
# FIXME: These indices will need to be saved somewhere in the
# annotations of the build class so they can be reloaded
if self.build_component_class.num_event_receive_ports:
ports_n_indices = [
(self.build_component_class.index_of(p), p.name)
for p in self.build_component_class.event_receive_ports]
# Get event receive ports sorted by the indices
sorted_ports = zip(
*sorted(ports_n_indices, key=operator.itemgetter(0)))[1]
else:
sorted_ports = []
self.type = collections.namedtuple('Type', 'receptor_types')(
sorted_ports)
# Call base init (needs to be after 9ML init)
super(Cell, self).__init__(*properties, **kwprops)
self._flag_created(True)