def __init__(self,
morphology=None,
model=None,
threshold=None,
reset=NoReset(),
refractory=0 * ms,
level=0,
clock=None,
unit_checking=True,
compile=False,
freeze=False,
implicit=True,
Cm=0.9 * uF / cm**2,
Ri=150 * ohm * cm,
bc_type=2,
diffeq_nonzero=True):
clock = guess_clock(clock)
N = len(morphology) # number of compartments
if isinstance(model, str):
model = Equations(model, level=level + 1)
model += Equations('''
v:volt # membrane potential
''')
# Process model equations (Im) to extract total conductance and the remaining current
if use_sympy:
try:
membrane_eq = model._string['Im'] # the membrane equation
except:
raise TypeError, "The transmembrane current Im must be defined"
# Check conditional linearity
ids = get_identifiers(membrane_eq)
_namespace = dict.fromkeys(
ids, 1.
) # there is a possibility of problems here (division by zero)
_namespace['v'] = AffineFunction()
eval(membrane_eq, model._namespace['v'], _namespace)
try:
eval(membrane_eq, model._namespace['v'], _namespace)
except: # not linear
raise TypeError, "The membrane current must be linear with respect to v"
# Extracts the total conductance from Im, and the remaining current
z = symbolic_eval(membrane_eq)
symbol_v = sympy.Symbol('v')
b = z.subs(symbol_v, 0)
a = -sympy.simplify(z.subs(symbol_v, 1) - b)
gtot_str = "_gtot=" + str(a) + ": siemens/cm**2"
I0_str = "_I0=" + str(b) + ": amp/cm**2"
model += Equations(
gtot_str + "\n" + I0_str, level=level +
1) # better: explicit insertion with namespace of v
else:
raise TypeError, "The Sympy package must be installed for SpatialNeuron"
# Equations for morphology (isn't it a duplicate??)
eqs_morphology = Equations("""
diameter : um
length : um
x : um
y : um
z : um
area : um**2
""")
full_model = model + eqs_morphology
NeuronGroup.__init__(self,
N,
model=full_model,
threshold=threshold,
reset=reset,
refractory=refractory,
level=level + 1,
clock=clock,
unit_checking=unit_checking,
implicit=implicit)
self.model_with_diffeq_nonzero = diffeq_nonzero
self._state_updater = SpatialStateUpdater(self, clock)
self.Cm = ones(len(self)) * Cm
self.Ri = Ri
self.bc_type = bc_type #default boundary condition on leaves
self.bc = ones(len(self)) # boundary conditions on branch points
self.changed = True
# Insert morphology
self.morphology = morphology
self.morphology.compress(diameter=self.diameter,
length=self.length,
x=self.x,
y=self.y,
z=self.z,
area=self.area)
def __init__(self,
morphology=None,
model=None,
threshold=None,
reset=NoReset(),
refractory=0 * ms,
level=0,
clock=None,
unit_checking=True,
compile=False,
freeze=False,
Cm=0.9 * uF / cm**2,
Ri=150 * ohm * cm):
clock = guess_clock(clock)
N = len(morphology) # number of compartments
if isinstance(model, str):
model = Equations(model, level=level + 1)
model += Equations('''
v:volt # membrane potential
''')
# Process model equations (Im) to extract total conductance and the remaining current
if use_sympy:
try:
membrane_eq = model._string['Im'] # the membrane equation
except:
raise TypeError, "The transmembrane current Im must be defined"
# Check conditional linearity
ids = get_identifiers(membrane_eq)
_namespace = dict.fromkeys(
ids, 1.
) # there is a possibility of problems here (division by zero)
_namespace['v'] = AffineFunction()
eval(membrane_eq, model._namespace['v'], _namespace)
try:
eval(membrane_eq, model._namespace['v'], _namespace)
except: # not linear
raise TypeError, "The membrane current must be linear with respect to v"
# Extracts the total conductance from Im, and the remaining current
z = symbolic_eval(membrane_eq)
symbol_v = sympy.Symbol('v')
b = z.subs(symbol_v, 0)
a = -sympy.simplify(z.subs(symbol_v, 1) - b)
gtot_str = "_gtot=" + str(a) + ": siemens/cm**2"
I0_str = "_I0=" + str(b) + ": amp/cm**2"
model += Equations(
gtot_str + "\n" + I0_str, level=level +
1) # better: explicit insertion with namespace of v
else:
raise TypeError, "The Sympy package must be installed for SpatialNeuron"
# Equations for morphology (isn't it a duplicate??)
eqs_morphology = Equations("""
diameter : um
length : um
x : um
y : um
z : um
area : um**2
""")
full_model = model + eqs_morphology
# Create the state updater
NeuronGroup.__init__(self,
N,
model=full_model,
threshold=threshold,
reset=reset,
refractory=refractory,
level=level + 1,
clock=clock,
unit_checking=unit_checking,
implicit=True)
#Group.__init__(self, full_model, N, unit_checking=unit_checking, level=level+1)
#self._eqs = model
#var_names = full_model._diffeq_names
self.Cm = Cm # could be a vector?
self.Ri = Ri
self._state_updater = SpatialStateUpdater(self, clock)
#S0 = {}
# Fill missing units
#for key, value in full_model._units.iteritems():
# if not key in S0:
# S0[key] = 0 * value
#self._S0 = [0] * len(var_names)
#for var, i in zip(var_names, count()):
# self._S0[i] = S0[var]
# Insert morphology
self.morphology = morphology
self.morphology.compress(diameter=self.diameter,
length=self.length,
x=self.x,
y=self.y,
z=self.z,
area=self.area)