def _event_builder(self, events):
"""
From events yield lems.OnCondition objects
"""
# loop ober the events of the group
for event in events:
event_out = lems.EventOut(event) # output (e.g. spike)
# get threshold condition and add it to on_cond
on_cond_code = events[event]['threshold']['code']
on_cond = lems.OnCondition(renderer.render_expr(on_cond_code))
on_cond.add_action(event_out)
# if event is not in model ports add it
if event not in self._component_type.event_ports:
self._component_type.add(
lems.EventPort(name=event, direction='out'))
# check and add reset equations
if 'reset' in events[event]:
reset_code = events[event]['reset']['code']
# loop over multiple reset codes
for single_reset_code in re.split(';|\n', reset_code):
single_reset_eq = _equation_separator(single_reset_code)
on_cond.add_action(
lems.StateAssignment(single_reset_eq[0],
single_reset_eq[1]))
# check event is spike
spike_flag = False
if event == 'spike':
spike_flag = True
yield (spike_flag, on_cond)
def _event_builder(self, events, event_codes):
"""
From *events* and *event_codes* yields lems.OnCondition objects
"""
for ev in events:
event_out = lems.EventOut(ev) # output (e.g. spike)
oc = lems.OnCondition(renderer.render_expr(events[ev]))
oc.add_action(event_out)
# if event is not in model ports we should add it
if ev not in self._component_type.event_ports:
self._component_type.add(lems.EventPort(name=ev, direction='out'))
if ev in event_codes:
for ec in re.split(';|\n', event_codes[ev]):
event_eq = _equation_separator(ec)
oc.add_action(lems.StateAssignment(event_eq[0], event_eq[1]))
spike_flag = False
if ev == SPIKE:
spike_flag = True
yield (spike_flag, oc)
def build_lems_for_model(src):
model = lems.Model()
model.add(lems.Dimension('time', t=1))
# model.add(lems.Dimension('au'))
# primary element of the model is a mass model component
mass = lems.ComponentType(src.name, extends="baseCellMembPot")
model.add(mass)
######### Adding v is required to ease mapping to NEURON...
mass.dynamics.add(lems.StateVariable(name="v", dimension="voltage", exposure="v"))
mass.add(lems.Attachments(name="synapses",type_="basePointCurrentDL"))
for input in src.input:
mass.dynamics.add(lems.DerivedVariable(name=input,
dimension='none',
exposure=input,
select='synapses[*]/I',
reduce='add'))
mass.add(lems.Exposure(input, 'none'))
for key, val in src.const.items():
mass.add(lems.Parameter(key, 'none')) # TODO units
mass.add(lems.Constant(name="MSEC", dimension="time", value="1ms"))
mass.add(lems.Constant(name="PI", dimension="none", value="3.14159265359"))
states = []
der_vars = []
# for key in src.param:
# mass.add(lems.Parameter(key, 'au')) # TODO units
for key, val in src.auxex:
val = val.replace('**', '^')
mass.dynamics.add(lems.DerivedVariable(key, value=val))
for key in src.obsrv:
name_dv = key.replace('(','_').replace(')','').replace(' - ','_min_')
mass.dynamics.add(lems.DerivedVariable(name_dv, value=key, exposure=name_dv))
mass.add(lems.Exposure(name_dv, 'none'))
for src_svar in src.state_space:
name = src_svar.name
ddt = src_svar.drift.replace('**', '^')
mass.dynamics.add(lems.StateVariable(name, 'none', name))
mass.dynamics.add(lems.TimeDerivative(name, '(%s)/MSEC'%ddt))
mass.add(lems.Exposure(name, 'none'))
''' On condition is not need on the model but NeuroML requires its definition -->
<OnCondition test="r .lt. 0">
<EventOut port="spike"/>
</OnCondition>'''
oc = lems.OnCondition(test='v .gt. 0')
oc.actions.append(lems.EventOut(port='spike'))
mass.dynamics.add(oc)
return model
def add_neurongroup(self, neurongrp, index_neurongrp, initializers):
"""
Add NeuronGroup to self._model
If number of elements is 1 it adds component of that type, if
it's bigger, the network is created by calling:
`make_multiinstantiate`.
Parameters
----------
neurongrp : dict
Standard dictionary representation of NeuronGroup object
index_neurongrp : int
Index of neurongroup in the network
initializers : list
List of initializers defined in the network
"""
# get name of the neurongrp
component_name = neurongrp['name']
self._model_namespace["neuronname"] = component_name
# add BASE_CELL component
self._component_type = lems.ComponentType(component_name,
extends=BASE_CELL)
# get identifiers attached to neurongrp and create special_properties dict
identifiers = []
special_properties = {}
if 'identifiers' in neurongrp:
identifiers = neurongrp['identifiers']
for initializer in initializers:
if 'identifiers' in initializer:
identifiers.update(initializer['identifiers'])
for identifier in identifiers.keys():
special_properties.update({identifier: None})
# add the identifers as properties of the component
for param in self._determine_properties(identifiers):
self._component_type.add(param)
# common things for every neuron definition
# TODO: Is this same for custom events too?
self._component_type.add(lems.EventPort(name='spike', direction='out'))
# dynamics of the network
dynamics = lems.Dynamics()
# get neurongrp equations
equations = neurongrp['equations']
# loop over the variables
initializer_vars = []
for initializer in initializers:
if initializer['source'] == neurongrp['name']:
initializer_vars.append(initializer['variable'])
for var in equations.keys():
# determine the dimension
dimension = _determine_dimension(equations[var]['unit'])
# add to all_params_unit
self._all_params_unit[var] = dimension
# identify diff eqns to add Exposure
if equations[var]['type'] == 'differential equation':
state_var = lems.StateVariable(var,
dimension=dimension,
exposure=var)
self._component_type.add(
lems.Exposure(var, dimension=dimension))
dynamics.add_state_variable(state_var)
else:
if var in initializer_vars and 'i' in initializer['value']:
self._component_type.add(lems.Property(var, dimension))
special_properties[var] = initializer['value']
continue
state_var = lems.StateVariable(var, dimension=dimension)
dynamics.add_state_variable(state_var)
# what happens at initialization
onstart = lems.OnStart()
# loop over the initializers
for var in equations.keys():
if var in (NOT_REFRACTORY, LAST_SPIKE):
continue
# check the initializer is connected to this neurongrp
if var not in initializer_vars:
continue
if var in special_properties:
continue
for initializer in initializers:
if initializer['variable'] == var and initializer[
'source'] == neurongrp['name']:
init_value = initializer['value']
if type(init_value) != str:
value = brian_unit_to_lems(init_value)
else:
value = renderer.render_expr(str(init_value))
# add to onstart
onstart.add(lems.StateAssignment(var, value))
dynamics.add(onstart)
# check whether refractoriness is defined
if ('events' in neurongrp and 'spike' in neurongrp['events']
and 'refractory' in neurongrp['events']['spike']):
# if refractoriness, we create separate regimes for integrating
integr_regime = lems.Regime(INTEGRATING, dynamics,
True) # True -> initial regime
# check spike event
# NOTE: isn't refractory only for spike events?
for spike_flag, on_cond in self._event_builder(
neurongrp['events']):
if spike_flag:
# if spike occured we make transition to refractory regime
on_cond.add_action(lems.Transition(REFRACTORY))
integr_regime.add_event_handler(on_cond)
# add refractory regime
refrac_regime = lems.Regime(REFRACTORY, dynamics)
# make lastspike variable and initialize it
refrac_regime.add_state_variable(
lems.StateVariable(LAST_SPIKE, dimension='time'))
oe = lems.OnEntry()
oe.add(lems.StateAssignment(LAST_SPIKE, 't'))
refrac_regime.add(oe)
# after refractory time we make transition to integrating regime
refractory_code = neurongrp['events']['spike']['refractory']
if not _equation_separator(str(refractory_code)):
# if there is no specific variable given, we assume
# that this is time condition
ref_oc = lems.OnCondition('t .gt. ( {0} + {1} )'.format(
LAST_SPIKE, brian_unit_to_lems(refractory_code)))
else:
ref_oc = lems.OnCondition(
renderer.render_expr(refractory_code))
ref_trans = lems.Transition(INTEGRATING)
ref_oc.add_action(ref_trans)
refrac_regime.add_event_handler(ref_oc)
# identify variables with differential equation
for var in neurongrp['equations']:
if neurongrp['equations'][var][
'type'] == 'differential equation':
diff_var = neurongrp['equations'][var]
# get the expression
td = lems.TimeDerivative(
var, renderer.render_expr(diff_var['expr']))
# check flags for UNLESS_REFRACTORY TODO: is this available in 'flags' key?
if 'flags' in diff_var:
# if unless refratory we add only do integration regime
if UNLESS_REFRACTORY in diff_var['flags']:
integr_regime.add_time_derivative(td)
continue
# add time derivative to both regimes
integr_regime.add_time_derivative(td)
refrac_regime.add_time_derivative(td)
# add the regimes to dynamics
dynamics.add_regime(integr_regime)
dynamics.add_regime(refrac_regime)
else:
# adding events directly to dynamics
for spike_flag, on_cond in self._event_builder(
neurongrp['events']):
dynamics.add_event_handler(on_cond)
# get variables with diff eqns
for var in neurongrp['equations']:
if neurongrp['equations'][var][
'type'] == 'differential equation':
diff_var = neurongrp['equations'][var]
td = lems.TimeDerivative(
var, renderer.render_expr(diff_var['expr']))
# add to dynamics
dynamics.add_time_derivative(td)
# add dynamics to _component_type
self._component_type.dynamics = dynamics
# add _component_type to _model
self._model.add_component_type(self._component_type)
# get identifiers
paramdict = dict()
for ident_name, ident_value in neurongrp['identifiers'].items():
paramdict[ident_name] = self._unit_lems_validator(ident_value)
# if more than one neuron use multiinstantiate
if neurongrp['N'] == 1:
self._model.add(
lems.Component("n{}".format(index_neurongrp), component_name,
**paramdict))
else:
self.make_multiinstantiate(special_properties, component_name,
paramdict, neurongrp['N'])
def add_neurongroup(self, obj, idx_of_ng, namespace, initializers):
"""
Adds NeuronGroup object *obj* to self._model.
If number of elements is 1 it adds component of that type, if
it's bigger, the network is created by calling:
`make_multiinstantiate`.
Parameters
----------
obj : brian2.NeuronGroup
NeuronGroup object to parse
idx_of_ng : int
index of neurongroup
namespace : dict
dictionary with all neccassary variables definition
initializers : dict
initial values for all model variables
"""
if hasattr(obj, "namespace") and not obj.namespace:
obj.namespace = namespace
self._nr_of_neurons = obj.N # maybe not the most robust solution
ct_name = "neuron{}".format(idx_of_ng+1)
self._model_namespace["neuronname"] = ct_name
self._component_type = lems.ComponentType(ct_name, extends=BASE_CELL)
# adding parameters
special_properties = {}
for key in obj.namespace.keys():
special_properties[key] = None
for param in self._determine_properties(obj.namespace):
self._component_type.add(param)
# common things for every neuron definition
self._component_type.add(lems.EventPort(name='spike', direction='out'))
# dynamics of the network
dynamics = lems.Dynamics()
ng_equations = obj.user_equations
for var in ng_equations:
if ng_equations[var].type == DIFFERENTIAL_EQUATION:
dim_ = _determine_dimension(ng_equations[var].unit)
sv_ = lems.StateVariable(var, dimension=dim_, exposure=var)
self._all_params_unit[var] = dim_
dynamics.add_state_variable(sv_)
self._component_type.add(lems.Exposure(var, dimension=dim_))
elif ng_equations[var].type in (PARAMETER, SUBEXPRESSION):
if var == NOT_REFRACTORY:
continue
dim_ = _determine_dimension(ng_equations[var].unit)
self._all_params_unit[var] = dim_
# all initializers contatining iterator need to be assigned
# as a property
# i is default iterator in Brian2
if var in initializers and "i" in get_identifiers(str(initializers[var])):
self._component_type.add(lems.Property(var, dim_))
special_properties[var] = initializers[var]
continue
sv_ = lems.StateVariable(var, dimension=dim_)
dynamics.add_state_variable(sv_)
# what happens at initialization
onstart = lems.OnStart()
for var in obj.equations.names:
if var in (NOT_REFRACTORY, LAST_SPIKE):
continue
if var not in initializers:
continue
if var in special_properties:
continue
init_value = initializers[var]
if type(init_value) != str:
init_value = brian_unit_to_lems(init_value)
else:
init_value = renderer.render_expr(str(init_value))
onstart.add(lems.StateAssignment(var, init_value))
dynamics.add(onstart)
if obj._refractory:
# if refractoriness, we create separate regimes
# - for integrating
integr_regime = lems.Regime(INTEGRATING, dynamics, True) # True -> initial regime
for spike_flag, oc in self._event_builder(obj.events, obj.event_codes):
if spike_flag:
# if spike occured we make transition to refractory regime
oc.add_action(lems.Transition(REFRACTORY))
integr_regime.add_event_handler(oc)
# - for refractory
refrac_regime = lems.Regime(REFRACTORY, dynamics)
# we make lastspike variable and initialize it
refrac_regime.add_state_variable(lems.StateVariable(LAST_SPIKE, dimension='time'))
oe = lems.OnEntry()
oe.add(lems.StateAssignment(LAST_SPIKE, 't'))
refrac_regime.add(oe)
# after time spiecified in _refractory we make transition
# to integrating regime
if not _equation_separator(str(obj._refractory)):
# if there is no specific variable given, we assume
# that this is time condition
ref_oc = lems.OnCondition('t .gt. ( {0} + {1} )'.format(LAST_SPIKE, brian_unit_to_lems(obj._refractory)))
else:
ref_oc = lems.OnCondition(renderer.render_expr(obj._refractory))
ref_trans = lems.Transition(INTEGRATING)
ref_oc.add_action(ref_trans)
refrac_regime.add_event_handler(ref_oc)
for var in obj.user_equations.diff_eq_names:
td = lems.TimeDerivative(var, renderer.render_expr(str(ng_equations[var].expr)))
# if unless refratory we add only do integration regime
if UNLESS_REFRACTORY in ng_equations[var].flags:
integr_regime.add_time_derivative(td)
continue
integr_regime.add_time_derivative(td)
refrac_regime.add_time_derivative(td)
dynamics.add_regime(integr_regime)
dynamics.add_regime(refrac_regime)
else:
# here we add events directly to dynamics
for spike_flag, oc in self._event_builder(obj.events, obj.event_codes):
dynamics.add_event_handler(oc)
for var in obj.user_equations.diff_eq_names:
td = lems.TimeDerivative(var, renderer.render_expr(str(ng_equations[var].expr)))
dynamics.add_time_derivative(td)
self._component_type.dynamics = dynamics
# making componenttype is done so we add it to the model
self._model.add_component_type(self._component_type)
obj.namespace.pop("init", None) # kick out init
# adding component to the model
paramdict = dict()
for param in obj.namespace:
paramdict[param] = self._unit_lems_validator(obj.namespace[param])
if obj.N == 1:
self._model.add(lems.Component("n{}".format(idx_of_ng), ct_name, **paramdict))
else:
self.make_multiinstantiate(special_properties, ct_name, paramdict)