def get_component():
# Leaky iaf
regimes = [
al.Regime(
"dV/dt = (-gL*(V-vL) + Isyn)/C",
transitions=al.On(
"V>Vth", do=["tspike = t", "V = V_reset", al.OutputEvent('spikeoutput')], to="refractory-regime"),
name="sub-threshold-regime"
),
al.Regime(
transitions=al.On("t >= tspike + trefractory", to="sub-threshold-regime"),
name="refractory-regime"
)]
analog_ports = [al.SendPort("V"),
al.ReducePort("Isyn", reduce_op="+")]
leaky_iaf = al.ComponentClass("LeakyIAF", regimes=regimes, analog_ports=analog_ports)
# ampa
regimes = [
al.Regime(
"dg/dt = -g/tau",
transitions=al.On(al.SpikeInputEvent, do="g+=q")
)]
analog_ports = [al.RecvPort("V"),
al.SendPort("Isyn = g(E-V)")]
coba_syn = al.ComponentClass("CoBaSynapse", regimes=regimes, analog_ports=analog_ports)
def _generate_cell_type_and_parameters(self, nineml_population):
"""
Determine cell class from standardcell--catalog mapping and cellparams
from nineml_population.prototype.parameters and standardcell.translations
"""
neuron_model = nineml_population.prototype.definition.component
neuron_namespace = _generate_variable_name(nineml_population.prototype.name)
synapse_models = {}
cell_params = {} # to be implemented
if nineml_population.name in self.psr_map:
for psr_component in self.psr_map[nineml_population.name]:
synapse_models[_generate_variable_name(psr_component.name)] = psr_component.definition.component
subnodes = {neuron_namespace: neuron_model}
subnodes.update(synapse_models)
combined_model = al.ComponentClass(name=_generate_variable_name(nineml_population.name),
subnodes=subnodes)
# now connect ports
connections = []
for source in [port for port in neuron_model.analog_ports if port.mode == 'send']:
for synapse_name, syn_model in synapse_models.items():
for target in [port for port in syn_model.analog_ports if port.mode == 'recv']:
connections.append(("%s.%s" % (neuron_namespace, source.name), "%s.%s" % (synapse_name, target.name)))
for synapse_name, syn_model in synapse_models.items():
for source in [port for port in syn_model.analog_ports if port.mode == 'send']:
for target in [port for port in neuron_model.analog_ports if port.mode in ('recv, reduce')]:
connections.append(("%s.%s" % (synapse_name, source.name), "%s.%s" % (neuron_namespace, target.name)))
#import pdb; pdb.set_trace()
for connection in connections:
combined_model.connect_ports(*connection)
synapse_components = [self._nineml_module.CoBaSyn(namespace=name, weight_connector='q') for name in synapse_models.keys()]
celltype_cls = self._nineml_module.nineml_cell_type(
combined_model.name, combined_model, synapse_components)
cell_params, random_params = resolve_parameters(cell_params, self.random_distributions)
return celltype_cls, cell_params, random_params
def get_component():
subthreshold_regime = al.Regime(
name="subthreshold_regime",
time_derivatives=[
"dV/dt = (g_L*(E_L-V) + g_sfa*(E_sfa-V) + g_rr*(E_rr-V) + Isyn)/C",
"dg_sfa/dt = -g_sfa/tau_sfa",
"dg_rr/dt = -g_rr/tau_rr",
],
transitions=al.On("V> theta",
do=["g_sfa =g_sfa + q_sfa", "g_rr =g_rr + q_rr", "t_spike = t",
al.OutputEvent('spikeoutput')],
to="refractory_regime"),
)
refractory_regime = al.Regime(
name="refractory_regime",
transitions=al.On("t >= t_spike + t_ref",
to='subthreshold_regime'),
)
analog_ports = [al.SendPort("V"),
al.ReducePort("Isyn", reduce_op="+")]
c1 = al.ComponentClass("iaf_sfa_relref",
regimes=[subthreshold_regime, refractory_regime],
analog_ports=analog_ports,
)
return c1
def get_component():
parameters = [
'C_m', 'g_L', 'E_L', 'Delta', 'V_T', 'S', 'tau_ref', 'tau_w', 'a', 'b'
]
aeIF = al.ComponentClass(
"aeIF",
regimes=[
al.Regime(
name="subthresholdregime",
time_derivatives=[
"dV/dt = -g_L*(V-E_L)/C_m + g_L*Delta*exp((V-V_T)/Delta-w/S)/C_m+ Isyn/C_m",
"dw/dt = (a*(V-E_L)-w)/tau_w",
],
transitions=al.On(
"V > V_T",
do=["V = E_L", "w = w + b",
al.OutputEvent('spikeoutput')],
to="refractoryregime"),
),
al.Regime(
name="refractoryregime",
transitions=al.On("t>=tspike+trefractory",
to="subthresholdregime"),
)
],
analog_ports=[al.ReducePort("Isyn", reduce_op="+")])
return aeIF
def get_hh_na():
hh_na = al.ComponentClass(
name="Hodgkin-Huxley-Na",
dynamicsblock=DynamicsBlock(
regimes=[
al.Regime(name="hh_regime_na",
time_derivatives=[
"dm/dt = (minf(V)-m)/mtau(V)",
"dh/dt = (hinf(V)-h)/htau(V)"
])
],
aliases=[
"q10 := 3.0**((celsius - 6.3)/10.0)",
"alpha_m := -0.1*(V+40.0)/(exp(-(V+40.0)/10.0) - 1.0)",
"beta_m := 4.0*exp(-(V+65.0)/18.0)",
"mtau := 1/(q10*(alpha_m(V) + beta_m(V)))",
"minf := alpha_m(V)/(alpha_m(V) + beta_m(V))",
"alpha_h := 0.07*exp(-(V+65.0)/20.0)",
"beta_h := 1.0/(exp(-(V+35)/10.0) + 1.0)",
"htau := 1.0/(q10*(alpha_h(V) + beta_h(V)))",
"hinf := alpha_h(V)/(alpha_h(V) + beta_h(V))",
"gna := gnabar*m*m*m*h",
"i := gna * (ena-V)",
],
state_variables=[StateVariable('m'),
StateVariable('h')],
),
analog_ports=[SendPort("i"), RecvPort("V")],
)
def get_component():
subthreshold_regime = al.Regime(
name="subthreshold_regime",
time_derivatives=[
"dV/dt = (-gL*(V-vL) + Isyn)/C",
],
transitions=[
al.On("V> theta",
do=[
"t_spike = t", "V = V_reset",
al.OutputEvent('spikeoutput')
],
to="refractory_regime")
],
)
refractory_regime = al.Regime(
transitions=[al.On("t >= t_spike + t_ref", to='subthreshold_regime')],
name="refractory_regime")
analog_ports = [al.SendPort("V"), al.ReducePort("Isyn", reduce_op="+")]
c1 = al.ComponentClass("LeakyIAF",
regimes=[subthreshold_regime, refractory_regime],
analog_ports=analog_ports)
return c1
def get_component():
inter_event_regime = al.Regime(
name="intereventregime",
time_derivatives=["dA/dt = -A/taur", "dB/dt = -B/taud"],
transitions=[
al.On('spikeinput',
do=["A = A + weight*factor", "B = B + weight*factor"])
])
dynamicsblock = al.DynamicsBlock(
aliases=[
"taupeak := taur*taud/(taud - taur)*log(taud/taur)",
"factor := 1/(exp(-taupeak/taud) - exp(-taupeak/taur))",
"gB := 1/(1 + mgconc*exp(-1*gamma*V)/beta)",
"g := gB*gmax*(B-A)",
"I := g * df",
"df := (E-V)",
],
state_variables=[al.StateVariable(o) for o in ('A', 'B')],
regimes=[inter_event_regime],
)
nmda = al.ComponentClass(name="NMDAPSR",
dynamicsblock=dynamicsblock,
analog_ports=[
al.RecvPort("V"),
al.SendPort("I"),
],
event_ports=[al.RecvEventPort('spikeinput')],
parameters=[
'taur', 'taud', 'gmax', 'mgconc', 'gamma',
'beta', 'E', 'weight'
])
return nmda
def get_aeIF_component():
"""
Adaptive exponential integrate-and-fire neuron as described in
A. Destexhe, J COmput Neurosci 27: 493--506 (2009)
Author B. Kriener (Jan 2011)
## neuron model: aeIF
## variables:
## V: membrane potential
## w: adaptation variable
## parameters:
## C_m # specific membrane capacitance [muF/cm**2]
## g_L # leak conductance [mS/cm**2]
## E_L # resting potential [mV]
## Delta # steepness of exponential approach to threshold [mV]
## V_T # spike threshold [mV]
## S # membrane area [mum**2]
## trefractory # refractory time [ms]
## tspike # spike time [ms]
## tau_w # adaptation time constant
## a, b # adaptation parameters [muS, nA]
"""
parameters = [
'C_m', 'g_L', 'E_L', 'Delta', 'V_T', 'S', 'trefractory', 'tspike',
'tau_w', 'a', 'b'
]
aeIF = al.ComponentClass(
"aeIF",
regimes=[
al.Regime(
name="subthresholdregime",
time_derivatives=[
"dV/dt = -g_L*(V-E_L)/C_m + Isyn/C_m + g_L*Delta*exp((V-V_T)/Delta-w/S)/C_m",
"dw/dt = (a*(V-E_L)-w)/tau_w",
],
transitions=al.On(
"V > V_T",
do=["V = E_L", "w = w + b",
al.OutputEvent('spikeoutput')],
to="refractoryregime"),
),
al.Regime(
name="refractoryregime",
transitions=al.On("t>=tspike+trefractory",
to="subthresholdregime"),
)
],
analog_ports=[al.ReducePort("Isyn", reduce_op="+")])
return aeIF
def get_component():
aliases = [
"q10 := 3.0**((celsius - 6.3)/10.0)", # temperature correction factor
"alpha_m := -0.1*(V+40.0)/(exp(-(V+40.0)/10.0) - 1.0)", # m
"beta_m := 4.0*exp(-(V+65.0)/18.0)",
"mtau := 1/(q10*(alpha_m + beta_m))",
"minf := alpha_m/(alpha_m + beta_m)",
"alpha_h := 0.07*exp(-(V+65.0)/20.0)", # h
"beta_h := 1.0/(exp(-(V+35)/10.0) + 1.0)",
"htau := 1.0/(q10*(alpha_h + beta_h))",
"hinf := alpha_h/(alpha_h + beta_h)",
"alpha_n := -0.01*(V+55.0)/(exp(-(V+55.0)/10.0) - 1.0)", # n
"beta_n := 0.125*exp(-(V+65.0)/80.0)",
"ntau := 1.0/(q10*(alpha_n + beta_n))",
"ninf := alpha_n/(alpha_n + beta_n)",
"gna := gnabar*m*m*m*h", #
"gk := gkbar*n*n*n*n",
"ina := gna*(ena - V)", # currents
"ik := gk*(ek - V)",
"il := gl*(el - V )"
]
hh_regime = al.Regime("dn/dt = (ninf-n)/ntau",
"dm/dt = (minf-m)/mtau",
"dh/dt = (hinf-h)/htau",
"dV/dt = (ina + ik + il + Isyn)/C",
transitions=al.On("V > theta",
do=al.SpikeOutputEvent()))
# the rest are not "parameters" but aliases, assigned vars, state vars,
# indep vars, analog_analog_ports, etc.
parameters = [
'el',
'C',
'ek',
'ena',
'gkbar',
'gnabar',
'theta',
'gl',
'celsius',
]
analog_ports = [al.SendPort("V"), al.ReducePort("Isyn", reduce_op="+")]
c1 = al.ComponentClass("HodgkinHuxley",
parameters=parameters,
regimes=(hh_regime, ),
aliases=aliases,
analog_ports=analog_ports)
return c1
def get_component():
regimes = [
al.Regime(
name="sub_threshold_regime",
time_derivatives=[
"dV/dt = (v_rest - V)/tau_m + (gE*(e_rev_E - V) + gI*(e_rev_I - V) + i_offset)/cm",
"dgE/dt = -gE/tau_syn_E",
"dgI/dt = -gI/tau_syn_I",
],
transitions=(
al.On("V > v_thresh",
do=[
"t_spike = t", "V = v_reset",
al.OutputEvent('spikeoutput')
],
to="refractory_regime"),
al.On('excitatory', do="gE=gE+q"),
al.On('inhibitory', do="gI=gI+q"),
),
),
al.Regime(
name="refractory_regime",
time_derivatives=[
"dgE/dt = -gE/tau_syn_E",
"dgI/dt = -gI/tau_syn_I",
],
transitions=(
al.On("t >= t_spike + tau_refrac", to="sub_threshold_regime"),
al.On('excitatoryspike', do="gE=gE+q"),
al.On('inhibitoryspike', do="gI=gI+q"),
),
)
]
analog_ports = [
al.SendPort("V"),
al.SendPort("gE"),
al.SendPort("gI"),
al.RecvPort("q")
]
c1 = al.ComponentClass("IF_cond_exp",
regimes=regimes,
analog_ports=analog_ports)
return c1
def get_component():
regimes = [
al.Regime(
name='defaultregime',
time_derivatives=[
"dR/dt = (1-R)/tau_r", # tau_r is the recovery time constant for depression
"du/dt = -(u-U)/tau_f", # tau_f is the time constant of facilitation
],
transition=al.On(
'spikeoutput',
do=[
"Wout = u*R*Win", "R = R - u*R", "u = u + U*(1-u)",
al.PreEventRelay
]) # Should I put a OutputEvent('spikeoutput') here?
)
]
analog_ports = [al.SendPort("Wout")]
c1 = al.ComponentClass("MarkramSynapseDynamics",
regimes=regimes,
analog_ports=analog_ports)
def get_component():
regimes = [
al.Regime(
name="subthreshold_regime",
time_derivatives=[
"dV/dt = 0.04*V*V + 5*V + 140.0 - U + Isyn",
"dU/dt = a*(b*V - U)"
],
transitions=[
al.On("V > theta",
do=["V = c", "U = U + d",
al.OutputEvent('spikeoutput')])
],
)
]
analog_ports = [al.SendPort("V"), al.ReducePort("Isyn", reduce_op="+")]
c1 = al.ComponentClass("Izhikevich",
regimes=regimes,
analog_ports=analog_ports)
return c1
import nineml.abstraction_layer as al
cond_decay = al.Regime(name='default',
time_derivatives=["dg/dt = -g/tau"],
transitions=[al.On(al.InputEvent('spikeinput'), do="g = g + q")]
)
coba_syn = al.ComponentClass(
name="CoBaSynapse",
dynamics=al.DynamicsBlock(
regimes=[cond_decay],
aliases=["I := g*(E-V)"],
),
analog_ports=[al.RecvPort("V"), al.SendPort("I")]
)
import nineml.abstraction_layer as al
# Define a single regime, and specify the differential equations,
# and a transition when we reach spiking threshold:
regime = al.Regime(
name='defaultregime',
time_derivatives=[
'dV/dt = 0.04*V*V + 5*V + 140 -U + I', 'dU/dt = a*(b*V - U)'
],
transitions=[
al.On('V > a',
do=['V = c', 'U = U + d ',
al.OutputEvent('spikeoutput')])
])
# Create the ComponentClass, including the dynamics.
iz = al.ComponentClass(name="IzikevichNeuron",
parameters=['a', 'b', 'c', 'd'],
analog_ports=[
al.AnalogPort('I', mode='recv'),
al.AnalogPort('V', mode='send')
],
event_ports=[al.EventPort('spikeoutput', mode='send')],
dynamicsblock=al.DynamicsBlock(
regimes=[regime], state_variables=['V', 'U']))
import nineml.abstraction_layer as al
iz = al.ComponentClass(
name="IzikevichNeuron",
parameters=['a', 'b', 'c', 'd'],
analog_ports=[
al.AnalogPort('I', mode='recv'),
al.AnalogPort('V', mode='send')
],
event_ports=[al.EventPort('spikeoutput', mode='send')],
)
import nineml.abstraction_layer as al
r1 = al.Regime(
name="sub-threshold-regime",
time_derivatives=["dV/dt = (-gL*(V-vL) + I)/C", ],
transitions=[al.On("V>Vth",
do=["tspike = t",
"V = V_reset",
al.OutputEvent('spike')],
to="refractory-regime"), ],
)
r2 = al.Regime(name="refractory-regime",
transitions=[al.On("t >= tspike + trefractory",
to="sub-threshold-regime"), ],
)
leaky_iaf = al.ComponentClass("LeakyIAF",
dynamicsblock=al.DynamicsBlock(
regimes=[r1, r2],
state_variables=['V', 'tspike'],
),
analog_ports=[al.AnalogPort("I", mode='recv')],
parameters=['C', 'V_reset', 'Vth', 'gL', 't', 'trefractory', 'vL'],
)
leaky_iaf.write("leaky_iaf.xml")
