def spiking_component_type_to_nineml(cls):
"""Return a 9ML ComponentClass describing the spike source model."""
source = al.ComponentClass(
name="poisson_spike_source",
regimes=[
al.Regime(
name="before",
transitions=[al.On("t > start",
do=["t_spike = -1"],
to="on")]),
al.Regime(
name="on",
transitions=[al.On("t >= t_spike",
do=["t_spike = t_spike + random.exponential(rate)",
al.OutputEvent('spike_output')]),
al.On("t >= start + duration",
to="after")],
),
al.Regime(name="after")
],
state_variables=[
al.StateVariable('t_spike'), #, dimension='[T]'
],
event_ports=[al.EventSendPort('spike_output'), ],
parameters=['start', 'rate', 'duration'], # add dimensions, or infer them from dimensions of variables
)
return source
def spiking_component_type_to_nineml(cls):
"""Return a 9ML ComponentClass describing the neuron model."""
iaf = al.ComponentClass(
name="iaf_tau",
regimes=[
al.Regime(
name="subthreshold_regime",
time_derivatives=["dv/dt = (v_rest - v)/tau_m + (i_offset + i_syn)/cm"],
transitions=al.On("v > v_thresh",
do=["t_spike = t",
"v = v_reset",
al.OutputEvent('spike_output')],
to="refractory_regime"),
),
al.Regime(
name="refractory_regime",
time_derivatives=["dv/dt = 0"],
transitions=[al.On("t >= t_spike + tau_refrac",
to="subthreshold_regime")],
)
],
state_variables=[
al.StateVariable('v'), #, dimension='[V]' # '[V]' should be an alias for [M][L]^2[T]^-3[I]^-1
al.StateVariable('t_spike'), #, dimension='[T]'
],
analog_ports=[al.AnalogSendPort("v"),
al.AnalogReducePort("i_syn", reduce_op="+"), ],
event_ports=[al.EventSendPort('spike_output'), ],
parameters=['cm', 'tau_refrac', 'tau_m', 'v_reset', 'v_rest', 'v_thresh', 'i_offset'] # add dimensions, or infer them from dimensions of variables
# in fact, we should be able to infer what are the parameters, without listing them
)
return iaf
def synaptic_receptor_component_type_to_nineml(cls, synapse_type):
"""Return a 9ML ComponentClass describing the synaptic receptor model."""
coba = al.ComponentClass(
name="cond_exp_syn",
aliases=["i_syn:=g_syn*(e_rev-v)", ],
regimes=[
al.Regime(
name="coba_default_regime",
time_derivatives=["dg_syn/dt = -g_syn/tau_syn", ],
transitions=al.On('spike_input', do=["g_syn=g_syn+q"]),
)
],
state_variables=[al.StateVariable('g_syn')], #, dimension='[G]' # alias [M]^-1[L]^-2[T]^3[I]^2
analog_ports=[al.AnalogReceivePort("v"), al.AnalogSendPort("i_syn"), al.AnalogReceivePort('q')],
parameters=['tau_syn', 'e_rev']
)
return coba
def spiking_component_type_to_nineml(cls):
"""Return a 9ML ComponentClass describing the spike source model."""
source = al.ComponentClass(
name="spike_source_array",
regimes=[
al.Regime(
name="on",
transitions=[al.On("t >= spike_times[i]", # this is currently illegal
do=["i = i + 1",
al.OutputEvent('spike_output')])],
),
],
state_variables=[
al.StateVariable('t_spike'), #, dimension='[T]'
al.StateVariable('i'), #, dimension='[T]'
],
event_ports=[al.EventSendPort('spike_output'), ],
parameters=['start', 'rate', 'duration'], # add dimensions, or infer them from dimensions of variables
)
return source