def build_network(sim, order=1000, epsilon=0.1, delay=1.5, J=0.1, theta=20.0,
tau=20.0, tau_syn=0.1, tau_refrac=2.0, v_reset=10.0,
R=1.5, g=5, eta=2, seed=None):
NE = 4 * order
NI = 1 * order
CE = int(epsilon * NE) # number of excitatory synapses per neuron
CI = int(epsilon * NI) # number of inhibitory synapses per neuron
CMem = tau/R
J_unit = psp_height(tau, R, tau_syn)
J_ex = J / J_unit
J_in = -g * J_ex
nu_th = theta / (J_ex * CE * R * tau_syn)
nu_ex = eta * nu_th
p_rate = 1000.0 * nu_ex * CE
assert seed is not None
rng = NumpyRNG(seed)
neuron_params = {
"nrn_tau": tau,
"nrn_v_threshold": theta,
"nrn_refractory_period": tau_refrac,
"nrn_v_reset": v_reset,
"nrn_R": R,
"syn_tau": tau_syn
}
celltype = Dynamics(name='iaf',
subnodes={'nrn': read("sources/BrunelIaF.xml")['BrunelIaF'],
'syn': read("sources/AlphaPSR.xml")['AlphaPSR']})
celltype.connect_ports('syn.i_synaptic', 'nrn.i_synaptic')
exc = sim.Population(NE, nineml_cell_type('BrunelIaF', celltype, {'syn': 'syn_weight'})(**neuron_params))
inh = sim.Population(NI, nineml_cell_type('BrunelIaF', celltype, {'syn': 'syn_weight'})(**neuron_params))
all = exc + inh
all.initialize(v=RandomDistribution('uniform', (0.0, theta), rng=rng))
stim = sim.Population(NE + NI, nineml_cell_type('Poisson', read("sources/Poisson.xml")['Poisson'], {})(rate=p_rate))
print("Connecting network")
exc_synapse = sim.StaticSynapse(weight=J_ex, delay=delay)
inh_synapse = sim.StaticSynapse(weight=J_in, delay=delay)
input_connections = sim.Projection(stim, all, sim.OneToOneConnector(), exc_synapse, receptor_type="syn")
exc_connections = sim.Projection(exc, all, sim.FixedNumberPreConnector(n=CE), exc_synapse, receptor_type="syn") # check is Pre not Post
inh_connections = sim.Projection(inh, all, sim.FixedNumberPreConnector(n=CI), inh_synapse, receptor_type="syn")
return stim, exc, inh
w_eff * cell_parameters['nrn_tau'] * cell_parameters['syn_tau'])
print("\ntau = {}, R = {}, tau_syn = {}".format(cell_parameters['nrn_tau'],
cell_parameters["nrn_R"],
cell_parameters["syn_tau"]))
print("\nEffective weight = {} nA".format(w_eff))
print("Threshold rate = {} Hz\n".format(nu_thresh))
# PyNN/NineML simulation
sim.setup(timestep=dt)
celltype = Dynamics(name='iaf',
subnodes={'nrn': read("../sources/BrunelIaF.xml")['BrunelIaF'],
'syn': read("../sources/AlphaPSR.xml")['AlphaPSR']})
celltype.connect_ports('syn.i_synaptic', 'nrn.i_synaptic')
p1 = sim.Population(4, nineml_cell_type('BrunelIaF', celltype, {'syn': 'syn_weight'})(**cell_parameters))
cell_parameters_no_spikes = copy(cell_parameters)
cell_parameters_no_spikes["nrn_v_threshold"] = 1000.0
p2 = sim.Population(4, nineml_cell_type('BrunelIaF', celltype, {'syn': 'syn_weight'})(**cell_parameters_no_spikes))
stim = sim.Population(4,
nineml_cell_type('Poisson', read("../sources/Poisson.xml")['Poisson'], {})(
rate=[0.5*nu_thresh, nu_thresh, 2*nu_thresh, 0.0]))
prj1 = sim.Projection(stim, p1,
sim.OneToOneConnector(),
sim.StaticSynapse(weight=w_eff, delay=delay),
receptor_type='syn')
prj2 = sim.Projection(stim, p2,
sim.OneToOneConnector(),
AnalogReceivePort("V", un.voltage),
AnalogSendPort("I", un.current)
],
parameters=[
Parameter('tau', un.time),
Parameter('q', un.conductance),
Parameter('vrev', un.voltage)
])
iaf_2coba = Dynamics(name="iaf_2coba",
subnodes={
"iaf": iaf,
"excitatory": coba,
"inhibitory": deepcopy(coba)
})
iaf_2coba.connect_ports("iaf.V", "excitatory.V")
iaf_2coba.connect_ports("iaf.V", "inhibitory.V")
iaf_2coba.connect_ports("excitatory.I", "iaf.ISyn")
iaf_2coba.connect_ports("inhibitory.I", "iaf.ISyn")
celltype_cls = sim.nineml.nineml_celltype_from_model(
name="iaf_2coba",
nineml_model=iaf_2coba,
synapse_components=[
sim.nineml.CoBaSyn(namespace='excitatory', weight_connector='q'),
sim.nineml.CoBaSyn(namespace='inhibitory', weight_connector='q')
])
parameters = {
'iaf_cm': 1.0,
'iaf_gl': 50.0,
cell_parameters["nrn_R"],
cell_parameters["syn_tau"])
w_eff = weight/scale_factor
delay = 0.5
print("\nEffective weight = {} nA\n".format(w_eff))
# PyNN/NineML simulation
sim.setup(timestep=dt)
celltype = Dynamics(name='iaf',
subnodes={'nrn': read("../sources/BrunelIaF.xml")['BrunelIaF'],
'syn': read("../sources/AlphaPSR.xml")['AlphaPSR']})
celltype.connect_ports('syn.i_synaptic', 'nrn.i_synaptic')
p = sim.Population(2, nineml_cell_type('BrunelIaF', celltype, {'syn': 'syn_weight'})(**cell_parameters))
stim = sim.Population(1, sim.SpikeSourceArray(spike_times=spike_times))
prj = sim.Projection(stim, p,
sim.AllToAllConnector(),
sim.StaticSynapse(weight=w_eff, delay=delay),
receptor_type='syn')
p.record(['nrn_v', 'syn_a', 'syn_b'])
sim.run(t_stop)
nrn_data = p.get_data().segments[0]
transitions=On('spikeinput', do=["g=g+q"]),
)
],
state_variables=[StateVariable('g', un.conductance)],
analog_ports=[AnalogReceivePort("V", un.voltage),
AnalogSendPort("I", un.current)],
parameters=[Parameter('tau', un.time),
Parameter('q', un.conductance),
Parameter('vrev', un.voltage)])
iaf_2coba = Dynamics(
name="iaf_2coba",
subnodes={"iaf": iaf,
"excitatory": coba,
"inhibitory": deepcopy(coba)})
iaf_2coba.connect_ports("iaf.V", "excitatory.V")
iaf_2coba.connect_ports("iaf.V", "inhibitory.V")
iaf_2coba.connect_ports("excitatory.I", "iaf.ISyn")
iaf_2coba.connect_ports("inhibitory.I", "iaf.ISyn")
celltype_cls = sim.nineml.nineml_celltype_from_model(
name="iaf_2coba",
nineml_model=iaf_2coba,
synapse_components=[
sim.nineml.CoBaSyn(namespace='excitatory', weight_connector='q'),
sim.nineml.CoBaSyn(namespace='inhibitory', weight_connector='q')])
parameters = {
'iaf_cm': 1.0,
'iaf_gl': 50.0,
'iaf_taurefrac': 2.0,
