def test_simneuronsmerger_warning(rng):
nt = nengo.PoissonSpiking(nengo.Tanh())
op1 = SimNeurons(
nt, J=Signal(shape=(1,)), output=Signal(shape=(1,)), state={"rng": rng}
)
op2 = SimNeurons(
nt, J=Signal(shape=(1,)), output=Signal(shape=(1,)), state={"rng": rng}
)
assert SimNeuronsMerger.is_mergeable(op1, op2)
with pytest.warns(UserWarning, match="Extra state has been modified"):
SimNeuronsMerger.merge([op1, op2])
def test_random_spiking(Simulator, inference_only, seed):
with nengo.Network() as net:
config.configure_settings(inference_only=inference_only)
inp = nengo.Node([1])
ens0 = nengo.Ensemble(100, 1, neuron_type=nengo.Tanh(), seed=seed)
ens1 = nengo.Ensemble(
100,
1,
neuron_type=nengo.StochasticSpiking(nengo.Tanh()),
seed=seed,
)
ens2 = nengo.Ensemble(
100,
1,
neuron_type=nengo.PoissonSpiking(nengo.Tanh()),
seed=seed,
)
nengo.Connection(inp, ens0, synapse=None)
nengo.Connection(inp, ens1, synapse=None)
nengo.Connection(inp, ens2, synapse=None)
p0 = nengo.Probe(ens0.neurons)
p1 = nengo.Probe(ens1.neurons)
p2 = nengo.Probe(ens2.neurons)
with pytest.warns(None) as recwarns:
with Simulator(net, seed=seed) as sim:
sim.run_steps(10000)
assert not any("native TensorFlow implementation" in str(w.message)
for w in recwarns)
assert np.allclose(sim.data[p0][0],
np.mean(sim.data[p1], axis=0),
atol=1,
rtol=2e-1)
assert np.allclose(sim.data[p0],
np.mean(sim.data[p2], axis=0),
atol=1,
rtol=1e-1)
def test_spikes_to_spikes_warning():
with pytest.warns(UserWarning, match="type 'LIF', which is a spiking"):
nengo.PoissonSpiking(nengo.LIF())
nengo.Probe(c)
with Simulator(net, seed=seed) as sim:
sim.check_gradients()
@pytest.mark.eager_only
@pytest.mark.parametrize(
"rate, spiking",
[
(nengo.RectifiedLinear, nengo.SpikingRectifiedLinear),
(nengo.LIFRate, nengo.LIF),
(SoftLIFRate, nengo.LIF),
pytest.param(
nengo.LIFRate,
lambda **kwargs: nengo.PoissonSpiking(nengo.LIFRate(**kwargs)),
marks=pytest.mark.skipif(
version.parse(nengo.__version__) < version.parse("3.1.0dev0"),
reason="PoissonSpiking does not exist",
),
),
pytest.param(
nengo.LIFRate,
lambda **kwargs: nengo.RegularSpiking(nengo.LIFRate(), **kwargs),
marks=pytest.mark.skipif(
version.parse(nengo.__version__) < version.parse("3.1.0dev0"),
reason="RegularSpiking does not exist",
),
),
],
)