def test_probeable():
def check_neuron_type(neuron_type, expected):
assert neuron_type.probeable == expected
ens = nengo.Ensemble(10, 1, neuron_type=neuron_type)
assert ens.neurons.probeable == expected + ("input", )
with nengo.Network():
check_neuron_type(Direct(), ("output", ))
check_neuron_type(RectifiedLinear(), ("output", ))
check_neuron_type(SpikingRectifiedLinear(), ("output", "voltage"))
check_neuron_type(Sigmoid(), ("output", ))
check_neuron_type(Tanh(), ("output", ))
check_neuron_type(LIFRate(), ("output", ))
check_neuron_type(LIF(), ("output", "voltage", "refractory_time"))
check_neuron_type(AdaptiveLIFRate(), ("output", "adaptation"))
check_neuron_type(
AdaptiveLIF(),
("output", "voltage", "refractory_time", "adaptation"))
check_neuron_type(Izhikevich(), ("output", "voltage", "recovery"))
check_neuron_type(RegularSpiking(LIFRate()),
("output", "rate_out", "voltage"))
check_neuron_type(StochasticSpiking(AdaptiveLIFRate()),
("output", "rate_out", "adaptation"))
check_neuron_type(PoissonSpiking(LIFRate()), ("output", "rate_out"))
def test_lif_rate(n_elements):
"""Test the `lif_rate` nonlinearity"""
rng = np.random
dt = 1e-3
n_neurons = [123459, 23456, 34567]
J = RA([rng.normal(loc=1, scale=10, size=n) for n in n_neurons])
R = RA([np.zeros(n) for n in n_neurons])
ref = 2e-3
taus = list(rng.uniform(low=15e-3, high=80e-3, size=len(n_neurons)))
queue = cl.CommandQueue(ctx)
clJ = CLRA(queue, J)
clR = CLRA(queue, R)
clTau = CLRA(queue, RA(taus))
# simulate host
nls = [
LIFRate(tau_ref=ref, tau_rc=taus[i]) for i, n in enumerate(n_neurons)
]
for i, nl in enumerate(nls):
nl.step_math(dt, J[i], R[i])
# simulate device
plan = plan_lif_rate(queue,
clJ,
clR,
ref,
clTau,
dt=dt,
n_elements=n_elements)
plan()
rate_sum = np.sum([np.sum(r) for r in R])
if rate_sum < 1.0:
logger.warn("LIF rate was not tested above the firing threshold!")
assert ra.allclose(J, clJ.to_host())
assert ra.allclose(R, clR.to_host())
def test_argreprs():
"""Test repr() for each neuron type."""
def check_init_args(cls, args):
assert getfullargspec(cls.__init__).args[1:] == args
def check_repr(obj):
assert eval(repr(obj)) == obj
check_init_args(Direct, [])
check_repr(Direct())
check_init_args(RectifiedLinear, ["amplitude"])
check_repr(RectifiedLinear())
check_repr(RectifiedLinear(amplitude=2))
check_init_args(SpikingRectifiedLinear, ["amplitude"])
check_repr(SpikingRectifiedLinear())
check_repr(SpikingRectifiedLinear(amplitude=2))
check_init_args(Sigmoid, ["tau_ref"])
check_repr(Sigmoid())
check_repr(Sigmoid(tau_ref=0.1))
check_init_args(LIFRate, ["tau_rc", "tau_ref", "amplitude"])
check_repr(LIFRate())
check_repr(LIFRate(tau_rc=0.1))
check_repr(LIFRate(tau_ref=0.1))
check_repr(LIFRate(amplitude=2))
check_repr(LIFRate(tau_rc=0.05, tau_ref=0.02))
check_repr(LIFRate(tau_rc=0.05, amplitude=2))
check_repr(LIFRate(tau_ref=0.02, amplitude=2))
check_repr(LIFRate(tau_rc=0.05, tau_ref=0.02, amplitude=2))
check_init_args(LIF, ["tau_rc", "tau_ref", "min_voltage", "amplitude"])
check_repr(LIF())
check_repr(LIF(tau_rc=0.1))
check_repr(LIF(tau_ref=0.1))
check_repr(LIF(amplitude=2))
check_repr(LIF(min_voltage=-0.5))
check_repr(LIF(tau_rc=0.05, tau_ref=0.02))
check_repr(LIF(tau_rc=0.05, amplitude=2))
check_repr(LIF(tau_ref=0.02, amplitude=2))
check_repr(LIF(tau_rc=0.05, tau_ref=0.02, amplitude=2))
check_repr(LIF(tau_rc=0.05, tau_ref=0.02, min_voltage=-0.5, amplitude=2))
check_init_args(AdaptiveLIFRate,
["tau_n", "inc_n", "tau_rc", "tau_ref", "amplitude"])
check_repr(AdaptiveLIFRate())
check_repr(AdaptiveLIFRate(tau_n=0.1))
check_repr(AdaptiveLIFRate(inc_n=0.5))
check_repr(AdaptiveLIFRate(tau_rc=0.1))
check_repr(AdaptiveLIFRate(tau_ref=0.1))
check_repr(AdaptiveLIFRate(amplitude=2))
check_repr(
AdaptiveLIFRate(tau_n=0.1,
inc_n=0.5,
tau_rc=0.05,
tau_ref=0.02,
amplitude=2))
check_init_args(
AdaptiveLIF,
["tau_n", "inc_n", "tau_rc", "tau_ref", "min_voltage", "amplitude"])
check_repr(AdaptiveLIF())
check_repr(AdaptiveLIF(tau_n=0.1))
check_repr(AdaptiveLIF(inc_n=0.5))
check_repr(AdaptiveLIF(tau_rc=0.1))
check_repr(AdaptiveLIF(tau_ref=0.1))
check_repr(AdaptiveLIF(min_voltage=-0.5))
check_repr(
AdaptiveLIF(
tau_n=0.1,
inc_n=0.5,
tau_rc=0.05,
tau_ref=0.02,
min_voltage=-0.5,
amplitude=2,
))
check_init_args(
Izhikevich,
["tau_recovery", "coupling", "reset_voltage", "reset_recovery"])
check_repr(Izhikevich())
check_repr(Izhikevich(tau_recovery=0.1))
check_repr(Izhikevich(coupling=0.3))
check_repr(Izhikevich(reset_voltage=-1))
check_repr(Izhikevich(reset_recovery=5))
check_repr(
Izhikevich(tau_recovery=0.1,
coupling=0.3,
reset_voltage=-1,
reset_recovery=5))
def step_math(self, dt, J, spiked):
rates = np.zeros_like(J)
LIFRate.step_math(self, dt=1, J=J, output=rates)
self._poisson_step_math(dt, rates, spiked)
for max_rate, intercept in [(300.0, 1.1), (300.0, 1.0), (100.0, 0.9), (100, 1.0)]:
with nengo.Network() as net:
nengo.Ensemble(
1,
1,
neuron_type=Sigmoid(),
max_rates=[max_rate],
intercepts=[intercept],
)
with pytest.raises(BuildError, match="lead to neurons with negative"):
with Simulator(net):
pass
@pytest.mark.slow
@pytest.mark.parametrize("base_type", [LIFRate(), RectifiedLinear(), Tanh()])
def test_spiking_types(base_type, seed, plt, allclose):
spiking_types = {
RegularSpiking: dict(atol=0.05, rmse_target=0.011),
PoissonSpiking: dict(atol=0.13, rmse_target=0.024),
StochasticSpiking: dict(atol=0.10, rmse_target=0.019),
}
n_neurons = 1000
with nengo.Network(seed=seed) as net:
u = nengo.Node(lambda t: np.sin(2 * np.pi * t))
a = nengo.Ensemble(n_neurons, 1)
nengo.Connection(u, a)
u_p = nengo.Probe(u, synapse=0.005)
a_p = nengo.Probe(a, synapse=0.005)
def test_mergeable():
# anything is mergeable with an empty list
assert mergeable(None, [])
# ops with different numbers of sets/incs/reads/updates are not mergeable
assert not mergeable(dummies.Op(sets=[dummies.Signal()]), [dummies.Op()])
assert not mergeable(dummies.Op(incs=[dummies.Signal()]), [dummies.Op()])
assert not mergeable(dummies.Op(reads=[dummies.Signal()]), [dummies.Op()])
assert not mergeable(dummies.Op(updates=[dummies.Signal()]), [dummies.Op()])
assert mergeable(dummies.Op(sets=[dummies.Signal()]),
[dummies.Op(sets=[dummies.Signal()])])
# check matching dtypes
assert not mergeable(dummies.Op(sets=[dummies.Signal(dtype=np.float32)]),
[dummies.Op(sets=[dummies.Signal(dtype=np.float64)])])
# shape mismatch
assert not mergeable(dummies.Op(sets=[dummies.Signal(shape=(1, 2))]),
[dummies.Op(sets=[dummies.Signal(shape=(1, 3))])])
# display shape mismatch
assert not mergeable(
dummies.Op(sets=[dummies.Signal(base_shape=(2, 2), shape=(4, 1))]),
[dummies.Op(sets=[dummies.Signal(base_shape=(2, 2), shape=(1, 4))])])
# first dimension mismatch
assert mergeable(dummies.Op(sets=[dummies.Signal(shape=(3, 2))]),
[dummies.Op(sets=[dummies.Signal(shape=(4, 2))])])
# Copy (inc must match)
assert mergeable(Copy(dummies.Signal(), dummies.Signal(), inc=True),
[Copy(dummies.Signal(), dummies.Signal(), inc=True)])
assert not mergeable(Copy(dummies.Signal(), dummies.Signal(), inc=True),
[Copy(dummies.Signal(), dummies.Signal(), inc=False)])
# elementwise (first dimension must match)
assert mergeable(
ElementwiseInc(dummies.Signal(), dummies.Signal(), dummies.Signal()),
[ElementwiseInc(dummies.Signal(), dummies.Signal(), dummies.Signal())])
assert mergeable(
ElementwiseInc(dummies.Signal(shape=(1,)), dummies.Signal(), dummies.Signal()),
[ElementwiseInc(dummies.Signal(shape=()), dummies.Signal(), dummies.Signal())])
assert not mergeable(
ElementwiseInc(dummies.Signal(shape=(3,)), dummies.Signal(), dummies.Signal()),
[ElementwiseInc(dummies.Signal(shape=(2,)), dummies.Signal(),
dummies.Signal())])
# simpyfunc (t input must match)
time = dummies.Signal()
assert mergeable(SimPyFunc(None, None, time, None),
[SimPyFunc(None, None, time, None)])
assert mergeable(SimPyFunc(None, None, None, dummies.Signal()),
[SimPyFunc(None, None, None, dummies.Signal())])
assert not mergeable(SimPyFunc(None, None, dummies.Signal(), None),
[SimPyFunc(None, None, None, dummies.Signal())])
# simneurons
# check matching TF_NEURON_IMPL
assert mergeable(SimNeurons(LIF(), dummies.Signal(), dummies.Signal()),
[SimNeurons(LIF(), dummies.Signal(), dummies.Signal())])
assert not mergeable(SimNeurons(LIF(), dummies.Signal(), dummies.Signal()),
[SimNeurons(LIFRate(), dummies.Signal(), dummies.Signal())])
# check custom with non-custom implementation
assert not mergeable(SimNeurons(LIF(), dummies.Signal(), dummies.Signal()),
[SimNeurons(Izhikevich(), dummies.Signal(),
dummies.Signal())])
# check non-custom matching
assert not mergeable(
SimNeurons(Izhikevich(), dummies.Signal(), dummies.Signal()),
[SimNeurons(AdaptiveLIF(), dummies.Signal(), dummies.Signal())])
assert not mergeable(
SimNeurons(Izhikevich(), dummies.Signal(), dummies.Signal(),
states=[dummies.Signal(dtype=np.float32)]),
[SimNeurons(Izhikevich(), dummies.Signal(), dummies.Signal(),
states=[dummies.Signal(dtype=np.int32)])])
assert mergeable(
SimNeurons(Izhikevich(), dummies.Signal(), dummies.Signal(),
states=[dummies.Signal(shape=(3,))]),
[SimNeurons(Izhikevich(), dummies.Signal(), dummies.Signal(),
states=[dummies.Signal(shape=(2,))])])
assert not mergeable(
SimNeurons(Izhikevich(), dummies.Signal(), dummies.Signal(),
states=[dummies.Signal(shape=(2, 1))]),
[SimNeurons(Izhikevich(), dummies.Signal(), dummies.Signal(),
states=[dummies.Signal(shape=(2, 2))])])
# simprocess
# mode must match
assert not mergeable(
SimProcess(Lowpass(0), None, dummies.Signal(), dummies.Signal(),
mode="inc"),
[SimProcess(Lowpass(0), None, dummies.Signal(), dummies.Signal(),
mode="set")])
# check that lowpass match
assert mergeable(SimProcess(Lowpass(0), None, None, dummies.Signal()),
[SimProcess(Lowpass(0), None, None, dummies.Signal())])
# check that lowpass and linear don't match
assert not mergeable(SimProcess(Lowpass(0), None, None, dummies.Signal()),
[SimProcess(Alpha(0), None, None, dummies.Signal())])
# check that two linear do match
assert mergeable(
SimProcess(Alpha(0.1), dummies.Signal(), None, dummies.Signal()),
[SimProcess(LinearFilter([1], [1, 1, 1]), dummies.Signal(), None,
dummies.Signal())])
# check custom and non-custom don't match
assert not mergeable(SimProcess(Triangle(0), None, None, dummies.Signal()),
[SimProcess(Alpha(0), None, None, dummies.Signal())])
# check non-custom matching
assert mergeable(SimProcess(Triangle(0), None, None, dummies.Signal()),
[SimProcess(Triangle(0), None, None, dummies.Signal())])
# simtensornode
a = SimTensorNode(None, dummies.Signal(), None, dummies.Signal())
assert not mergeable(a, [a])
# learning rules
a = SimBCM(dummies.Signal((4,)), dummies.Signal(), dummies.Signal(), dummies.Signal(),
dummies.Signal())
b = SimBCM(dummies.Signal((5,)), dummies.Signal(), dummies.Signal(), dummies.Signal(),
dummies.Signal())
assert not mergeable(a, [b])
def test_mergeable():
# anything is mergeable with an empty list
assert mergeable(None, [])
# ops with different numbers of sets/incs/reads/updates are not mergeable
assert not mergeable(DummyOp(sets=[DummySignal()]), [DummyOp()])
assert not mergeable(DummyOp(incs=[DummySignal()]), [DummyOp()])
assert not mergeable(DummyOp(reads=[DummySignal()]), [DummyOp()])
assert not mergeable(DummyOp(updates=[DummySignal()]), [DummyOp()])
assert mergeable(DummyOp(sets=[DummySignal()]),
[DummyOp(sets=[DummySignal()])])
# check matching dtypes
assert not mergeable(DummyOp(sets=[DummySignal(dtype=np.float32)]),
[DummyOp(sets=[DummySignal(dtype=np.float64)])])
# shape mismatch
assert not mergeable(DummyOp(sets=[DummySignal(shape=(1, 2))]),
[DummyOp(sets=[DummySignal(shape=(1, 3))])])
# display shape mismatch
assert not mergeable(
DummyOp(sets=[DummySignal(base_shape=(2, 2), shape=(4, 1))]),
[DummyOp(sets=[DummySignal(base_shape=(2, 2), shape=(1, 4))])])
# first dimension mismatch
assert mergeable(DummyOp(sets=[DummySignal(shape=(3, 2))]),
[DummyOp(sets=[DummySignal(shape=(4, 2))])])
# Copy (inc must match)
assert mergeable(Copy(DummySignal(), DummySignal(), inc=True),
[Copy(DummySignal(), DummySignal(), inc=True)])
assert not mergeable(Copy(DummySignal(), DummySignal(), inc=True),
[Copy(DummySignal(), DummySignal(), inc=False)])
# elementwise (first dimension must match)
assert mergeable(
ElementwiseInc(DummySignal(), DummySignal(), DummySignal()),
[ElementwiseInc(DummySignal(), DummySignal(), DummySignal())])
assert mergeable(
ElementwiseInc(DummySignal(shape=(1,)), DummySignal(), DummySignal()),
[ElementwiseInc(DummySignal(shape=()), DummySignal(), DummySignal())])
assert not mergeable(
ElementwiseInc(DummySignal(shape=(3,)), DummySignal(), DummySignal()),
[ElementwiseInc(DummySignal(shape=(2,)), DummySignal(),
DummySignal())])
# simpyfunc (t input must match)
time = DummySignal()
assert mergeable(SimPyFunc(None, None, time, None),
[SimPyFunc(None, None, time, None)])
assert mergeable(SimPyFunc(None, None, None, DummySignal()),
[SimPyFunc(None, None, None, DummySignal())])
assert not mergeable(SimPyFunc(None, None, DummySignal(), None),
[SimPyFunc(None, None, None, DummySignal())])
# simneurons
# check matching TF_NEURON_IMPL
assert mergeable(SimNeurons(LIF(), DummySignal(), DummySignal()),
[SimNeurons(LIF(), DummySignal(), DummySignal())])
assert not mergeable(SimNeurons(LIF(), DummySignal(), DummySignal()),
[SimNeurons(LIFRate(), DummySignal(), DummySignal())])
# check custom with non-custom implementation
assert not mergeable(SimNeurons(LIF(), DummySignal(), DummySignal()),
[SimNeurons(Izhikevich(), DummySignal(),
DummySignal())])
# check non-custom matching
assert not mergeable(
SimNeurons(Izhikevich(), DummySignal(), DummySignal()),
[SimNeurons(AdaptiveLIF(), DummySignal(), DummySignal())])
assert not mergeable(
SimNeurons(Izhikevich(), DummySignal(), DummySignal(),
states=[DummySignal(dtype=np.float32)]),
[SimNeurons(Izhikevich(), DummySignal(), DummySignal(),
states=[DummySignal(dtype=np.int32)])])
assert mergeable(
SimNeurons(Izhikevich(), DummySignal(), DummySignal(),
states=[DummySignal(shape=(3,))]),
[SimNeurons(Izhikevich(), DummySignal(), DummySignal(),
states=[DummySignal(shape=(2,))])])
assert not mergeable(
SimNeurons(Izhikevich(), DummySignal(), DummySignal(),
states=[DummySignal(shape=(2, 1))]),
[SimNeurons(Izhikevich(), DummySignal(), DummySignal(),
states=[DummySignal(shape=(2, 2))])])
# simprocess
# mode must match
assert not mergeable(
SimProcess(Lowpass(0), None, None, DummySignal(), mode="inc"),
[SimProcess(Lowpass(0), None, None, DummySignal(), mode="set")])
# check matching TF_PROCESS_IMPL
# note: we only have one item in TF_PROCESS_IMPL at the moment, so no
# such thing as a mismatch
assert mergeable(SimProcess(Lowpass(0), None, None, DummySignal()),
[SimProcess(Lowpass(0), None, None, DummySignal())])
# check custom vs non custom
assert not mergeable(SimProcess(Lowpass(0), None, None, DummySignal()),
[SimProcess(Alpha(0), None, None, DummySignal())])
# check non-custom matching
assert mergeable(SimProcess(Triangle(0), None, None, DummySignal()),
[SimProcess(Alpha(0), None, None, DummySignal())])
# simtensornode
a = SimTensorNode(None, DummySignal(), None, DummySignal())
assert not mergeable(a, [a])
# learning rules
a = SimBCM(DummySignal((4,)), DummySignal(), DummySignal(), DummySignal(),
DummySignal())
b = SimBCM(DummySignal((5,)), DummySignal(), DummySignal(), DummySignal(),
DummySignal())
assert not mergeable(a, [b])
