def test_global_inhibition_sink(self):
"""Test that sinks are correctly determined for connections which are
global inhibition connections.
"""
# Create a model with a global inhibition connection
with nengo.Network():
a = nengo.Ensemble(100, 2)
b = nengo.Ensemble(200, 4)
a_b = nengo.Connection(a, b.neurons, transform=[[1.0, 0.5]]*200)
# Create a model with the Ensemble for b in it
model = builder.Model()
b_ens = operators.EnsembleLIF(b)
model.object_operators[b] = b_ens
decs = mock.Mock()
evals = mock.Mock()
si = mock.Mock()
model.params[a_b] = builder.BuiltConnection(decs, evals, a_b.transform,
si)
# Get the sink, check that an appropriate target is return
sink = ensemble.get_neurons_sink(model, a_b)
assert sink.target.obj is b_ens
assert sink.target.port is ensemble.EnsembleInputPort.global_inhibition
assert model.params[a_b].decoders is decs
assert model.params[a_b].eval_points is evals
assert model.params[a_b].solver_info is si
assert np.all(model.params[a_b].transform == np.array([[1.0, 0.5]]))
assert model.params[a_b].transform.shape == (1, 2)
def test_arbitrary_neuron_sink(self, source):
"""Test that standard connections to neurons return an appropriate
sink.
We have no plan to support arbitrary connections to neurons, but we
allow them at this stage because they may later become global
inhibition connections when we optimise out passthrough Nodes.
"""
with nengo.Network():
a = nengo.Ensemble(100, 2)
b = nengo.Ensemble(100, 4)
if source == "neurons":
a_b = nengo.Connection(a.neurons,
b.neurons,
transform=np.eye(100))
else:
a_b = nengo.Connection(a,
b.neurons,
transform=[[1.0, 0.5]] * 99 +
[[0.5, 1.0]])
# Create a model with the Ensemble for b in it
model = builder.Model()
b_ens = operators.EnsembleLIF(b)
model.object_operators[b] = b_ens
# Get the sink, check that an appropriate target is return
sink = ensemble.get_neurons_sink(model, a_b)
assert sink.target.obj is b_ens
assert sink.target.port is ensemble.EnsembleInputPort.neurons
def test_arbitrary_neuron_sink(self, source):
"""Test that standard connections to neurons return an appropriate
sink.
We have no plan to support arbitrary connections to neurons, but we
allow them at this stage because they may later become global
inhibition connections when we optimise out passthrough Nodes.
"""
with nengo.Network():
a = nengo.Ensemble(100, 2)
b = nengo.Ensemble(100, 4)
if source == "neurons":
a_b = nengo.Connection(a.neurons, b.neurons,
transform=np.eye(100))
else:
a_b = nengo.Connection(a, b.neurons,
transform=[[1.0, 0.5]]*99 +
[[0.5, 1.0]])
# Create a model with the Ensemble for b in it
model = builder.Model()
b_ens = operators.EnsembleLIF(b)
model.object_operators[b] = b_ens
# Get the sink, check that an appropriate target is return
sink = ensemble.get_neurons_sink(model, a_b)
assert sink.target.obj is b_ens
assert sink.target.port is ensemble.EnsembleInputPort.neurons
def test_arbitrary_neuron_sink(self):
"""We have no plan to support arbitrary connections to neurons."""
with nengo.Network():
a = nengo.Ensemble(100, 2)
b = nengo.Ensemble(200, 4)
a_b = nengo.Connection(a, b.neurons,
transform=[[1.0, 0.5]]*199 + [[0.5, 1.0]])
# Create a model with the Ensemble for b in it
model = builder.Model()
b_ens = operators.EnsembleLIF(b)
model.object_operators[b] = b_ens
# This should fail
with pytest.raises(NotImplementedError):
ensemble.get_neurons_sink(model, a_b)
def test_neuron_sink(self):
"""Test that standard connections to neurons return an appropriate
sink.
"""
with nengo.Network():
a = nengo.Ensemble(100, 2)
b = nengo.Ensemble(100, 4)
a_b = nengo.Connection(a.neurons, b.neurons, transform=np.eye(100))
# Create a model with the Ensemble for b in it
model = builder.Model()
b_ens = operators.EnsembleLIF(b)
model.object_operators[b] = b_ens
# Get the sink, check that an appropriate target is return
sink = ensemble.get_neurons_sink(model, a_b)
assert sink.target.obj is b_ens
assert sink.target.port is ensemble.EnsembleInputPort.neurons
def test_global_inhibition_sink(self):
"""Test that sinks are correctly determined for connections which are
global inhibition connections.
"""
# Create a model with a global inhibition connection
with nengo.Network():
a = nengo.Ensemble(100, 2)
b = nengo.Ensemble(200, 4)
a_b = nengo.Connection(a, b.neurons, transform=[[1.0, 0.5]] * 200)
# Create a model with the Ensemble for b in it
model = builder.Model()
b_ens = operators.EnsembleLIF(b)
model.object_operators[b] = b_ens
decs = mock.Mock()
evals = mock.Mock()
si = mock.Mock()
# Get the sink, check that an appropriate target is return
sink = ensemble.get_neurons_sink(model, a_b)
assert sink.target.obj is b_ens
assert sink.target.port is ensemble.EnsembleInputPort.global_inhibition
def test_global_inhibition_sink(self):
"""Test that sinks are correctly determined for connections which are
global inhibition connections.
"""
# Create a model with a global inhibition connection
with nengo.Network():
a = nengo.Ensemble(100, 2)
b = nengo.Ensemble(200, 4)
a_b = nengo.Connection(a, b.neurons, transform=[[1.0, 0.5]]*200)
# Create a model with the Ensemble for b in it
model = builder.Model()
b_ens = operators.EnsembleLIF(b)
model.object_operators[b] = b_ens
decs = mock.Mock()
evals = mock.Mock()
si = mock.Mock()
# Get the sink, check that an appropriate target is return
sink = ensemble.get_neurons_sink(model, a_b)
assert sink.target.obj is b_ens
assert sink.target.port is ensemble.EnsembleInputPort.global_inhibition