def test_construction_single_synapses():
'''
Test the construction of synapses with a single synapse per connection.
'''
G = NeuronGroup(20, model='v:1', threshold=NoThreshold())
# specifying only one group should use it as source and target
syn = Synapses(G, model='w:1')
assert syn.source is syn.target
# specifying source and target with subgroups
subgroup1, subgroup2 = G[:10], G[10:]
syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w')
assert syn.source is subgroup1
assert syn.target is subgroup2
# create all-to-all connections
syn[:, :] = True
assert len(syn) == 10 * 10
# deleting a synapse should not work
def delete_synapse():
syn[0, 0] = False
assert_raises(ValueError, delete_synapse)
# set the weights
syn.w[:, :] = 2
# set the delays
syn.delay[:, :] = 1 * ms
all_weights = np.array([
syn.w[i, j] for i in xrange(len(subgroup1))
for j in xrange(len(subgroup2))
])
assert (all_weights == 2).all()
all_delays = np.array([
syn.delay[i, j] for i in xrange(len(subgroup1))
for j in xrange(len(subgroup2))
])
assert (all_delays == 1 * ms).all()
# create one-to-one connections
syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w')
syn[:, :] = 'i == j'
syn.w[:, :] = 2
assert len(syn) == len(subgroup1)
for i in xrange(len(subgroup1)):
for j in xrange(len(subgroup2)):
if i == j:
assert syn.w[i, j] == 2
else:
assert len(syn.w[i, j]) == 0
net = Network(G, syn)
net.run(defaultclock.dt)
# adding synapses should not work after the simulation has already been run
def adding_a_synapse():
syn[0, 1] = True
assert_raises(AttributeError, adding_a_synapse)
syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w')
syn.connect_one_to_one(subgroup1, subgroup2)
syn.w[:, :] = 2
assert len(syn) == len(subgroup1)
for i in xrange(len(subgroup1)):
for j in xrange(len(subgroup2)):
if i == j:
assert syn.w[i, j] == 2
else:
assert len(syn.w[i, j]) == 0
# Calling connect_one_to_one without specifying groups should use the full
# source or target group
syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w')
syn.connect_one_to_one(subgroup1)
assert len(syn) == len(subgroup1)
syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w')
syn.connect_one_to_one(post=subgroup2)
assert len(syn) == len(subgroup1)
syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w')
syn.connect_one_to_one()
assert len(syn) == len(subgroup1)
# connect_one_to_one should only work with subgroups of the same size
assert_raises(TypeError, lambda: syn.connect_one_to_one(G[:2], G[:1]))
# create random connections
# the only two cases that can be tested exactly are 0 and 100% connection
# probability
syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w')
# floats are interpreted as connection probabilities
syn[:, :] = 0.0
assert len(syn) == 0
syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w')
syn.connect_random(subgroup1, subgroup2, 0.0)
assert len(syn) == 0
syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w')
syn[:, :] = 1.0
assert len(syn) == 10 * 10
# set the weights
syn.w[:, :] = 2
# set the delays
syn.delay[:, :] = 1 * ms
all_weights = np.array([
syn.w[i, j] for i in xrange(len(subgroup1))
for j in xrange(len(subgroup2))
])
assert (all_weights == 2).all()
all_delays = np.array([
syn.delay[i, j] for i in xrange(len(subgroup1))
for j in xrange(len(subgroup2))
])
assert (all_delays == 1 * ms).all()
syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w')
syn.connect_random(subgroup1, subgroup2, 1.0)
assert len(syn) == 10 * 10
# set the weights
syn.w[:, :] = 2
# set the delays
syn.delay[:, :] = 1 * ms
all_weights = np.array([
syn.w[i, j] for i in xrange(len(subgroup1))
for j in xrange(len(subgroup2))
])
assert (all_weights == 2).all()
all_delays = np.array([
syn.delay[i, j] for i in xrange(len(subgroup1))
for j in xrange(len(subgroup2))
])
assert (all_delays == 1 * ms).all()
# Calling connect_random without specifying groups should use the full
# source or target group
syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w')
syn.connect_random(subgroup1, sparseness=1.0)
assert len(syn) == 10 * 10
syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w')
syn.connect_random(post=subgroup2, sparseness=1.0)
assert len(syn) == 10 * 10
syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w')
syn.connect_random(sparseness=1.0)
assert len(syn) == 10 * 10
# Just test that probabilities between zero and one work at all
syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w')
syn.connect_random(subgroup1, subgroup2, 0.3)
syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w')
syn[:, :] = 0.3
# Test that probabilities outside of the legal range raise an error
syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w')
assert_raises(ValueError,
lambda: syn.connect_random(subgroup1, subgroup2, 1.3))
assert_raises(ValueError,
lambda: syn.connect_random(subgroup1, subgroup2, -.3))
def wrong_probability():
syn[:, :] = -0.3
assert_raises(ValueError, wrong_probability)
def wrong_probability(): #@DuplicatedSignature
syn[:, :] = 1.3
assert_raises(ValueError, wrong_probability)
# this test requires python 2.6
if sys.version_info[0] >= 2 and sys.version_info[1] >= 6:
# Running a model with a synapses object with 0 synapses should raise a warning
syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w')
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
# Trigger a warning.
syn.compress()
# Verify some things
assert len(w) == 1
# use arrays as neuron indices instead of slices or ints
syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w')
syn[np.arange(5), np.arange(5)] = True
syn.w[np.arange(5), np.arange(5)] = 2
syn.delay[np.arange(5), np.arange(5)] = 5 * ms
assert len(syn) == 5 * 5
assert all([syn.w[i, j] == 2 for i in xrange(5) for j in xrange(5)])
assert all(
[syn.delay[i, j] == 5 * ms for i in xrange(5) for j in xrange(5)])
assert all([
len(syn.w[i, j]) == 0 for i in xrange(5, len(subgroup1))
for j in xrange(5, len(subgroup2))
])
assert all([
len(syn.delay[i, j]) == 0 for i in xrange(5, len(subgroup1))
for j in xrange(5, len(subgroup2))
])
# Create a synapse without a model (e.g. a simple connection with constant
# weights
syn = Synapses(subgroup1, subgroup2, model='', pre='v+=1')
