def test_csa_block_connector():
MockSimulator.setup()
try:
# This creates a block of size (2, 5) with a probability of 0.5; then
# within the block an individual connection has a probability of 0.3
connector = CSAConnector(
csa.block(2, 5) * csa.random(0.5) * csa.random(0.3))
weight = 1.0
delay = 2.0
mock_synapse_info = MockSynapseInfo(MockPopulation(10, "pre"),
MockPopulation(10, "post"), weight,
delay)
connector.set_projection_information(1000.0, mock_synapse_info)
pre_vertex_slice = Slice(0, 10)
post_vertex_slice = Slice(0, 10)
block = connector.create_synaptic_block([pre_vertex_slice], 0,
[post_vertex_slice], 0,
pre_vertex_slice,
post_vertex_slice, 0,
mock_synapse_info)
assert (len(block) >= 0)
assert (all(item["weight"] == 1.0 for item in block))
assert (all(item["delay"] == 2.0 for item in block))
except TypeError:
raise SkipTest("https://github.com/INCF/csa/issues/17")
except RuntimeError:
if sys.version_info >= (3, 7):
raise SkipTest("https://github.com/INCF/csa/issues/16")
raise
def test_csa_block_connector():
MockSimulator.setup()
# This creates a block of size (2, 5) with a probability of 0.5; then
# within the block an individual connection has a probability of 0.3
connector = CSAConnector(
csa.block(2, 5) * csa.random(0.5) * csa.random(0.3))
connector.set_projection_information(
MockPopulation(10, "pre"), MockPopulation(10, "post"),
MockRNG(), 1000.0)
pre_vertex_slice = Slice(0, 10)
post_vertex_slice = Slice(0, 10)
block = connector.create_synaptic_block(
1.0, 2.0, [pre_vertex_slice], 0, [post_vertex_slice], 0,
pre_vertex_slice, post_vertex_slice, 0)
assert(len(block) >= 0)
assert(all(item["weight"] == 1.0 for item in block))
assert(all(item["delay"] == 2.0 for item in block))
def test_csa_block_connector():
unittest_setup()
try:
# This creates a block of size (2, 5) with a probability of 0.5; then
# within the block an individual connection has a probability of 0.3
connector = CSAConnector(
csa.block(2, 5) * csa.random(0.5) * csa.random(0.3))
weight = 1.0
delay = 2.0
mock_synapse_info = SynapseInformation(
connector=None,
pre_population=MockPopulation(10, "Pre"),
post_population=MockPopulation(10, "Post"),
prepop_is_view=False,
postpop_is_view=False,
rng=None,
synapse_dynamics=None,
synapse_type=None,
receptor_type=None,
is_virtual_machine=False,
synapse_type_from_dynamics=False,
weights=weight,
delays=delay)
connector.set_projection_information(mock_synapse_info)
pre_vertex_slice = Slice(0, 10)
post_vertex_slice = Slice(0, 10)
block = connector.create_synaptic_block([pre_vertex_slice], 0,
[post_vertex_slice], 0,
pre_vertex_slice,
post_vertex_slice, 0,
mock_synapse_info)
assert (len(block) >= 0)
assert (all(item["weight"] == 1.0 for item in block))
assert (all(item["delay"] == 2.0 for item in block))
except TypeError as e:
raise SkipTest("https://github.com/INCF/csa/issues/17") from e
except RuntimeError as e:
if sys.version_info >= (3, 7):
raise SkipTest("https://github.com/INCF/csa/issues/16") from e
raise e
def do_run(plot):
runtime = 3000
p.setup(timestep=1.0, min_delay=1.0, max_delay=144.0)
nNeurons = 200 # number of neurons in each population
p.set_number_of_neurons_per_core(p.IF_curr_exp, nNeurons / 10)
cell_params_lif = {
'cm': 0.25,
'i_offset': 0.0,
'tau_m': 20.0,
'tau_refrac': 2.0,
'tau_syn_E': 5.0,
'tau_syn_I': 5.0,
'v_reset': -70.0,
'v_rest': -65.0,
'v_thresh': -50.0
}
populations = list()
projections = list()
weight_to_spike = 2.0
delay = 10
# connection list in a loop for first population
loopConnections = []
for i in range(0, nNeurons):
singleConnection = ((i, (i + 1) % nNeurons))
loopConnections.append(singleConnection)
# injection list to set the chain going
injectionConnection = [(0, 0)]
spikeArray = {'spike_times': [[0]]}
# list of populations
populations.append(
p.Population(nNeurons, p.IF_curr_exp(**cell_params_lif),
label='pop_1'))
populations.append(
p.Population(nNeurons, p.IF_curr_exp(**cell_params_lif),
label='pop_2'))
populations.append(
p.Population(nNeurons, p.IF_curr_exp(**cell_params_lif),
label='pop_3'))
populations.append(
p.Population(nNeurons, p.IF_curr_exp(**cell_params_lif),
label='pop_4'))
populations.append(
p.Population(1,
p.SpikeSourceArray(**spikeArray),
label='inputSpikes_1'))
# Loop connector: we can just pass in the list we made earlier
CSA_loop_connector = p.CSAConnector(loopConnections)
# random connector: each connection has a probability of 0.05
CSA_random_connector = p.CSAConnector(csa.random(0.05))
# one-to-one connector: do I really need to explain?
CSA_onetoone_connector = p.CSAConnector(csa.oneToOne)
# This creates a block of size (5,10) with a probability of 0.05; then
# within the block an individual connection has a probability of 0.3
csa_block_random = csa.block(15, 10) * csa.random(0.05) * csa.random(0.3)
CSA_randomblock_connector = p.CSAConnector(csa_block_random)
# list of projections using the connectors above
projections.append(
p.Projection(populations[0], populations[0], CSA_loop_connector,
p.StaticSynapse(weight=weight_to_spike, delay=delay)))
projections.append(
p.Projection(populations[0], populations[1], CSA_random_connector,
p.StaticSynapse(weight=weight_to_spike, delay=delay)))
projections.append(
p.Projection(populations[1], populations[2], CSA_onetoone_connector,
p.StaticSynapse(weight=weight_to_spike, delay=delay)))
projections.append(
p.Projection(populations[2], populations[3], CSA_randomblock_connector,
p.StaticSynapse(weight=weight_to_spike, delay=delay)))
projections.append(
p.Projection(populations[4], populations[0],
p.FromListConnector(injectionConnection),
p.StaticSynapse(weight=weight_to_spike, delay=1)))
populations[0].record(['v', 'spikes'])
populations[1].record(['v', 'spikes'])
populations[2].record(['v', 'spikes'])
populations[3].record(['v', 'spikes'])
p.run(runtime)
# get data (could be done as one, but can be done bit by bit as well)
v = populations[0].spinnaker_get_data('v')
v2 = populations[1].spinnaker_get_data('v')
v3 = populations[2].spinnaker_get_data('v')
v4 = populations[3].spinnaker_get_data('v')
spikes = populations[0].spinnaker_get_data('spikes')
spikes2 = populations[1].spinnaker_get_data('spikes')
spikes3 = populations[2].spinnaker_get_data('spikes')
spikes4 = populations[3].spinnaker_get_data('spikes')
if plot:
# Use the show functionality of CSA to display connection sets
CSA_loop_connector.show_connection_set()
CSA_random_connector.show_connection_set()
CSA_onetoone_connector.show_connection_set()
CSA_randomblock_connector.show_connection_set()
# Now plot some spikes
pylab.figure()
pylab.plot([i[1] for i in spikes], [i[0] for i in spikes], "r.")
pylab.xlabel('Time/ms')
pylab.ylabel('spikes')
pylab.title('spikes: population 1')
pylab.show()
pylab.figure()
pylab.plot([i[1] for i in spikes3], [i[0] for i in spikes3], "g.")
pylab.plot([i[1] for i in spikes4], [i[0] for i in spikes4], "r.")
pylab.plot([i[1] for i in spikes2], [i[0] for i in spikes2], "b.")
pylab.xlabel('Time/ms')
pylab.ylabel('spikes')
pylab.title('spikes: populations 2, 3, 4')
pylab.show()
p.end()
return v, v2, v3, v4, spikes, spikes2, spikes3, spikes4