def test_population_size(self):
populations = list()
populations.append(pyNN.Population(1, pyNN.IF_curr_exp, cell_params_lif,
label="One population"))
populations.append(
pyNN.Population(10, pyNN.IF_curr_exp, cell_params_lif,
label="Two population"))
self.assertEqual(populations[0]._size, 1)
self.assertEqual(populations[1]._size, 10)
def test_recording_numerious_element(self):
p.setup(timestep=1.0, min_delay=1.0, max_delay=144.0)
n_neurons = 20 # number of neurons in each population
p.set_number_of_neurons_per_core("IF_curr_exp", n_neurons / 2)
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()
boxed_array = numpy.zeros(shape=(0, 2))
spike_array = list()
for neuron_id in range(0, n_neurons):
spike_array.append(list())
for random_time in range(0, 20):
random_time2 = random.randint(0, 5000)
boxed_array = numpy.append(boxed_array,
[[neuron_id, random_time2]],
axis=0)
spike_array[neuron_id].append(random_time)
spike_array_params = {'spike_times': spike_array}
populations.append(
p.Population(n_neurons,
p.IF_curr_exp,
cell_params_lif,
label='pop_1'))
populations.append(
p.Population(n_neurons,
p.SpikeSourceArray,
spike_array_params,
label='inputSpikes_1'))
projections.append(
p.Projection(populations[1], populations[0],
p.OneToOneConnector()))
populations[1].record()
p.run(5000)
spike_array_spikes = populations[1].getSpikes()
boxed_array = boxed_array[numpy.lexsort(
(boxed_array[:, 1], boxed_array[:, 0]))]
numpy.testing.assert_array_equal(spike_array_spikes, boxed_array)
p.end()
def _create_network(self,
record_spikes=False,
record_v=False,
verbose=False):
print(
"INFO: Creating Cooperative Network of size {0} (in microensembles)."
.format(self.size))
# if record_spikes:
# from pyNN.spiNNaker import record
network = []
neural_params = self.cell_params['neural']
for x in range(0, self.size):
blocker_columns = ps.Population(
self.dim_y * 2,
ps.IF_curr_exp, {
'tau_syn_E': neural_params['tau_E'],
'tau_syn_I': neural_params['tau_I'],
'tau_m': neural_params['tau_mem'],
'v_reset': neural_params['v_reset_blocker']
},
label="Blocker {0}".format(x))
collector_column = ps.Population(
self.dim_y,
ps.IF_curr_exp, {
'tau_syn_E': neural_params['tau_E'],
'tau_syn_I': neural_params['tau_I'],
'tau_m': neural_params['tau_mem'],
'v_reset': neural_params['v_reset_collector']
},
label="Collector {0}".format(x))
if record_spikes:
collector_column.record() # records only the spikes
if record_v:
collector_column.record_v(
) # records the membrane potential -- very resource demanding!
blocker_columns.record_v()
network.append((blocker_columns, collector_column))
self._interconnect_neurons(network, verbose=verbose)
if self.dim_x > 1:
self._interconnect_neurons_inhexc(network, verbose)
else:
global _retina_proj_l, _retina_proj_r, same_disparity_indices
_retina_proj_l = [[0]]
_retina_proj_r = [[0]]
same_disparity_indices = [[0]]
return network
def test_connector_populations_of_different_sizes(self):
weight = 2
delay = 5
p1 = pyNN.Population(10,
pyNN.IF_curr_exp,
cell_params_lif,
label="pop 1")
p2 = pyNN.Population(5,
pyNN.IF_curr_exp,
cell_params_lif,
label="pop 2")
with self.assertRaises(ConfigurationException):
pyNN.Projection(p1, p2, pyNN.OneToOneConnector(weight, delay))
def test_synapse_list_generation_for_different_sized_populations(self):
number_of_neurons = 10
first_population = pyNN.Population(number_of_neurons, pyNN.IF_curr_exp,
cell_params_lif, label="One pop")
second_population = pyNN.Population(number_of_neurons + 5,
pyNN.IF_curr_exp, cell_params_lif,
label="Second pop")
weight = 2
delay = 1
connection = pyNN.FixedProbabilityConnector(0.1, weight, delay)
synaptic_list = connection.generate_synapse_list(
first_population, second_population, 1, 1.0, 0)
pp(synaptic_list.get_rows())
def test_projection_params(self):
populations = list()
projection_details = list()
populations = list()
weight_to_spike = 2
delay = 5
populations.append(
pyNN.Population(no_neurons,
pyNN.IF_curr_exp,
cell_params_lif,
label="LIF Pop"))
populations.append(
pyNN.Population(no_neurons,
pyNN.IF_curr_dual_exp,
cell_params_lif2exp,
label="IF_curr_dual_exp Pop"))
populations.append(
pyNN.Population(no_neurons,
pyNN.IF_cond_exp,
cell_params_lifexp,
label="IF_cond_exp Pop"))
populations.append(
pyNN.Population(no_neurons,
pyNN.IZK_curr_exp,
cell_params_izk,
label="IZK_curr_exp Pop"))
for i in range(4):
for j in range(4):
projection_details.append({
'presyn':
populations[i],
'postsyn':
populations[j],
'connector':
pyNN.OneToOneConnector(weight_to_spike, delay)
})
projections.append(
pyNN.Projection(
populations[i], populations[j],
pyNN.OneToOneConnector(weight_to_spike, delay)))
for i in range(4):
for j in range(4):
self.assertEqual(
projections[i + j]._projection_edge._pre_vertex,
projection_details[i + j]['presyn']._vertex)
self.assertEqual(
projections[i + j]._projection_edge._post_vertex,
projection_details[i + j]['postsyn']._vertex)
def test_one_to_one_connector_from_high_to_low(self):
weight_to_spike, delay = 2, 5
second_population = pyNN.Population(no_neurons,
pyNN.IF_curr_exp,
cell_params_lif,
label="LIF Pop")
different_population = pyNN.Population(
20,
pyNN.IF_curr_exp,
cell_params_lif,
label="A random sized population")
with self.assertRaises(exc.ConfigurationException):
pyNN.Projection(different_population, second_population,
pyNN.OneToOneConnector(weight_to_spike, delay))
def test_multiple_connections_between_same_populations(self):
p1 = pyNN.Population(no_neurons,
pyNN.IF_curr_exp,
cell_params_lif,
label="LIF Pop")
p2 = pyNN.Population(no_neurons,
pyNN.IF_curr_exp,
cell_params_lif,
label="LIF Pop")
pyNN.Projection(p1, p2, pyNN.OneToOneConnector(1, 1))
self.assertIsInstance(
pyNN.Projection(p1, p2, pyNN.OneToOneConnector(1, 1)), Projection,
"Failed to create multiple connections between"
" the same pair of populations")
def test_synapse_list_generation_for_different_sized_populations(self):
number_of_neurons = 10
first_population = pyNN.Population(number_of_neurons,
pyNN.IF_curr_exp,
cell_params_lif,
label="One pop")
second_population = pyNN.Population(number_of_neurons + 5,
pyNN.IF_curr_exp,
cell_params_lif,
label="Second pop")
weight = 2
delay = 1
connection = pyNN.OneToOneConnector(weight, delay)
with self.assertRaises(ConfigurationException):
connection.generate_synapse_list(first_population,
second_population, 1, 1.0, 0)
def test_a(self):
weight = 2
delay = 1
first_pop = pyNN.Population(5,
pyNN.IF_curr_exp,
cell_params_lif,
label="First normal pop")
second_pop = pyNN.Population(10,
pyNN.IF_curr_exp,
cell_params_lif,
label="Second normal pop")
pyNN.Projection(
first_pop, second_pop,
pyNN.MultapseConnector(num_synapses=5,
weights=weight,
delays=delay))
def test_spikes_per_second_not_set_in_a_pop(self):
pop = pyNN.Population(10,
pyNN.IF_curr_exp,
cell_params_lif,
label="Constrained population")
spikes_per_second = pop._get_vertex.spikes_per_second
self.assertEqual(spikes_per_second, 30)
def test_set_constraint_to_population(self):
pop = pyNN.Population(10, pyNN.IF_curr_exp, cell_params_lif,
label="Constrained population")
placer_constraint = pyNN.PlacerChipAndCoreConstraint(x=1, y=0)
pop.set_constraint(placer_constraint)
constraints = pop._get_vertex.constraints
self.assertIn(placer_constraint, constraints)
def test_ring_buffer_sigma_not_set_in_a_pop(self):
pop = pyNN.Population(10,
pyNN.IF_curr_exp,
cell_params_lif,
label="Constrained population")
ring_buffer_sigma = pop._get_vertex.ring_buffer_sigma
self.assertEqual(ring_buffer_sigma, 5.0)
def test_not_safe_and_verbose(self):
number_of_neurons = 5
first_population = pyNN.Population(number_of_neurons,
pyNN.IF_curr_exp,
cell_params_lif,
label="One pop")
weight = 2
delay = 1
connection_list = list()
for i in range(5):
for j in range(5):
connection_list.append((i, j, weight, delay))
synapse_type = 0
connection = pyNN.FromListConnector(connection_list,
safe=False,
verbose=True)
synaptic_list = connection.generate_synapse_list(
first_population, first_population, 1, 1.0, synapse_type)
self.assertEqual(synaptic_list.get_max_weight(), weight)
self.assertEqual(synaptic_list.get_min_weight(), weight)
pp(synaptic_list.get_rows())
self.assertEqual(synaptic_list.get_n_rows(), number_of_neurons)
self.assertEqual(synaptic_list.get_max_delay(), delay)
self.assertEqual(synaptic_list.get_min_delay(), delay)
def test_recording_1_element(self):
p.setup(timestep=1.0, min_delay=1.0, max_delay=144.0)
n_neurons = 200 # number of neurons in each population
p.set_number_of_neurons_per_core("IF_curr_exp", n_neurons / 2)
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()
spike_array = {'spike_times': [[0]]}
populations.append(
p.Population(n_neurons,
p.IF_curr_exp,
cell_params_lif,
label='pop_1'))
populations.append(
p.Population(1,
p.SpikeSourceArray,
spike_array,
label='inputSpikes_1'))
projections.append(
p.Projection(populations[1], populations[0],
p.AllToAllConnector()))
populations[1].record()
p.run(5000)
spike_array_spikes = populations[1].getSpikes()
boxed_array = numpy.zeros(shape=(0, 2))
boxed_array = numpy.append(boxed_array, [[0, 0]], axis=0)
numpy.testing.assert_array_equal(spike_array_spikes, boxed_array)
p.end()
def test_synapse_list_generation_for_different_sized_populations(self):
number_of_neurons = 10
first_population = pyNN.Population(number_of_neurons, pyNN.IF_curr_exp,
cell_params_lif, label="One pop")
second_population = pyNN.Population(number_of_neurons + 5,
pyNN.IF_curr_exp, cell_params_lif,
label="Second pop")
weight = 2
delay = 1
connection = pyNN.AllToAllConnector(weight, delay)
synaptic_list = connection.generate_synapse_list(
first_population, second_population, 1, 1.0, 0)
self.assertEqual(synaptic_list.get_max_weight(), weight)
self.assertEqual(synaptic_list.get_min_weight(), weight)
self.assertEqual(synaptic_list.get_n_rows(), number_of_neurons)
self.assertEqual(synaptic_list.get_max_delay(), delay)
self.assertEqual(synaptic_list.get_min_delay(), delay)
def test_t_set_invalid(self):
pop = pyNN.Population(10,
pyNN.IF_curr_exp,
cell_params_lif,
label="Constrained population")
data = [0, 1, 2, 3, 4, 5, 6, 7]
with self.assertRaises(ConfigurationException):
pop.tset("cm", data)
def test_synapse_list_generation_for_simulated_one_to_one_smaller_to_larger(
self):
number_of_neurons = 10
first_population = pyNN.Population(number_of_neurons,
pyNN.IF_curr_exp,
cell_params_lif, label="One pop")
second_population = pyNN.Population(number_of_neurons + 5,
pyNN.IF_curr_exp,
cell_params_lif,
label="Second pop")
weight = 2
delay = 1
connection_list = list()
for i in range(number_of_neurons + 5):
connection_list.append((i, i, weight, delay))
connection = pyNN.FromListConnector(connection_list)
with self.assertRaises(ConfigurationException):
connection.generate_synapse_list(
second_population, first_population, 1, 1.0, 0)
def test_recording_poisson_spikes_rate_0(self):
p.setup(timestep=1.0, min_delay=1.0, max_delay=144.0)
n_neurons = 256 # number of neurons in each population
p.set_number_of_neurons_per_core("IF_curr_exp", n_neurons / 2)
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()
populations.append(
p.Population(n_neurons,
p.IF_curr_exp,
cell_params_lif,
label='pop_1'))
populations.append(
p.Population(n_neurons,
p.SpikeSourcePoisson, {'rate': 0},
label='inputSpikes_1'))
projections.append(
p.Projection(populations[1], populations[0],
p.OneToOneConnector()))
populations[1].record()
p.run(5000)
spikes = populations[1].getSpikes()
print spikes
p.end()
def test_t_set(self):
pop = pyNN.Population(10,
pyNN.IF_curr_exp,
cell_params_lif,
label="Constrained population")
data = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
pop.tset("cm", data)
cm = pop.get("cm")
for index in range(0, len(data)):
self.assertEqual(cm[index], data[index])
def build_network(self, dynamics, cell_params):
"""
Function to build the basic network - dynamics should be a PyNN
synapse dynamics object
:param dynamics:
:return:
"""
# SpiNNaker setup
model = sim.IF_curr_exp
sim.setup(timestep=1.0, min_delay=1.0, max_delay=10.0)
# Create excitatory and inhibitory populations of neurons
ex_pop = sim.Population(NUM_EXCITATORY, model, cell_params)
in_pop = sim.Population(NUM_EXCITATORY / 4, model, cell_params)
# Record excitatory spikes
ex_pop.record()
# Make excitatory->inhibitory projections
sim.Projection(ex_pop,
in_pop,
sim.FixedProbabilityConnector(0.02, weights=0.03),
target='excitatory')
sim.Projection(ex_pop,
ex_pop,
sim.FixedProbabilityConnector(0.02, weights=0.03),
target='excitatory')
# Make inhibitory->inhibitory projections
sim.Projection(in_pop,
in_pop,
sim.FixedProbabilityConnector(0.02, weights=-0.3),
target='inhibitory')
# Make inhibitory->excitatory projections
ie_projection = sim.Projection(in_pop,
ex_pop,
sim.FixedProbabilityConnector(
0.02, weights=0),
target='inhibitory',
synapse_dynamics=dynamics)
return ex_pop, ie_projection
def test_generate_synapse_list_pre_1(self):
number_of_neurons = 5
first_population = pyNN.Population(number_of_neurons, pyNN.IF_curr_exp,
cell_params_lif, label="One pop")
weight = 2
delay = 1
synapse_type = first_population._vertex.get_synapse_id('excitatory')
connection = pyNN.FixedNumberPreConnector(1, weight, delay)
synaptic_list = connection.generate_synapse_list(
first_population, first_population, 1, 1.0, synapse_type)
pp(synaptic_list.get_rows())
def test_generate_synapse_list_probability_zero_percent(self):
number_of_neurons = 5
first_population = pyNN.Population(number_of_neurons, pyNN.IF_curr_exp,
cell_params_lif, label="One pop")
weight = 2
delay = 1
synapse_type = 0
connection = pyNN.FixedProbabilityConnector(0, weight, delay)
synaptic_list = connection.generate_synapse_list(
first_population, first_population, 1, 1.0, synapse_type)
pp(synaptic_list.get_rows())
def test_inhibitory_connector(self):
weight_to_spike = 2
delay = 5
p1 = pyNN.Population(no_neurons,
pyNN.IF_curr_exp,
cell_params_lif,
label="LIF Pop")
p2 = pyNN.Population(no_neurons,
pyNN.IF_curr_exp,
cell_params_lif,
label="LIF Pop")
s12_2 = pyNN.Projection(p1,
p2,
pyNN.OneToOneConnector(weight_to_spike, delay),
target='inhibitory')
s21 = pyNN.Projection(p2,
p1,
pyNN.OneToOneConnector(weight_to_spike, delay),
target='excitatory')
def simulate(self, spinnaker, input_spike_times):
# Cell parameters
cell_params = {
'tau_m': 20.0,
'v_rest': -60.0,
'v_reset': -60.0,
'v_thresh': -40.0,
'tau_syn_E': 2.0,
'tau_syn_I': 2.0,
'tau_refrac': 2.0,
'cm': 0.25,
'i_offset': 0.0,
}
rng = p.NumpyRNG(seed=28375)
v_init = p.RandomDistribution('uniform', [-60, -40], rng)
p.setup(timestep=1.0, min_delay=1.0, max_delay=10.0)
pop = p.Population(1, p.IF_curr_exp, cell_params, label='population')
pop.randomInit(v_init)
pop.record()
pop.record_v()
noise = p.Population(1, p.SpikeSourceArray,
{"spike_times": input_spike_times})
p.Projection(noise,
pop,
p.OneToOneConnector(weights=0.4, delays=1),
target='excitatory')
# Simulate
p.run(self.simtime)
pop_voltages = pop.get_v(compatible_output=True)
pop_spikes = pop.getSpikes(compatible_output=True)
p.end()
return pop_voltages, pop_spikes
def test_synapse_list_generation_simulated_one_to_one_larger_to_smaller(
self):
number_of_neurons = 10
first_population = pyNN.Population(number_of_neurons, pyNN.IF_curr_exp,
cell_params_lif, label="One pop")
second_population = pyNN.Population(number_of_neurons + 5,
pyNN.IF_curr_exp, cell_params_lif,
label="Second pop")
weight = 2
delay = 1
connection_list = list()
for i in range(number_of_neurons):
connection_list.append((i, i, weight, delay))
connection = pyNN.FromListConnector(connection_list)
synaptic_list = connection.generate_synapse_list(
first_population, second_population, 1, 1.0, 0)
self.assertEqual(synaptic_list.get_max_weight(), weight)
self.assertEqual(synaptic_list.get_min_weight(), weight)
self.assertEqual(synaptic_list.get_n_rows(), number_of_neurons)
self.assertEqual(synaptic_list.get_max_delay(), delay)
self.assertEqual(synaptic_list.get_min_delay(), delay)
def test_generate_synapse_list_pre_negative(self):
number_of_neurons = 5
first_population = pyNN.Population(number_of_neurons,
pyNN.IF_curr_exp,
cell_params_lif, label="One pop")
weight = 2
delay = 1
synapse_type = first_population._vertex.get_synapse_id('excitatory')
connection = pyNN.FixedNumberPreConnector(-1, weight, delay)
with self.assertRaises(ConfigurationException):
connection.generate_synapse_list(
first_population, first_population, 1, 1.0, synapse_type)
def test_allow_self_connections(self):
number_of_neurons = 5
first_population = pyNN.Population(number_of_neurons, pyNN.IF_curr_exp,
cell_params_lif, label="One pop")
weight = 2
delay = 1
synapse_type = 0
connection = pyNN.FixedNumberPreConnector(5, weight, delay,
allow_self_connections=False)
synaptic_list = connection.generate_synapse_list(
first_population, first_population, 1, 1.0, synapse_type)
pp(synaptic_list.get_rows())
def test_get_default_parameters_of_if_curr_exp(self):
pop = pyNN.Population(10,
pyNN.IF_curr_exp,
cell_params_lif,
label="Constrained population")
default_params = pop.default_parameters
boxed_defaults = \
{'tau_m': 20.0, 'cm': 1.0, 'v_rest': -65.0, 'v_reset': -65.0,
'v_thresh': -50.0, 'tau_syn_E': 5.0, 'tau_syn_I': 5.0,
'tau_refrac': 0.1, 'i_offset': 0, 'v_init': -65.0}
for param in default_params.keys():
self.assertEqual(default_params[param], boxed_defaults[param])
def test_connect_two_different_populations(self):
number_of_neurons = 10
first_population = pyNN.Population(number_of_neurons,
pyNN.IF_curr_exp,
cell_params_lif,
label="One pop")
second_population = pyNN.Population(number_of_neurons,
pyNN.IF_curr_exp,
cell_params_lif,
label="Second pop")
weight = 2
delay = 1
synapse_type = 0
connection = pyNN.OneToOneConnector(weight, delay)
synaptic_list = connection.generate_synapse_list(
first_population, second_population, 1, 1.0, synapse_type)
self.assertEqual(synaptic_list.get_max_weight(), weight)
self.assertEqual(synaptic_list.get_min_weight(), weight)
pp(synaptic_list.get_rows())
self.assertEqual(synaptic_list.get_n_rows(), number_of_neurons)
self.assertEqual(synaptic_list.get_max_delay(), delay)
self.assertEqual(synaptic_list.get_min_delay(), delay)