def test_issue2051(self):
p1 = pyhmf.Population(1000, pyhmf.EIF_cond_exp_isfa_ista)
p2 = pyhmf.Population(1000, pyhmf.EIF_cond_exp_isfa_ista)
if self.API_VERSION == "0.8":
prj = pyhmf.Projection(p1, p2, pyhmf.AllToAllConnector(),
pyhmf.StaticSynapse(weight=0.05, delay=0.5))
params = {
'U': 0.04,
'tau_rec': 100.0,
'tau_facil': 1000.0,
'weight': 0.01
}
facilitating = pyhmf.TsodyksMarkramSynapse(**params)
prj = pyhmf.Projection(
p1,
p2,
pyhmf.FixedProbabilityConnector(p_connect=0.1),
synapse_type=facilitating)
elif self.API_VERSION == "0.7":
prj = pyhmf.Projection(
p1, p2, pyhmf.AllToAllConnector(weights=0.05, delays=0.5))
params = {'U': 0.04, 'tau_rec': 100.0, 'tau_facil': 1000.0}
facilitating = pyhmf.SynapseDynamics(
fast=pyhmf.TsodyksMarkramMechanism(**params))
prj = pyhmf.Projection(p1,
p2,
pyhmf.FixedProbabilityConnector(
p_connect=0.1, weights=0.01),
synapse_dynamics=facilitating)
def big_network(self):
pynn.setup(marocco=self.marocco)
synapses = 0
numberOfPopulations = random.randint(100, 150)
logging.debug("number of populations: %d" % (numberOfPopulations))
#print "numPops: %d" % (numberOfPopulations)
# FIXME: spuouriously fails, why?
#pops = [ Population(random.randint(50, 85), EIF_cond_exp_isfa_ista) for x in
#range(numberOfPopulations) ]
pops = [
pynn.Population(80, pynn.EIF_cond_exp_isfa_ista)
for x in range(numberOfPopulations)
]
for idx, pop in enumerate(pops):
connectorE = pynn.FixedProbabilityConnector(
p_connect=0.20, allow_self_connections=True, weights=1.)
connectorI = pynn.FixedProbabilityConnector(
p_connect=0.10, allow_self_connections=True, weights=1.)
# build ring like network topology
proj = pynn.Projection(pop,
pops[(idx + 1) % len(pops)],
connectorE,
target='excitatory')
synapses += np.count_nonzero(proj.getWeights())
proj = pynn.Projection(pop,
pops[(idx + 1) % len(pops)],
connectorI,
target='inhibitory')
synapses += np.count_nonzero(proj.getWeights())
# add poisson stimulus
source = pynn.Population(1, pynn.SpikeSourcePoisson, {'rate': 2})
proj = pynn.Projection(source,
pop,
connectorE,
target='excitatory')
synapses += np.count_nonzero(proj.getWeights())
pynn.run(100)
pynn.end()
logging.debug("synapses counted in python: %d", synapses)
return synapses
def test(self):
import pyhmf as pynn
from pymarocco import PyMarocco
import pylogging, pyhalbe
pyhalbe.Debug.change_loglevel(2)
pylogging.set_loglevel(pylogging.get("marocco"),
pylogging.LogLevel.TRACE)
pylogging.set_loglevel(pylogging.get("sthal"),
pylogging.LogLevel.DEBUG)
marocco = PyMarocco()
marocco.neuron_placement.default_neuron_size(4)
pynn.setup(marocco=marocco)
neuron1 = pynn.Population(1, pynn.IF_cond_exp)
inh = pynn.Population(1, pynn.SpikeSourceArray, {'spike_times': [0]})
exc = pynn.Population(1, pynn.SpikeSourceArray, {'spike_times': [0]})
exc_2 = pynn.Population(1, pynn.SpikeSourceArray, {'spike_times': [0]})
exc_3 = pynn.Population(1, pynn.SpikeSourceArray, {'spike_times': [0]})
c_exc = pynn.FixedProbabilityConnector(p_connect=1.0, weights=1)
proj1 = pynn.Projection(inh, neuron1, c_exc, target='excitatory')
proj2 = pynn.Projection(exc, neuron1, c_exc, target='excitatory')
proj3 = pynn.Projection(exc_2, neuron1, c_exc, target='excitatory')
proj4 = pynn.Projection(exc_3, neuron1, c_exc, target='inhibitory')
pynn.run(10000)
pynn.end()
def test_issue1565(self):
# although there is only 1 synapse column per neuron (of size 2), a 2nd synapse is used
self.marocco.neuron_placement.default_neuron_size(2)
con = pynn.FixedProbabilityConnector(p_connect=1.0, weights=0.004)
pynn.setup(marocco=self.marocco)
pop1 = pynn.Population(10, pynn.IF_cond_exp, {})
ipu1 = pynn.Population(2, pynn.SpikeSourceArray, {'spike_times': []})
pro1 = pynn.Projection(ipu1, pop1, con, target='excitatory')
pynn.run(0)
def build(self):
self.neurons = pynn.Population(self.N, self.model)
connector = pynn.FixedProbabilityConnector(p_connect=self.prob,
allow_self_connections=True,
weights=0.003)
pynn.Projection(self.neurons,
self.neurons,
connector,
target='excitatory',
rng=pynn.NativeRNG(42))
def test_issue1681(self):
con = pynn.FixedProbabilityConnector(p_connect=1.0, weights=0.004)
pynn.setup(marocco=self.marocco)
pop1 = pynn.Population(10, pynn.IF_cond_exp, {})
ipu1 = pynn.Population(1, pynn.SpikeSourceArray, {'spike_times': []})
pro1 = pynn.Projection(ipu1, pop1, con, target='excitatory')
# Issue 1681 "IndexError after mapping ends" is triggered by adding a
# population after a projection:
# Expected Output: NO ERROR
# Actual Output: "IndexError: vector::_M_range_check" (i.e. abnormal termination)
pop2 = pynn.Population(10, pynn.IF_cond_exp, {})
pynn.run(0)
def build(self):
self.neurons = []
for i in range(self.num_layers):
self.neurons.append(
pynn.Population(self.neurons_per_layer, self.model))
connector = pynn.FixedProbabilityConnector(
p_connect=self.conn_prob,
allow_self_connections=False,
weights=0.003)
proj = []
for i in range(1, self.num_layers):
proj = pynn.Projection(self.neurons[i - 1],
self.neurons[i],
connector,
target='excitatory',
rng=pynn.NativeRNG(42))
def test_stimulated_network(self):
con = pynn.FixedProbabilityConnector(p_connect=1.0, weights=0.004)
pop_size = 100
ipu_size = 20
pynn.setup(marocco=self.marocco)
ipu1 = pynn.Population(ipu_size, pynn.SpikeSourceArray, {'spike_times': []})
pop1 = pynn.Population(pop_size, pynn.IF_cond_exp, {})
pro1 = pynn.Projection(ipu1, pop1, con, target='excitatory')
ipu2 = pynn.Population(ipu_size, pynn.SpikeSourceArray, {'spike_times': []})
pop2 = pynn.Population(pop_size, pynn.IF_cond_exp, {})
pro2 = pynn.Projection(ipu2, pop2, con, target='excitatory')
pynn.run(0)
all_pops = [pop1, ipu1, pop2, ipu2]
map(self.helper_test_mapping, all_pops)
pynn.end()
def main():
"""
create small network with synapse loss. The synapse loss happens due to a
maximum syndriver chain length of 5 and only 4 denmems per neuron. After
mapping, the synapse loss per projection is evaluated and plotted for one
projection. The sum of lost synapses per projection is compared to the
overall synapse loss returnd by the mapping stats.
"""
marocco = PyMarocco()
marocco.neuron_placement.default_neuron_size(4)
marocco.synapse_routing.driver_chain_length(5)
marocco.continue_despite_synapse_loss = True
marocco.calib_backend = PyMarocco.CalibBackend.Default
marocco.neuron_placement.skip_hicanns_without_neuron_blacklisting(False)
pynn.setup(marocco=marocco)
neuron = pynn.Population(50, pynn.IF_cond_exp)
source = pynn.Population(50, pynn.SpikeSourcePoisson, {'rate': 2})
connector = pynn.FixedProbabilityConnector(allow_self_connections=True,
p_connect=0.5,
weights=0.00425)
proj_stim = pynn.Projection(source, neuron, connector, target="excitatory")
proj_rec = pynn.Projection(neuron, neuron, connector, target="excitatory")
pynn.run(1)
print marocco.stats
total_syns = 0
lost_syns = 0
for proj in [proj_stim, proj_rec]:
l, t = projectionwise_synapse_loss(proj, marocco)
total_syns += t
lost_syns += l
assert total_syns == marocco.stats.getSynapses()
assert lost_syns == marocco.stats.getSynapseLoss()
plot_projectionwise_synapse_loss(proj_stim, marocco)
pynn.end()
import pyhmf as pynn
from pymarocco import PyMarocco
import pylogging, pyhalbe
pyhalbe.Debug.change_loglevel(2)
pylogging.set_loglevel(pylogging.get("marocco"), pylogging.LogLevel.TRACE)
pylogging.set_loglevel(pylogging.get("sthal"), pylogging.LogLevel.DEBUG)
marocco = PyMarocco()
marocco.neuron_placement.default_neuron_size(4)
pynn.setup(marocco=marocco)
neuron1 = pynn.Population(1, pynn.IF_cond_exp)
inh = pynn.Population(1, pynn.SpikeSourceArray, {'spike_times': [0]})
exc = pynn.Population(1, pynn.SpikeSourceArray, {'spike_times': [0]})
exc_2 = pynn.Population(1, pynn.SpikeSourceArray, {'spike_times': [0]})
exc_3 = pynn.Population(1, pynn.SpikeSourceArray, {'spike_times': [0]})
c_exc = pynn.FixedProbabilityConnector(p_connect=1.0, weights=1)
proj1 = pynn.Projection(inh, neuron1, c_exc, target='excitatory')
proj2 = pynn.Projection(exc, neuron1, c_exc, target='excitatory')
proj3 = pynn.Projection(exc_2, neuron1, c_exc, target='excitatory')
proj4 = pynn.Projection(exc_3, neuron1, c_exc, target='inhibitory')
pynn.run(10000)
pynn.end()
