def build(self):
weights = self._create_nn_unit_weights(self.linearsize, self.dimension)
self.neurons = [
pynn.Population(1, self.model)
for _ in range(self.linearsize**self.dimension)
]
self.exsources = pynn.Population(self.nsources,
pynn.SpikeSourcePoisson,
{'rate': self.sourcerate})
self.insources = pynn.Population(self.nsources,
pynn.SpikeSourcePoisson,
{'rate': self.sourcerate})
self.biasneurons = pynn.Population(self.nbiasneurons, self.model)
connector = pynn.FixedNumberPreConnector(n=self.ksources, weights=0.3)
proj = pynn.Projection(self.exsources,
self.neurons,
connector,
target='excitatory',
rng=pynn.NativeRNG(42))
proj = pynn.Projection(self.exsources,
self.neurons,
connector,
target='inhibitory',
rng=pynn.NativeRNG(42))
connector = pynn.FixedNumberPreConnector(n=self.nbiasneurons,
weights=0.4)
proj = pynn.Projection(self.biasneurons,
self.neurons,
connector,
target='inhibitory',
rng=pynn.NativeRNG(42))
for ipre, ipost, w in weights:
connector = pynn.AllToAllConnector(weights=1)
proj = pynn.Projection(self.neurons[ipre],
self.neurons[ipost],
connector,
target="excitatory")
def build(self):
weights = self._create_nn_unit_weights(self.linearsize, self.dimension)
self.neurons = [
pynn.Population(1, self.model)
for _ in range(self.linearsize**self.dimension)
]
self.noise = pynn.Population(self.nsources, pynn.IF_cond_exp)
self.biasneurons = pynn.Population(self.nbiasneurons, self.model)
connector = pynn.FixedNumberPreConnector(n=30,
weights=0.3,
allow_self_connections=False)
pynn.Projection(self.noise, self.noise, connector, target='inhibitory')
connector = pynn.FixedNumberPreConnector(n=self.ksources, weights=0.3)
pynn.Projection(self.noise,
self.neurons,
connector,
target='excitatory',
rng=pynn.NativeRNG(42))
pynn.Projection(self.noise,
self.neurons,
connector,
target='inhibitory',
rng=pynn.NativeRNG(43))
connector = pynn.FixedNumberPreConnector(n=self.kbiasneurons,
weights=0.4)
pynn.Projection(self.biasneurons,
self.neurons,
connector,
target='inhibitory',
rng=pynn.NativeRNG(44))
for ipre, ipost, w in weights:
connector = pynn.AllToAllConnector(weights=1)
pynn.Projection(self.neurons[ipre],
self.neurons[ipost],
connector,
target="excitatory")
def build(self):
self.neurons = pynn.Population(self.N, self.model)
connector = pynn.FixedNumberPreConnector(self.K,
weights=1,
allow_self_connections=False)
pynn.Projection(self.neurons,
self.neurons,
connector,
target='excitatory',
rng=pynn.NativeRNG(42))
def runTest(self):
pylogging.reset()
pylogging.default_config(pylogging.LogLevel.INFO)
marocco = pymarocco.PyMarocco()
marocco.neuron_placement.default_neuron_size(8)
marocco.calib_backend = pymarocco.PyMarocco.CalibBackend.Default
marocco.defects.backend = pymarocco.Defects.Backend.Without
marocco.hicann_configurator = pysthal.HICANNConfigurator()
marocco.continue_despite_synapse_loss = True
marocco.backend = pymarocco.PyMarocco.ESS
marocco.experiment_time_offset = 5e-7
n_exc = 100 # Number of excitatory neurons per group
sim_duration = 200.
pp_start = 50. # start = center of pulse-packet
weight_exc = 0.002 # uS weight for excitatory to excitatory connections
# (double than in reference paper)
pynn.setup(
max_delay=20.,
marocco=marocco,
)
# v_thresh close to v_rest to make sure there are some spikes
neuron_params = {
'v_rest': -65.,
'v_thresh': -62.5,
}
exc_pop = pynn.Population(n_exc, pynn.IF_cond_exp, neuron_params)
exc_pop.record()
pop_stim = pynn.Population(n_exc, pynn.SpikeSourceArray,
{'spike_times': [pp_start]})
conn = pynn.FixedNumberPreConnector(60, weights=weight_exc, delays=20.)
pynn.Projection(pop_stim, exc_pop, conn, target='excitatory')
pynn.run(sim_duration)
pynn.end()
import pyhmf as pynn
from pymarocco import PyMarocco
neuron_size = 4
marocco = PyMarocco()
marocco.placement.setDefaultNeuronSize(neuron_size)
pynn.setup(marocco=marocco)
con_alltoall = pynn.AllToAllConnector(weights=0.04)
con_fixednumberpre = pynn.FixedNumberPreConnector(n=6, weights=0.04)
n_neurons = 112
pop = pynn.Population(n_neurons, pynn.IF_cond_exp)
neuron_pool = range(n_neurons)
in_0 = pynn.Population(1, pynn.SpikeSourceArray, {'spike_times': [0]})
for _ in xrange(4):
pynn.Projection(in_0,
pop[neuron_pool[0:4]],
con_alltoall,
target='excitatory')
n_pops = 6
n_in_pop = 10
for p in xrange(n_pops):
l_a = neuron_pool[p * n_in_pop:(p + 1) * n_in_pop - 1]
l_b = neuron_pool[(p + 1) * n_in_pop:(p + 2) * n_in_pop - 1]