#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
Simple network consisting of 10 disconnected neurons and a spike source array.
"""
import sys
import common.setup # Common example code (checks command line parameters)
import common.params # Parameters for the models which work with all systems
import common.utils # Output functions
import pynnless as pynl
# Create a new pl instance with the given backend
backend = sys.argv[1]
sim = pynl.PyNNLess(backend)
# Create and run network with two populations: One population consisting of a
# spike source arrays and another population consisting of neurons.
print("Simulating network...")
count = 10
res = sim.run(pynl.Network().add_source(
spike_times=[100.0 * i for i in xrange(1, 9)]).add_population(
pynl.IfCondExpPopulation(
count=count,
params=common.params.IF_cond_exp).record_spikes()).add_connections(
[((0, 0), (1, i), 0.024, 0.0) for i in xrange(count)]))
print("Done!")
# Write the spike times for each neuron to disk
print("Writing spike times to " + common.setup.outfile)
# Read in neuron weights, some still have to be set manually
with open("data/optimised_weights.json", 'r') as outfile:
weights = json.load(outfile)
# Generate BiNAM
mat_in = data.generate(data_params["n_bits_in"], data_params["n_ones_in"],
data_params["n_samples"])
mat_out = data.generate(data_params["n_bits_out"], data_params["n_ones_out"],
data_params["n_samples"])
print "Data generated!"
# set up simulator
scm = pyscm.SpikeCounterModel(mat_in, mat_out)
sim = pynl.PyNNLess(sys.argv[1])
net, input_indices, _, input_times = scm.build(
params=params,
weights=weights,
delay=delay,
terminating_neurons=terminating_neurons,
flag=flag)
print "Preparations done"
# Simulation
res = sim.run(net)
print "Simulation done"
# Plot
for pIdx, pop in enumerate(res):
if (not "spikes" in pop):