def test():
if not HAVE_H5PY:
raise SkipTest
sim.setup()
p1 = sim.Population(10,
sim.IF_cond_exp(
v_rest=-65,
tau_m=lambda i: 10 + 0.1*i,
cm=RD('normal', (0.5, 0.05))),
label="population_one")
p2 = sim.Population(20,
sim.IF_curr_alpha(
v_rest=-64,
tau_m=lambda i: 11 + 0.1*i),
label="population_two")
prj = sim.Projection(p1, p2,
sim.FixedProbabilityConnector(p_connect=0.5),
synapse_type=sim.StaticSynapse(weight=RD('uniform', [0.0, 0.1]),
delay=0.5),
receptor_type='excitatory')
net = Network(p1, p2, prj)
export_to_sonata(net, "tmp_serialization_test", overwrite=True)
net2 = import_from_sonata("tmp_serialization_test/circuit_config.json", sim)
for orig_population in net.populations:
imp_population = net2.get_component(orig_population.label)
assert orig_population.size == imp_population.size
for name in orig_population.celltype.default_parameters:
assert_array_almost_equal(orig_population.get(name), imp_population.get(name), 12)
w1 = prj.get('weight', format='array')
prj2 = net2.get_component(asciify(prj.label).decode('utf-8') + "-0")
w2 = prj2.get('weight', format='array')
assert_array_almost_equal(w1, w2, 12)
def test_native_stdp_model():
if not have_nest:
raise SkipTest
nest = pyNN.nest
from pyNN.utility import init_logging
init_logging(logfile=None, debug=True)
nest.setup()
p1 = nest.Population(10, nest.IF_cond_exp())
p2 = nest.Population(10, nest.SpikeSourcePoisson())
stdp_params = {'Wmax': 50.0, 'lambda': 0.015, 'weight': 0.001}
stdp = nest.native_synapse_type("stdp_synapse")(**stdp_params)
connector = nest.AllToAllConnector()
prj = nest.Projection(p2,
p1,
connector,
receptor_type='excitatory',
synapse_type=stdp)
def connect(self):
n_cells = self.parameters.config.n_cells
n_segments = self.parameters.config.n_segments
# connect populations
pynn.Projection(self.distal_input, self.distal,
pynn.OneToOneConnector(weights=0.025))
for i in range(self.parameters.config.n_columns):
# get "compartments" for all cells in this column
inhibitions = self.inhibitory[i * n_cells:(i + 1) * n_cells]
somas = self.soma[i * n_cells:(i + 1) * n_cells]
proximal_input = pynn.PopulationView(self.proximal_input, [i])
# set up connections with columnar symmetry
pynn.Projection(inhibitions,
somas,
pynn.DistanceDependentProbabilityConnector(
'd>=1', weights=0.2),
target='SYN_2') # 4
pynn.Projection(proximal_input,
somas,
pynn.AllToAllConnector(weights=0.08),
target='SYN_1') # 1
pynn.Projection(proximal_input, inhibitions,
pynn.AllToAllConnector(weights=0.042)) # 2
for j in range(self.parameters.config.n_cells):
# get "compartments" for this specific cell
segments = self.distal[i * n_cells * n_segments + j *
n_segments:i * n_cells * n_segments +
(j + 1) * n_segments]
inhibition = pynn.PopulationView(self.inhibitory,
[i * n_cells + j])
soma = pynn.PopulationView(self.soma, [i * n_cells + j])
# set up connections with cellular symmetry
pynn.Projection(segments,
inhibition,
pynn.AllToAllConnector(weights=0.15),
target='inhibitory') # 3
pynn.Projection(segments,
soma,
pynn.AllToAllConnector(weights=0.15),
target='SYN_3') # 3
def test_projection(self):
path = tempfile.mkstemp()[1]
size_a = random.randint(1, 100)
size_b = random.randint(1, 100)
dist = pyhmf.RandomDistribution(rng=pyhmf.NativeRNG(1337))
conn_pyhmf = pyhmf.AllToAllConnector(weights=dist, delays=42)
proj_pyhmf = pyhmf.Projection(
pyhmf.Population(size_a, pyhmf.IF_cond_exp),
pyhmf.Population(size_b, pyhmf.IF_cond_exp),
conn_pyhmf)
proj_pyhmf.saveConnections(getattr(pyhmf, self.file_type)(path, 'w'))
conn_pynn = pynn.FromFileConnector(getattr(pynn.recording.files, self.file_type)(path))
proj_pynn = pynn.Projection(
pynn.Population(size_a, pynn.IF_cond_exp),
pynn.Population(size_b, pynn.IF_cond_exp),
conn_pynn)
numpy.testing.assert_equal(proj_pyhmf.getWeights(format='array'), proj_pynn.getWeights(format='array'))
def presentStimuli(pres_duration, num_pres_per_stim, num_source, num_target, bright_on_weights, bright_off_weights, bright_lat_weights, dark_on_weights, dark_off_weights, dark_lat_weights, is_repeated=False):
"""
For presenting a stimulus to the target network. Callback is used to switch between presentation rates.
Arguments: num_source
num_target
num_pres_per_stim,
pres_duration
"""
num_stim = 2 # two stimuli 'bright' and 'dark'
total_duration = num_stim * num_pres_per_stim * pres_duration
source_on_pop = pynn.Population(num_source, pynn.SpikeSourcePoisson(), label='source_on_pop')
source_off_pop = pynn.Population(num_source, pynn.SpikeSourcePoisson(), label='source_off_pop')
is_bright, random_on_rates, random_off_rates = getPresentationRatesForCallback(num_stim, num_source, num_pres_per_stim, is_repeated=is_repeated)
bright_target_pop = pynn.Population(num_target, pynn.IF_cond_exp, {'i_offset':0.11, 'tau_refrac':3.0, 'v_thresh':-51.0}, label='target_pop')
dark_target_pop = pynn.Population(num_target, pynn.IF_cond_exp, {'i_offset':0.11, 'tau_refrac':3.0, 'v_thresh':-51.0}, label='target_pop')
bright_on_conn = pynn.Projection(source_on_pop, bright_target_pop, connector=pynn.AllToAllConnector(), synapse_type=pynn.StaticSynapse(weight=bright_on_weights), receptor_type='excitatory')
bright_off_conn = pynn.Projection(source_off_pop, bright_target_pop, connector=pynn.AllToAllConnector(), synapse_type=pynn.StaticSynapse(weight=bright_off_weights), receptor_type='excitatory')
bright_lat_conn = pynn.Projection(bright_target_pop, bright_target_pop, connector=pynn.AllToAllConnector(), synapse_type=pynn.StaticSynapse(weight=bright_lat_weights), receptor_type='inhibitory')
dark_on_conn = pynn.Projection(source_on_pop, dark_target_pop, connector=pynn.AllToAllConnector(), synapse_type=pynn.StaticSynapse(weight=dark_on_weights), receptor_type='excitatory')
dark_off_conn = pynn.Projection(source_off_pop, dark_target_pop, connector=pynn.AllToAllConnector(), synapse_type=pynn.StaticSynapse(weight=dark_off_weights), receptor_type='excitatory')
dark_lat_conn = pynn.Projection(dark_target_pop, dark_target_pop, connector=pynn.AllToAllConnector(), synapse_type=pynn.StaticSynapse(weight=dark_lat_weights), receptor_type='inhibitory')
source_on_pop.record('spikes')
source_off_pop.record('spikes')
bright_target_pop.record(['spikes'])
dark_target_pop.record(['spikes'])
pynn.run(total_duration, callbacks=[PoissonWeightVariation(source_on_pop, random_on_rates, pres_duration), PoissonWeightVariation(source_off_pop, random_off_rates, pres_duration)])
pynn.end()
source_on_spikes = source_on_pop.get_data('spikes').segments[0].spiketrains
source_off_spikes = source_off_pop.get_data('spikes').segments[0].spiketrains
bright_spikes = bright_target_pop.get_data('spikes').segments[0].spiketrains
dark_spikes = dark_target_pop.get_data('spikes').segments[0].spiketrains
return is_bright, source_on_spikes, source_off_spikes, bright_spikes, dark_spikes
measureSpikes = stimSpikes + delayStimMeasure
stim = pynn.Population(1, pynn.SpikeSourceArray, {'spike_times': stimSpikes})
measure = pynn.Population(1, pynn.SpikeSourceArray,
{'spike_times': measureSpikes})
#create neuron
neuron = pynn.Population(1, pynn.IF_cond_exp)
#init and import custom NEST synapse model
pynn.nest.SetDefaults(synapseModel, synapseParams)
stdpModel = pynn.NativeSynapseDynamics(synapseModel)
#connect stimuli
connStim = pynn.OneToOneConnector(weights=stimWeight)
connMeasure = pynn.OneToOneConnector(weights=measureWeight)
pynn.Projection(stim, neuron, connStim, target='excitatory')
prj = pynn.Projection(measure,
neuron,
connMeasure,
synapse_dynamics=stdpModel,
target='excitatory')
#record spike times and membrane potential
neuron.record()
neuron.record_v()
connSTDP = pynn.nest.FindConnections(measure)
weightList = []
aCausalList = []
aAnticausalList = []
timeGrid = np.arange(0, runtime + timeStep / 2.0, timeStep)
poissonE = sim.Population((1, ),
cellclass=sim.SpikeSourcePoisson,
cellparams=poissonE_params,
label='poissonE')
poissonI = sim.Population((1, ),
cellclass=sim.SpikeSourcePoisson,
cellparams=poissonI_params,
label='poissonI')
myconn = sim.AllToAllConnector(weights=globalWeight, delays=dt)
### Connections ###
prjE_E = sim.Projection(poissonE, popE, method=myconn, target='excitatory')
prjI_E = sim.Projection(poissonI, popE, method=myconn, target='inhibitory')
prjE_I = sim.Projection(poissonE, popI, method=myconn, target='excitatory')
prjI_I = sim.Projection(poissonI, popI, method=myconn, target='inhibitory')
## Record the spikes ##
popE.record(to_file=False)
popE.record_v(to_file=False)
popI.record(to_file=False)
popE.record_gsyn(to_file=False)
t1 = time()
sim.run(tsim)
t2 = time()
print("Elapsed %f seconds." % (t2 - t1, ))
def test_create_with_synapse_dynamics(self):
prj = sim.Projection(self.p1,
self.p2,
self.all2all,
synapse_type=sim.TsodyksMarkramSynapse())
# Create the C2 layer and connect it to the single output neuron
training_spiketrains = [[s for s in st] for st in training_pair[1]]
C2_populations, compound_C2_population =\
create_C2_populations(training_spiketrains)
out_p = sim.Population(1, sim.IF_curr_exp(tau_refrac=.1))
stdp_weight = 7 / s2_prototype_cells
stdp = sim.STDPMechanism(
weight=stdp_weight,
timing_dependence=sim.SpikePairRule(tau_plus=20.0,
tau_minus=26.0,
A_plus=stdp_weight / 5,
A_minus=stdp_weight / 4.48),
weight_dependence=sim.AdditiveWeightDependence(w_min=0.0,
w_max=15.8 *
stdp_weight))
learn_proj = sim.Projection(compound_C2_population, out_p,
sim.AllToAllConnector(), stdp)
epoch = training_pair[0]
print('Simulating for epoch', epoch)
# Record the spikes for visualization purposes and to count the number of
# fired spikes
# compound_C2_population.record('spikes')
out_p.record(['spikes', 'v'])
# Let the simulation run to "fill" the layer pipeline with spikes
sim.run(40)
# Datastructure for storing the computed STDP weights for this epoch
classifier_weights = []
/ (2 * np.power(sig, 2.))))), 2)
cann_connector.append((i, j, weights[i][j]))#, delay_cann2cann))
print("Weight matrix:\n", weights)
spike_times = [1000., 2000.]
cann_pop = sim.Population(n, sim.IF_cond_alpha(**cell_params),
label = "cann_pop")
inhib_pop = sim.Population(1, sim.IF_cond_alpha(**cell_params),
label = "inhib_pop")
spike_source = sim.Population(1, sim.SpikeSourceArray(
spike_times = spike_times))
spike_2_conn = sim.Projection(spike_source, cann_pop[spiky:spiky+1], sim.AllToAllConnector(),
sim.StaticSynapse(weight = 0.002, delay = 0.1))
cann_2_cann = sim.Projection(cann_pop, cann_pop, sim.FromListConnector(cann_connector, column_names=["weight"]),
sim.StaticSynapse(weight = 0.0001, delay = 75))
cann_2_inh = sim.Projection(cann_pop, inhib_pop, sim.AllToAllConnector(),
sim.StaticSynapse(weight = 0.02, delay = 0.1),
receptor_type = "excitatory")
inh_2_cann = sim.Projection(inhib_pop, cann_pop, sim.AllToAllConnector(),
sim.StaticSynapse(weight = 0.2, delay = 0.1),
receptor_type = "inhibitory")
spike_source.record('spikes')
cann_pop.record(('v','spikes'))
inhib_pop.record(('v','spikes'))
sim.IF_cond_alpha(**cell_params),
label="inhib_pop")
spike_source_1 = sim.Population(
1, sim.SpikeSourceArray(spike_times=spike_time_for_id_1))
spike_source_2 = sim.Population(
1, sim.SpikeSourceArray(spike_times=spike_time_for_id_2))
spike_source_3 = sim.Population(
1, sim.SpikeSourceArray(spike_times=spike_time_for_id_3))
spike_source_4 = sim.Population(
1, sim.SpikeSourceArray(spike_times=spike_time_for_id_4))
spike_source_9 = sim.Population(
1, sim.SpikeSourceArray(spike_times=spike_time_for_id_9))
spike_2_conn_for_1 = sim.Projection(spike_source_1, cann_pop[1:2],
sim.OneToOneConnector(),
sim.StaticSynapse(weight=0.002, delay=0.1))
spike_2_conn_for_2 = sim.Projection(spike_source_2, cann_pop[2:3],
sim.OneToOneConnector(),
sim.StaticSynapse(weight=0.002, delay=0.1))
spike_2_conn_for_3 = sim.Projection(spike_source_3, cann_pop[3:4],
sim.OneToOneConnector(),
sim.StaticSynapse(weight=0.002, delay=0.1))
spike_2_conn_for_4 = sim.Projection(spike_source_4, cann_pop[4:5],
sim.OneToOneConnector(),
sim.StaticSynapse(weight=0.002, delay=0.1))
spike_2_conn_for_9 = sim.Projection(spike_source_9, cann_pop[9:10],
sim.OneToOneConnector(),
sim.StaticSynapse(weight=0.002, delay=0.1))
cann_2_cann = sim.Projection(
}
neuron1 = sim.create(sim.HH_cond_exp(**cellparams))
neuron1.record(["v"])
neuron1.initialize(v=cellparams["e_rev_leak"])
neuron2 = sim.create(sim.HH_cond_exp(**cellparams))
neuron2.record(["v"])
neuron2.initialize(v=cellparams["e_rev_leak"])
spike_times = [2.0]
spike_source = sim.Population(1, sim.SpikeSourceArray(spike_times=spike_times))
sim.Projection(spike_source,
neuron1,
sim.OneToOneConnector(),
sim.StaticSynapse(weight=0.076, delay=0.1),
receptor_type='excitatory'),
sim.Projection(spike_source,
neuron2,
sim.OneToOneConnector(),
sim.StaticSynapse(weight=0.0755, delay=0.1),
receptor_type='excitatory'),
sim.run(tEnd)
data1 = neuron1.get_data()
data2 = neuron2.get_data()
signal_names = [s.name for s in data1.segments[0].analogsignalarrays]
stat_syn_input = p.StaticSynapse(weight=50.0, delay=1)
stat_syn_rout = p.StaticSynapse(weight=0.0, delay=1)
######################
###### Connections #######
res_conn = p.FixedProbabilityConnector(param.res_pconn, rng=param.rng)
inp_conn = p.AllToAllConnector()
rout_conn = p.AllToAllConnector()
connections = {}
connections['r2r'] = p.Projection(reservoir,
reservoir,
res_conn,
synapse_type=stat_syn_res,
receptor_type='excitatory')
connections['inp2r'] = p.Projection(input_neurons,
reservoir,
inp_conn,
synapse_type=stat_syn_input,
receptor_type='excitatory')
connections['r2rout'] = p.Projection(reservoir,
readout_neurons,
rout_conn,
synapse_type=stat_syn_rout,
receptor_type='excitatory')
def test_callback(data_input):
global message
message = data_input.actual.positions
msg_list = list(message)
#msg_list[0] = int(message[0].encode('hex'),16)
#for i in
#msg_list = int(message.encode('hex'),16)
#print('============= Received image data.',message)
rospy.loginfo('=====received data %r', msg_list[0])
timer = Timer()
dt = 0.1
p.setup(timestep=dt) # 0.1ms
pop_1 = p.Population(1, p.IF_curr_exp, {}, label="pop_1")
#input = p.Population(1, p.SpikeSourceArray, {'spike_times': [[0,3,6]]}, label='input')
input = p.Population(1, p.SpikeSourcePoisson,
{'rate': (msg_list[0] + 1.6) * 100})
stat_syn = p.StaticSynapse(weight=50.0, delay=1)
input_proj = p.Projection(input,
pop_1,
p.OneToOneConnector(),
synapse_type=stat_syn,
receptor_type='excitatory')
pop_1.record(['v', 'spikes'])
p.run(10)
pop_1_data = pop_1.get_data()
spikes = pop_1_data.segments[0].spiketrains[0]
mean_rate = int(gaussian_convolution(spikes, dt))
rospy.loginfo('=====mean_rate %r', mean_rate) # mean_rate = 64
rate_command = mean_rate
# rate coding of the spike train
'''
pub = rospy.Publisher('/cmd_vel_mux/input/teleop', Twist, queue_size=10)
# construct the output command
command = Twist()
command.linear.x = rate_command*0.02
command.angular.z = rate_command/50000.
pub.publish(command)
'''
pub = rospy.Publisher('/arm_controller/follow_joint_trajectory/goal',
FollowJointTrajectoryActionGoal,
queue_size=10)
command = FollowJointTrajectoryActionGoal()
command.header.stamp = rospy.Time.now()
command.goal.trajectory.joint_names = ['elbow']
point = JointTrajectoryPoint()
point.positions = [rate_command / 10]
point.time_from_start = rospy.Duration(1)
command.goal.trajectory.points.append(point)
pub.publish(command)
rospy.loginfo('=====send command %r', command.goal.trajectory.points[0])
fig_settings = {
'lines.linewidth': 0.5,
'axes.linewidth': 0.5,
'axes.labelsize': 'small',
'legend.fontsize': 'small',
'font.size': 8
}
plt.rcParams.update(fig_settings)
fig1 = plt.figure(1, figsize=(6, 8))
def plot_spiketrains(segment):
for spiketrain in segment.spiketrains:
y = np.ones_like(spiketrain) * spiketrain.annotations['source_id']
plt.plot(spiketrain, y, '.')
plt.ylabel(segment.name)
plt.setp(plt.gca().get_xticklabels(), visible=False)
def plot_signal(signal, index, colour='b'):
label = "Neuron %d" % signal.annotations['source_ids'][index]
plt.plot(signal.times, signal[:, index], colour, label=label)
plt.ylabel("%s (%s)" %
(signal.name, signal.units._dimensionality.string))
plt.setp(plt.gca().get_xticklabels(), visible=False)
plt.legend()
print("now plotting the network---------------")
rospy.loginfo('--------now plotting---------------')
n_panels = sum(a.shape[1]
for a in pop_1_data.segments[0].analogsignalarrays) + 2
plt.subplot(n_panels, 1, 1)
plot_spiketrains(pop_1_data.segments[0])
panel = 3
for array in pop_1_data.segments[0].analogsignalarrays:
for i in range(array.shape[1]):
plt.subplot(n_panels, 1, panel)
plot_signal(array, i, colour='bg'[panel % 2])
panel += 1
plt.xlabel("time (%s)" % array.times.units._dimensionality.string)
plt.setp(plt.gca().get_xticklabels(), visible=True) #
# from numpy import r
input = sim.Population(3, sim.SpikeSourceArray)
numpy.random.seed(12345)
input[0].spike_times = numpy.add.accumulate(
numpy.random.exponential(1000.0 / 100.0, size=1000))
input[1].spike_times = numpy.add.accumulate(
numpy.random.exponential(1000.0 / 20.0, size=1000))
input[2].spike_times = numpy.add.accumulate(
numpy.random.exponential(1000.0 / 50.0, size=1000))
connector = sim.OneToOneConnector(weights=1.0, delays=0.5)
conn = [
sim.Projection(input[0:1], cells, connector, target='AMPA_spikeinput'),
sim.Projection(input[1:2], cells, connector, target='GABAa_spikeinput'),
sim.Projection(input[2:3], cells, connector, target='GABAb_spikeinput')
]
cells._record('iaf_V')
cells._record('AMPA_g')
cells._record('GABAa_g')
cells._record('GABAb_g')
cells.record()
sim.run(100.0)
cells.recorders['iaf_V'].write("Results/nineml_neuron.V", filter=[cells[0]])
cells.recorders['AMPA_g'].write("Results/nineml_neuron.g_exc",
filter=[cells[0]])
1,
sim.SpikeSourcePoisson(duration=simparams['duration'],
rate=simparams['input_rate']))
inp.label = 'input cell'
outp = sim.Population(1, sim.IF_curr_exp, cellparams=cellparams)
outp.label = 'output cell'
inp.record('spikes')
outp.record(['v', 'spikes'])
synapse = sim.StaticSynapse(weight=1, delay=simparams['delay'])
connector = sim.OneToOneConnector()
connection = sim.Projection(inp, outp, connector, synapse)
def report_time(t):
print("Time: {}".format(t))
return t + simparams['dt']
par = 'i_offset'
for p in [0.01]:
outp.set(**{par: p})
cellparams[par] = p
outp.initialize(v=cellparams['v_rest'])
sim.run(simparams['duration'], callbacks=[report_time])
sim.reset(annotations={par: p})
inp_data = inp.get_data()
outp_data = outp.get_data()
for syn_type in ('exc', 'inh'):
populations[syn_type] = [sim.Population(population_size,
sim.IF_cond_exp,
neuron_parameters)
for i in range(n_populations)]
# --- Connect the populations in a chain -------
connector_exc_exc = sim.AllToAllConnector(weights=weight_exc_exc, delays=delay)
connector_exc_inh = sim.AllToAllConnector(weights=weight_exc_inh, delays=delay)
connector_inh_exc = sim.AllToAllConnector(weights=weight_inh_exc, delays=delay)
for i in range(n_populations):
j = (i + 1) % n_populations
prj_exc_exc = sim.Projection(populations['exc'][i], populations['exc'][j],
connector_exc_exc, target='excitatory')
prj_exc_inh = sim.Projection(populations['exc'][i], populations['inh'][j],
connector_exc_inh, target='excitatory')
prj_inh_exc = sim.Projection(populations['inh'][i], populations['exc'][i],
connector_exc_exc, target='inhibitory')
# --- Create and connect stimulus --------------
numpy.random.seed(rng_seed)
stim_spikes = numpy.random.normal(loc=stimulus_onset,
scale=stimulus_sigma,
size=population_size)
stim_spikes.sort()
stimulus = sim.Population(1, sim.SpikeSourceArray, {'spike_times': stim_spikes})
prj_stim_exc = sim.Projection(stimulus, populations['exc'][0],
stat_syn_inh = p.StaticSynapse(weight=20.0, delay=1)
stat_syn_input = p.StaticSynapse(weight=50.0, delay=1)
######################
###### Connections #######
exc_conn = p.FixedProbabilityConnector(param.res_pconn, rng=param.rng)
inh_conn = p.FixedProbabilityConnector(param.res_pconn, rng=param.rng)
inp_conn = p.AllToAllConnector()
rout_conn = p.AllToAllConnector()
connections = {}
connections['e2e'] = p.Projection(reservoir_exc,
reservoir_exc,
exc_conn,
synapse_type=stat_syn_exc,
receptor_type='excitatory')
connections['e2i'] = p.Projection(reservoir_exc,
reservoir_inh,
exc_conn,
synapse_type=stat_syn_exc,
receptor_type='excitatory')
connections['i2e'] = p.Projection(reservoir_inh,
reservoir_exc,
inh_conn,
synapse_type=stat_syn_inh,
receptor_type='inhibitory')
connections['i2i'] = p.Projection(reservoir_inh,
reservoir_inh,
inh_conn,
#generate_spike_times = [[0], [500], [1000], [1500], [2000], [2500], [3000],
# [3500], [4000], [4500]] #, [5000]]
#spike_source = sim.Population(n_neurons,
# sim.SpikeSourceArray(
# spike_times = generate_spike_times))
spike_source = sim.Population(
n_neurons, sim.SpikeSourceArray(spike_times=[0, 1000, 3000]))
# populations and projections
layer_1 = sim.Population(n_neurons, sim.IF_curr_alpha(), label="layer_1")
'''
layer_2 = sim.Population(n_neurons, sim.IF_curr_alpha(),
label = "layer_2")
'''
proj_src_2_l1 = sim.Projection(spike_source, layer_1, sim.OneToOneConnector())
'''
proj_l1_2_l2 = sim.Projection(layer_1, layer_2,
sim.OneToOneConnector())
'''
sim.run(sim_time)
layer_1.record(('spikes', 'v'))
#layer_2.record(['spikes', 'v'])
#layer_1.record(['v', 'spikes'])
#layer_2.record(['v', 'spikes'])
# plot the spikes
data_1 = layer_1.get_data().segments[0]
vm = data_1.filter(name="v")
Figure(Panel(vm, ylabel="Memb. Pot."),
# a sub-set of the inh_gamma_generaters are silenced after a time "tinit"
myConnectorE_E_silenced = sim.FixedNumberPreConnector(NumOfConE_E, weights=globalWeight, delays=0.1)
myConnectorE_I_silenced = sim.FixedNumberPreConnector(NumOfConE_I, weights=globalWeight, delays=0.1)
#myConnectorI = sim.AllToAllConnector(weights=globalWeight, delays=0.1)
# InhGamma Generators need "_S" (selective) type synapses
# Passing this class to the Projection in a ComposedSynapseType object
# is how to get them:
#sd = NativeSynapseType('static_synapse_S')
#prjE_E = sim.Projection(gammaE_E, popE, method=myConnectorE_E, target='excitatory', synapse_type=sd)
#prjE_I = sim.Projection(gammaE_I, popI, method=myConnectorE_I, target='excitatory', synapse_type=sd)
prjE_E = sim.Projection(gammaE_E, popE, method=myConnectorE_E, target='excitatory')
prjE_I = sim.Projection(gammaE_I, popI, method=myConnectorE_I, target='excitatory')
# silenced excitatory input
prjE_E = sim.Projection(gammaE_E_silenced, popE, method=myConnectorE_E_silenced,
target='excitatory', synapse_type=sd)
prjE_I = sim.Projection(gammaE_I_silenced, popI, method=myConnectorE_I_silenced,
target='excitatory', synapse_type=sd)
# return to default synapse type (non-selective)
sd = None
#prjI_E = sim.Projection(poissonI_E, popE, method=myConnectorI, target='inhibitory', synapse_type=sd)
#prjI_I = sim.Projection(poissonI_I, popI, method=myConnectorI, target='inhibitory', synapse_type=sd)
fc0 = sim.Population(w1.shape[0], fc_celltype)
fc0.initialize(**cellvalues)
fc0.set(v_thresh=vth0)
fc1 = sim.Population(w1.shape[0], fc_celltype)
fc1.initialize(**cellvalues)
fc1.set(v_thresh=vth1)
#fc1.set(i_offset=b1)
fc2 = sim.Population(10, fc_celltype)
fc2.initialize(**cellvalues)
fc2.set(v_thresh=vth2)
#fc2.set(i_offset=b2)
proj0 = sim.Projection(input_pop,
fc0,
connector=sim.FromListConnector(conns_0))
proj1 = sim.Projection(fc0, fc1, connector=sim.FromListConnector(conns_1))
proj2 = sim.Projection(fc1, fc2, connector=sim.FromListConnector(conns_2))
#input_pop.record('spikes')
fc0.record(['spikes', 'v'])
#fc1.record(['spikes','v'])
#fc2.record(['spikes','v'])
fc2.record(['spikes'])
sim.run(duration - dt)
#spiketrains_i = input_pop.get_data().segments[-1].spiketrains
#spiketrains_0 = fc0.get_data().segments[-1].spiketrains
#spiketrains_1 = fc1.get_data().segments[-1].spiketrains
for i in range(weights[layer].shape[1]):
for j in range(weights[layer].shape[0]):
if float(weights[layer][j, i]) < 0.0:
inh_synapses.append([i, j, -1.0 * weights[layer][j, i], delay])
else:
exc_synapses.append([i, j, weights[layer][j, i], delay])
pops.append(sim.Population(weights[layer].shape[0], fc_celltype))
vth = v_th - biases[layer]
vth[vth < 0.0] = 0.001
pops[layer + 1].set(v_thresh=vth)
sim.Projection(pops[layer],
pops[layer + 1],
connector=sim.FromListConnector(inh_synapses),
receptor_type='inhibitory')
sim.Projection(pops[layer],
pops[layer + 1],
connector=sim.FromListConnector(exc_synapses),
receptor_type='excitatory')
pops[-1].record(['spikes'])
pops[0].record(['spikes'])
pops[1].record(['spikes', 'v'])
num_to_test = 10
acc = 0.0
num_timesteps = duration / dt
def estimate_kb(cell_params_lif):
cell_para = copy.deepcopy(cell_params_lif)
random.seed(0)
p.setup(timestep=1.0, min_delay=1.0, max_delay=16.0)
run_s = 10.
runtime = 1000. * run_s
max_rate = 1000.
ee_connector = p.OneToOneConnector(weights=1.0, delays=2.0)
pop_list = []
pop_output = []
pop_source = []
x = np.arange(0., 1.01, 0.1)
count = 0
trail = 10
for i in x:
for j in range(trail): #trails for average
pop_output.append(p.Population(1, p.IF_curr_exp, cell_para))
poisson_spikes = mu.poisson_generator(i * max_rate, 0, runtime)
pop_source.append(
p.Population(1, p.SpikeSourceArray,
{'spike_times': poisson_spikes}))
p.Projection(pop_source[count],
pop_output[count],
ee_connector,
target='excitatory')
pop_output[count].record()
count += 1
count = 0
for i in x:
cell_para['i_offset'] = i
pop_list.append(p.Population(1, p.IF_curr_exp, cell_para))
pop_list[count].record()
count += 1
pop_list[count - 1].record_v()
p.run(runtime)
rate_I = np.zeros(count)
rate_P = np.zeros(count)
rate_P_max = np.zeros(count)
rate_P_min = np.ones(count) * 1000.
for i in range(count):
spikes = pop_list[i].getSpikes(compatible_output=True)
rate_I[i] = len(spikes) / run_s
for j in range(trail):
spikes = pop_output[i * trail +
j].getSpikes(compatible_output=True)
spike_num = len(spikes) / run_s
rate_P[i] += spike_num
if spike_num > rate_P_max[i]:
rate_P_max[i] = spike_num
if spike_num < rate_P_min[i]:
rate_P_min[i] = spike_num
rate_P[i] /= trail
'''
#plot_spikes(spikes, 'Current = 10. mA')
plt.plot(x, rate_I, label='current',)
plt.plot(x, rate_P, label='Poisson input')
plt.fill_between(x, rate_P_min, rate_P_max, facecolor = 'green', alpha=0.3)
'''
x0 = np.where(rate_P > 1.)[0][0]
x1 = 4
k = (rate_P[x1] - rate_P[x0]) / (x[x1] - x[x0])
'''
plt.plot(x, k*(x-x[x0])+rate_P[x0], label='linear')
plt.legend(loc='upper left', shadow=True)
plt.grid('on')
plt.show()
'''
p.end()
return k, x[x0], rate_P[x0]
def create_S2_layers(C1_layers: Dict[float, Sequence[Layer]], feature_size,
s2_prototype_cells, refrac_s2=.1, stdp=True,
inhibition=True)\
-> Dict[float, List[Layer]]:
"""
Creates all prototype S2 layers for all sizes.
Parameters:
`layers_dict`: A dictionary containing for each size a list of C1
layers, for each feature one
`feature_size`:
`s2_prototype_cells`:
`refrac_s2`:
`stdp`:
Returns:
A dictionary containing for each size a list of different S2
layers, for each prototype one.
"""
f_s = feature_size
initial_weight = 25 / (f_s * f_s)
weight_rng = rnd.RandomDistribution('normal',
mu=initial_weight,
sigma=initial_weight / 20)
i_offset_rng = rnd.RandomDistribution('normal', mu=.5, sigma=.45)
weights = list(
map(lambda x: weight_rng.next() * 1000, range(4 * f_s * f_s)))
S2_layers = {}
i_offsets = list(
map(lambda x: i_offset_rng.next(), range(s2_prototype_cells)))
ndicts = list(map(lambda x: {}, range(s2_prototype_cells)))
ondicts = list(map(lambda x: {}, range(s2_prototype_cells)))
omdicts = list(map(lambda x: {}, range(s2_prototype_cells)))
for size, layers in C1_layers.items():
n, m = how_many_squares_in_shape(layers[0].shape, (f_s, f_s), f_s)
if stdp:
l_i_offsets = [list(map(lambda x: rnd.RandomDistribution('normal',
mu=i_offsets[i], sigma=.25).next(), range(n * m)))\
for i in range(s2_prototype_cells)]
else:
l_i_offsets = np.zeros((s2_prototype_cells, n * m))
print('S2 Shape', n, m)
layer_list = list(
map(
lambda i: Layer(
sim.Population(n * m,
sim.IF_curr_exp(i_offset=l_i_offsets[i],
tau_refrac=refrac_s2),
structure=space.Grid2D(aspect_ratio=m / n),
label=str(i)), (n, m)),
range(s2_prototype_cells)))
for S2_layer in layer_list:
for C1_layer in layers:
S2_layer.projections[C1_layer.population.label] =\
connect_layer_to_layer(C1_layer, S2_layer, (f_s, f_s), f_s,
[[w] for w in weights[:f_s * f_s]],
stdp=stdp,
initial_weight=initial_weight,
ndicts=ndicts, ondicts=ondicts,
omdicts=omdicts)
S2_layers[size] = layer_list
# Set the labels of the shared connections
if stdp:
t = time.clock()
print('Set shared labels')
for s2_label_dicts in [ndicts, ondicts, omdicts]:
for i in range(s2_prototype_cells):
w_iter = weights.__iter__()
for label, (source, target) in s2_label_dicts[i].items():
conns = nest.GetConnections(source=source, target=target)
nest.SetStatus(conns, {
'label': label,
'weight': w_iter.__next__()
})
print('Setting labels took', time.clock() - t)
if inhibition:
# Create inhibitory connections between the S2 cells
# First between the neurons of the same layer...
inh_weight = -10
inh_delay = .1
print('Create S2 self inhibitory connections')
for layer_list in S2_layers.values():
for layer in layer_list:
sim.Projection(
layer.population, layer.population,
sim.AllToAllConnector(allow_self_connections=False),
sim.StaticSynapse(weight=inh_weight, delay=inh_delay))
# ...and between the layers
print('Create S2 cross-scale inhibitory connections')
for i in range(s2_prototype_cells):
for layer_list1 in S2_layers.values():
for layer_list2 in S2_layers.values():
if layer_list1[i] != layer_list2[i]:
sim.Projection(
layer_list1[i].population,
layer_list2[i].population, sim.AllToAllConnector(),
sim.StaticSynapse(weight=inh_weight,
delay=inh_delay))
if stdp:
# Create the inhibition between different prototype layers
print('Create S2 cross-prototype inhibitory connections')
for layer_list in S2_layers.values():
for layer1 in layer_list:
for layer2 in layer_list:
if layer1 != layer2:
sim.Projection(
layer1.population, layer2.population,
sim.OneToOneConnector(),
sim.StaticSynapse(weight=inh_weight - 1,
delay=inh_delay))
return S2_layers
for variable in s['variables']:
if getattr(population, variable, None) is None:
setattr(population, variable, s[label][variable])
if label != 'InputLayer':
population.set(i_offset=s[label]['i_offset'])
layers.append(population)
print('assembly loaded')
# ------ Load weights and delays --------
for i in range(len(layers) - 1):
print('Loading connections for layer ' + str(layers[i].label))
filepath = os.path.join(layers_path, layers[i + 1].label)
conn_list = read_from_file2list(filepath)
exc_connector = sim.FromListConnector(conn_list[0])
pro_exc = sim.Projection(layers[i],
layers[i + 1],
connector=exc_connector,
receptor_type='excitatory')
if conn_list[1] != []:
inh_connector = sim.FromListConnector(read_from_file2list(filepath)[1])
pro_inh = sim.Projection(layers[i],
layers[i + 1],
connector=inh_connector,
receptor_type='inhibitory')
print('connections loaded')
# -------- Cell initialization -----------
#vars_to_record = ['spikes','v']
#if 'spikes' in vars_to_record:
# layers[0].record(['spikes']) # Input layer has no 'v'
for k in range(1, len(layers)):
layers[k].set(**cellparams)
def setup(self):
# set up layers according to backend
self.layers = []
if self.backend == 'spinnaker':
spin_sim.setup(timestep=1.0, time_scale_factor=1.0, min_delay=1.0, max_delay=None)
# set neuron hyperparameters
# tau_m should not be set too high, because it can easily lead to an overflow
ctx_parameters = {'cm': 1.0, 'i_offset': 0.0, 'tau_m': 100.0, 'tau_refrac': 0.0,
'v_reset': 0.0, 'v_rest': 0.0, 'v_thresh': 1.0}
tc_parameters = ctx_parameters.copy()
neuron_type = extra_models.IFCurDelta(**tc_parameters)
for l in range(0,len(self.weights)):
self.layers.append(spin_sim.Population(self.architecture[l+1], neuron_type,
initial_values={'v': 0.0}))
# set output layer threshold: must be set high enough s.t. output neurons do not spike,
# must not be set too high to avoid overflow
self.layers[-1].set(v_thresh=10000.0)
elif self.backend == 'nest':
nest_sim.setup(timestep=1.0, min_delay=1.0)
# set neuron type, currently only supports iaf_psc_delta and one set of hyperparameters
neuron_type = nest_sim.native_cell_type('iaf_psc_delta')
for l in range(0, len(self.weights)-1):
self.layers.append(nest_sim.Population(self.architecture[l+1],
neuron_type(V_th=1.0, t_ref=0.0,
V_min=-np.inf, C_m=1.0,
V_reset=0.0, tau_m=10.0**10,
E_L=0.0, I_e=0.0)))
self.layers[l].initialize(V_m=0.0)
# set up output layer seperately with infinite threshold
self.layers.append(nest_sim.Population(self.architecture[-1],
neuron_type(V_th=np.inf, t_ref=0.0,
V_min=-np.inf, C_m=1.0, V_reset=0.0,
tau_m=10.0 ** 10, E_L=0.0, I_e=0.0)))
self.layers[-1].initialize(V_m=0.0)
elif self.backend == 'pynn':
raise Exception('Current implementation does not support native pynn types. '
'Code after this exception acts as an example on how native pynn types'
'are used and can be modified. Additionally, the backend still needs to'
'be specified and is set to nest in the code below.')
# the following out-commented lines show how a native pynn type would be used
'''
nest_sim.setup(timestep=1.0, time_scale_factor=1.0, min_delay=1.0, max_delay=None)
# conductive exponential neuron as an example
ctx_parameters = {'cm': 1.0, 'e_rev_E': 0.0, 'e_rev_I': -65.0, 'i_offset': 0.0, 'tau_m': 10000.0, 'tau_refrac': 0.0, 'tau_syn_E': 0.01, 'tau_syn_I': 0.01, 'v_reset': -65.0, 'v_rest': -65.0, 'v_thresh': -64.0}
#tc_parameters = ctx_parameters.copy()
neuron_type = sim.IF_cond_exp(**tc_parameters)
for l in range(0,len(self.weights)):
self.layers.append(sim.Population(self.architecture[l+1], neuron_type, initial_values={'v': 0.0}))
# output layer should not spike
self.layers[-1].set(v_thresh=np.inf)
'''
else:
raise Exception('backend not supported')
# connect layers (input layer is connected to first in simulate by setting offsets
for l in range(0, len(self.layers)-1):
excitatory_connections = []
inhibitory_connections = []
# 1 and 2
for i in range(0, len(self.layers[l])):
for j in range(0, len(self.layers[l+1])):
if self.weights[l+1][i][j].detach().item() >= 0:
excitatory_connections.append((i, j,
self.weights[l+1][i][j].detach().item(),1.0))
else:
inhibitory_connections.append((i, j,
self.weights[l+1][i][j].detach().item(),1.0))
# when using NEST or native PyNN models, connect has to be called explicitly, when using
# SpiNNaker connecting happens automatically
if self.backend == 'pynn' or self.backend == 'nest':
excitatory_connector = nest_sim.FromListConnector(excitatory_connections,
column_names=["weight", "delay"])
excitatory_projection = nest_sim.Projection(self.layers[l], self.layers[l + 1],
connector=excitatory_connector)
excitatory_connector.connect(excitatory_projection)
elif self.backend == 'spinnaker':
excitatory_connector = spin_sim.FromListConnector(excitatory_connections,
column_names=["weight", "delay"])
excitatory_projection = spin_sim.Projection(self.layers[l], self.layers[l + 1],
connector=excitatory_connector)
# when using NEST or native PyNN models, connect has to be called explicitly, when using
# SpiNNaker connecting happens automatically
if self.backend == 'pynn' or self.backend == 'nest':
inhibitory_connector = nest_sim.FromListConnector(inhibitory_connections,
column_names=["weight", "delay"])
inhibitory_projection = nest_sim.Projection(self.layers[l], self.layers[l + 1],
inhibitory_connector,
receptor_type='inhibitory')
inhibitory_connector.connect(inhibitory_projection)
elif self.backend == 'spinnaker':
inhibitory_connector = spin_sim.FromListConnector(inhibitory_connections,
column_names=["weight", "delay"])
inhibitory_projection = spin_sim.Projection(self.layers[l], self.layers[l + 1],
inhibitory_connector,
receptor_type='inhibitory')
# set biases (constant input currents)
for l in range(0,len(self.layers)):
for i in range(0,len(self.layers[l])):
# use i_offset on spynnaker instead of DCSource
offset = self.biases[l][i].detach().item()
if self.backend == 'nest':
self.layers[l][i:i+1].set(I_e=offset)
elif self.backend == 'pynn' or self.backend=='spinnaker':
self.layers[l][i:i+1].set(i_offset=offset)
# record the potentials of the last layer
if self.backend == 'nest' or self.backend == 'pynn':
self.layers[-1].record('V_m')
elif self.backend == 'spinnaker':
self.layers[-1].record('v')
def test_create_simple(self):
prj = sim.Projection(self.p1,
self.p2,
self.all2all,
synapse_type=self.syn_a2a)
pop = p.Population(
num_output,
p.SpikeSourcePoisson,
{
'rate': MIN_rate, #test_x[i],
'start': (epo * num_epo + i) * (dur_train + silence),
'duration': dur_train
})
temp_popv = p.PopulationView(pop, np.nonzero(train_y[ind])[0])
temp_popv.set('rate', teaching_rate)
TeachingPoission.append(pop)
#print ImagePoission[10].get('start')
ee_connector = p.OneToOneConnector(weights=3.0)
for i in range(num_train * num_epo * 1):
p.Projection(ImagePoission[i],
pop_input,
ee_connector,
target='excitatory')
pop_output = p.Population(num_output, p.IF_curr_exp, cell_params_lif)
weight_max = 1.3
stdp_model = p.STDPMechanism(
timing_dependence=p.SpikePairRule(tau_plus=10., tau_minus=10.0),
weight_dependence=p.MultiplicativeWeightDependence(w_min=0.0,
w_max=weight_max,
A_plus=0.01,
A_minus=0.01)
#AdditiveWeightDependence
)
'''
weight_distr = p.RandomDistribution(distribution='normal',parameters=[1,0.1])
'''
def __init__(self, sim_params=None, cell_params=None, verbose=True):
'''
Parameters : Stimulus, Population, Synapses, Recording, Running
'''
self.verbose = verbose
self.sim_params = sim_params
self.cell_params = cell_params
sim.setup() #spike_precision='on_grid')#timestep = .1)
N_inh = int(sim_params['nb_neurons'] *
sim_params['p']) #total pop * proportion of inhib
self.spike_source = sim.Population(
N_inh,
sim.SpikeSourcePoisson(rate=sim_params['input_rate'],
duration=sim_params['simtime'] / 2))
#orientation stimulus, see bottom section of notebook
angle = 1. * np.arange(N_inh)
rates = self.tuning_function(angle,
sim_params['angle_input'] / 180. * N_inh,
sim_params['b_input'], N_inh)
rates /= rates.mean()
rates *= sim_params['input_rate']
for i, cell in enumerate(self.spike_source):
cell.set_parameters(rate=rates[i])
#neuron model selection
if sim_params['neuron_model'] == 'IF_cond_alpha':
model = sim.IF_cond_alpha #LIF with nice dynamics
else:
model = sim.IF_cond_exp #LIF with exp dynamics
#populations
E_neurons = sim.Population(
N_inh,
model(**cell_params),
initial_values={
'v':
rnd('uniform',
(sim_params['v_init_min'], sim_params['v_init_max']))
},
label="Excitateurs")
I_neurons = sim.Population(
int(sim_params['nb_neurons'] - N_inh),
model(**cell_params),
initial_values={
'v':
rnd('uniform',
(sim_params['v_init_min'], sim_params['v_init_max']))
},
label="Inhibiteurs")
#input to excitatories
input_exc = sim.Projection(
self.spike_source, E_neurons, sim.OneToOneConnector(),
sim.StaticSynapse(weight=sim_params['w_input_exc'],
delay=sim_params['s_input_exc']))
#loop through connections type and use associated params, can be a bit slow
conn_types = ['exc_inh', 'inh_exc', 'exc_exc',
'inh_inh'] #connection types
'''
self.proj = self.set_synapses(conn_types = conn_types, sim_params =sim_params,
E_neurons = E_neurons, I_neurons = I_neurons,
N_inh = N_inh)
'''
#Multi threading support NE MARCHE PAS LAISSER LE NJOBS EN 1
self.proj = Parallel(n_jobs=1, backend='multiprocessing')(
delayed(self.set_synapses)(conn_type,
sim_params=sim_params,
E_neurons=E_neurons,
I_neurons=I_neurons,
N_inh=N_inh,
conn_types=conn_types,
verbose=verbose)
for conn_type in range(len(conn_types)))
if verbose: print('Done building synapses !')
#record
self.spike_source.record('spikes')
E_neurons.record('spikes')
I_neurons.record('spikes')
#run
if verbose: print('Running simulation..')
sim.run(sim_params['simtime'])
if verbose: print('Done running !')
#get the spikes
self.E_spikes = E_neurons #.get_data().segments[0]
self.I_spikes = I_neurons #.get_data().segments[0]
self.P_spikes = self.spike_source #.get_data().segments[0]
def run_retina(params):
"""Run the retina using the specified parameters."""
tmpdir = tempfile.mkdtemp()
print "Setting up simulation"
pyNN.Timer.start() # start timer on construction
pyNN.setup(timestep=params['dt'], max_delay=params['syn_delay'])
pyNN.pynest.setDict([0], {'threads': params['threads']})
pyNN.setRNGseeds(params['kernelseeds'])
N = params['N']
phr_ON = pyNN.Population((N, N), 'dc_generator')
phr_OFF = pyNN.Population((N, N), 'dc_generator')
noise_ON = pyNN.Population((N, N), 'noise_generator', {
'mean': 0.,
'std': params['noise_std']
})
noise_OFF = pyNN.Population((N, N), 'noise_generator', {
'mean': 0.,
'std': params['noise_std']
})
phr_ON.set({
'start': params['simtime'] / 4,
'stop': params['simtime'] / 4 * 3
})
phr_ON.tset('amplitude', params['amplitude'] * params['snr'])
phr_OFF.set({
'start': params['simtime'] / 4,
'stop': params['simtime'] / 4 * 3
})
phr_OFF.tset('amplitude', -params['amplitude'] * params['snr'])
# target ON and OFF populations
out_ON = pyNN.Population((N, N), 'iaf_sfa_neuron', params['parameters_gc'])
out_OFF = pyNN.Population((N, N), 'iaf_sfa_neuron',
params['parameters_gc'])
#print "Connecting the network"
retina_proj_ON = pyNN.Projection(phr_ON, out_ON, 'oneToOne')
retina_proj_ON.setWeights(params['weight'])
retina_proj_OFF = pyNN.Projection(phr_OFF, out_OFF, 'oneToOne')
retina_proj_OFF.setWeights(params['weight'])
noise_proj_ON = pyNN.Projection(noise_ON, out_ON, 'oneToOne')
noise_proj_ON.setWeights(params['weight'])
noise_proj_OFF = pyNN.Projection(noise_OFF, out_OFF, 'oneToOne')
noise_proj_OFF.setWeights(params['weight'])
out_ON_filename = os.path.join(tmpdir, 'out_on.gdf')
out_OFF_filename = os.path.join(tmpdir, 'out_off.gdf')
out_ON.record()
out_OFF.record()
# reads out time used for building
buildCPUTime = pyNN.Timer.elapsedTime()
print "Running simulation"
pyNN.Timer.start() # start timer on construction
pyNN.run(params['simtime'])
simCPUTime = pyNN.Timer.elapsedTime()
out_ON.printSpikes(out_ON_filename)
out_OFF.printSpikes(out_OFF_filename)
out_ON_DATA = tmpfile2spikelist(out_ON_filename, params['dt'])
out_OFF_DATA = tmpfile2spikelist(out_OFF_filename, params['dt'])
print "\nRetina Network Simulation:"
print(params['description'])
print "Number of Neurons : ", N**2
print "Output rate (ON) : ", out_ON.meanSpikeCount(), \
"spikes/neuron in ", params['simtime'], "ms"
print "Output rate (OFF) : ", out_OFF.meanSpikeCount(), \
"spikes/neuron in ",params['simtime'], "ms"
print "Build time : ", buildCPUTime, "s"
print "Simulation time : ", simCPUTime, "s"
return out_ON_DATA, out_OFF_DATA
