def main(args):
setup(timestep=0.1)
random_image = np.random.rand(2,2)
size = random_image.size
input_population_arr = Population(random_image.size, SpikeSourceArray, {'spike_times': [0 for i in range(0, random_image.size)]})
cell_params = {'tau_refrac': 2.0, 'v_thresh': -50.0, 'tau_syn_E': 2.0, 'tau_syn_I': 2.0}
output_population = Population(1, IF_curr_alpha, cell_params, label="output")
projection = Projection(input_population_arr, output_population, AllToAllConnector())
projection.setWeights(1.0)
input_population_arr.record('spikes')
output_population.record('spikes')
tstop = 1000.0
run(tstop)
output_population.write_data("simpleNetwork_output.pkl",'spikes')
input_population_arr.write_data("simpleNetwork_input.pkl",'spikes')
#output_population.print_v("simpleNetwork.v")
end()
def two_neuron_example(
current=1000.0,
time_simulation=2000.0,
weight=0.4,
neuron_parameters={"v_rest": -50.0, "cm": 1, "tau_m": 20.0, "tau_refrac": 5.0, "v_thresh": -40.0, "v_reset": -50.0},
):
sim.setup(timestep=0.1, min_delay=0.1)
pulse = sim.DCSource(amplitude=current, start=0.0, stop=time_simulation)
pre = sim.Population(1, sim.IF_curr_exp(**neuron_parameters))
pre.record("spikes")
pulse.inject_into(pre)
sim.run(time_simulation)
# rates in Hz
rate_pre = len(pre.get_data("spikes").segments[0].spiketrains[0]) / time_simulation * 1000.0
sim.end()
return rate_pre
def sim_neuron(rate):
neuron_parameters={
'v_rest' : -50.0,
'cm' : 1,
'tau_m' : 20.0,
'tau_syn_E' : 5.0,
'tau_syn_I' : 5.0,
'v_reset' : -50.0,
'v_thresh' : 10000000000000000000000000000000000000000000000000000000000000000000000.0,
'e_rev_E' : 0.0,
'e_rev_I' : -100,
}
time_simulation = 100000 # don't choose to small number in order to get good statistics
weight = 0.1 # is this value allreight
sim.setup(timestep=0.1, min_delay=0.1)
pois_exc = sim.SpikeSourcePoisson(duration=time_simulation,start=0.0,rate=rate) # generate poisson rate stimulus
pois_inh = sim.SpikeSourcePoisson(duration=time_simulation,start=0.0,rate=rate) # generate poisson rate stimulus
exc = sim.Population(1, cellclass=pois_exc) # simulate excitatory cell
inh = sim.Population(1, cellclass=pois_inh) # simulate inhibitory cell
rec = sim.Population(1, sim.IF_cond_exp(**neuron_parameters)) # simulate receiving neuron
sim.Projection(exc, rec, connector=sim.OneToOneConnector(),synapse_type=sim.StaticSynapse(weight=weight),receptor_type='excitatory') # connect excitatory neuron to receiver
sim.Projection(inh, rec, connector=sim.OneToOneConnector(),synapse_type=sim.StaticSynapse(weight=weight),receptor_type='inhibitory') # connect inhibitory neuron to receiver
rec.record('v') # record membrane potential
rec.record('gsyn_exc') # record excitatory conductance
rec.record('gsyn_inh') # record inhibitory conductance
sim.run(time_simulation) # start simulation
return rec.get_data('v').segments[0].analogsignalarrays[0], rec.get_data('gsyn_exc').segments[0].analogsignalarrays[0], rec.get_data('gsyn_inh').segments[0].analogsignalarrays[0] # return membrane potential, excitatory conductance, inhibitory conductance
def compute(self, proximal, distal=None):
if distal is not None:
for i, times in enumerate(distal):
self.distal_input[i].spike_times = times
active = []
predictive = []
if not (isinstance(proximal[0], list) or isinstance(proximal[0], np.ndarray)):
proximal = [proximal]
timestep = self.parameters.config.timestep
for p in proximal:
t = pynn.get_current_time()
for c in p:
self.proximal_input[int(c)].spike_times = np.array([t + 0.01])
pynn.run(self.parameters.config.timestep)
spikes_soma = self.soma.getSpikes()
mask = (spikes_soma[:,1] >= t) & (spikes_soma[:,1] < t + timestep)
active.append(np.unique(spikes_soma[mask,0]))
spikes_distal = self.distal.getSpikes()
mask = (spikes_distal[:,1] >= t) & (spikes_distal[:,1] < t + timestep)
predictive.append(np.unique(spikes_distal[mask,0].astype(np.int16)/2))
return (active, predictive)
def run_sim(ncell):
print "Cells: ", ncell
setup0 = time.time()
sim.setup(timestep=0.1)
hh_cell_type = sim.HH_cond_exp()
hh = sim.Population(ncell, hh_cell_type)
pulse = sim.DCSource(amplitude=0.5, start=20.0, stop=80.0)
pulse.inject_into(hh)
hh.record('v')
setup1 = time.time()
t0 = time.time()
sim.run(100.0)
v = hh.get_data()
sim.end()
t1 = time.time()
setup_total = setup1 - setup0
run_total = t1 - t0
print "Setup: ", setup_total
print "Run: ", run_total
print "Total sim time: ", setup_total + run_total
return run_total
def run(self, spiketimes):
assert spiketimes.shape[0] == self.n_spike_source, 'spiketimes length should be equal to input neurons'
start = time.clock()
sim.reset()
end = time.clock()
print "reset uses %f s." % (end - start)
for i in range(self.n_spike_source):
spiketime = np.array(spiketimes[i], dtype=float)
if spiketimes[i].any():
self.spike_source[i].spike_times = spiketime
sim.initialize(self.hidden_neurons, V_m=0)
sim.initialize(self.output_neurons, V_m=0.)
sim.run(self.sim_time)
spiketrains = self.output_neurons.get_data(clear=True).segments[0].spiketrains
# vtrace = self.hidden_neurons.get_data(clear=True).segments[0].filter(name='V_m')[0]
# plt.figure()
# plt.plot(vtrace.times, vtrace)
# plt.show()
hidden_spiketrains = self.hidden_neurons.get_data(clear=True).segments[0].spiketrains
spike_cnts = 0
for spiketrain in hidden_spiketrains:
spike_cnts += len(list(np.array(spiketrain)))
self.hidden_spike_cnts.append(spike_cnts)
print 'hidden spikes: ', spike_cnts
spiketimes_out = []
for spiketrain in spiketrains:
spiketimes_out.append(list(np.array(spiketrain)))
return np.array(spiketimes_out)
def compute(self, proximal, distal=None):
if distal is not None:
for i, times in enumerate(distal):
self.distal_input[i].spike_times = times
active = []
predictive = []
if not (isinstance(proximal[0], list)
or isinstance(proximal[0], np.ndarray)):
proximal = [proximal]
timestep = self.parameters.config.timestep
for p in proximal:
t = pynn.get_current_time()
for c in p:
self.proximal_input[int(c)].spike_times = np.array([t + 0.01])
pynn.run(self.parameters.config.timestep)
spikes_soma = self.soma.getSpikes()
mask = (spikes_soma[:, 1] >= t) & (spikes_soma[:, 1] <
t + timestep)
active.append(np.unique(spikes_soma[mask, 0]))
spikes_distal = self.distal.getSpikes()
mask = (spikes_distal[:, 1] >= t) & (spikes_distal[:, 1] <
t + timestep)
predictive.append(
np.unique(spikes_distal[mask, 0].astype(np.int16) / 2))
return (active, predictive)
def main():
# setup timestep of simulation and minimum and maximum synaptic delays
setup(timestep=simulationTimestep, min_delay=minSynapseDelay, max_delay=maxSynapseDelay)
# create a spike sources
retinaLeft = createSpikeSource("Retina Left")
retinaRight = createSpikeSource("Retina Right")
# create network and attach the spike sources
network = createCooperativeNetwork(retinaLeft=retinaLeft, retinaRight=retinaRight)
# run simulation for time in milliseconds
print "Simulation started..."
run(simulationTime)
print "Simulation ended."
# plot results
from itertools import repeat
numberOfLayersToPlot = 4
layers = zip(repeat(network, numberOfLayersToPlot), range(1, numberOfLayersToPlot+1), repeat(False, numberOfLayersToPlot))
customLayers = [(network, 20, False),(network, 40, False),(network, 60, False),(network, 80, False)]
for proc in range(0, numberOfLayersToPlot):
p = Process(target=plotSimulationResults, args=customLayers[proc])
p.start()
# finalise program and simulation
end()
def run_test(w_list, cell_para, spike_source_data):
pop_list = []
p.setup(timestep=1.0, min_delay=1.0, max_delay=3.0)
#input poisson layer
input_size = w_list[0].shape[0]
pop_in = p.Population(input_size, p.SpikeSourceArray, {'spike_times' : []})
for j in range(input_size):
pop_in[j].spike_times = spike_source_data[j]
pop_list.append(pop_in)
for w in w_list:
pos_w = np.copy(w)
pos_w[pos_w < 0] = 0
neg_w = np.copy(w)
neg_w[neg_w > 0] = 0
output_size = w.shape[1]
pop_out = p.Population(output_size, p.IF_curr_exp, cell_para)
p.Projection(pop_in, pop_out, p.AllToAllConnector(weights = pos_w), target='excitatory')
p.Projection(pop_in, pop_out, p.AllToAllConnector(weights = neg_w), target='inhibitory')
pop_list.append(pop_out)
pop_in = pop_out
pop_out.record()
run_time = np.ceil(np.max(spike_source_data)[0]/1000.)*1000
p.run(run_time)
spikes = pop_out.getSpikes(compatible_output=True)
return spikes
def scnn_test(cell_params_lif, l_cnn, w_cnn, num_test, test, max_rate,
dur_test, silence):
p.setup(timestep=1.0, min_delay=1.0, max_delay=3.0)
L = l_cnn
random.seed(0)
input_size = L[0][1]
pops_list = []
pops_list.append(
init_inputlayer(input_size, test[:num_test, :], max_rate, dur_test,
silence))
print('SCNN constructing...')
for l in range(len(w_cnn)):
pops_list.append(
construct_layer(cell_params_lif, pops_list[l], L[l + 1][0],
L[l + 1][1], w_cnn[l]))
result = pops_list[-1][0]
result.record(['v', 'spikes']) # new
print('SCNN running...')
p.run((dur_test + silence) * num_test)
spike_result = result.getSpikes(compatible_output=True)
#spike_result = result.get_spike_counts(gather=True) #tuple datta
#spike_result = result.get_data('spikes')
p.end()
print('analysing...')
spike_result_count = count_spikes(spike_result, 10, num_test, dur_test,
silence)
print("spike_result_count : ", spike_result_count)
predict = np.argmax(spike_result_count, axis=0)
print("predict : ", predict)
# prob = np.exp(spike_result_count)/np.sum(np.exp(spike_result_count), axis=0)
return predict, spike_result
def test_replicate_can_replicate():
p1 = pynn.Population(6, pynn.IF_cond_exp(i_offset=10))
p2 = pynn.Population(6, pynn.IF_cond_exp())
p3 = pynn.Population(6, pynn.IF_cond_exp())
l = v.Replicate(p1, (p2, p3), v.ReLU(), weights=(1, 1))
pynn.run(1000)
l.store_spikes()
expected = np.ones((2, 6))
assert np.allclose(expected, l.get_output())
def test_replicate_create():
p1 = pynn.Population(6, pynn.IF_cond_exp())
p2 = pynn.Population(6, pynn.IF_cond_exp())
p3 = pynn.Population(6, pynn.IF_cond_exp())
l = v.Replicate(p1, (p2, p3), v.ReLU(), weights=(1, 1))
pynn.run(1000)
l.store_spikes()
assert l.layer1.input.shape == (6, 0)
assert l.layer2.input.shape == (6, 0)
assert l.get_output().shape == (2, 6)
def test_ticket244():
nest = pyNN.nest
nest.setup(threads=4)
p1 = nest.Population(4, nest.IF_curr_exp())
p1.record('spikes')
poisson_generator = nest.Population(3, nest.SpikeSourcePoisson(rate=1000.0))
conn = nest.OneToOneConnector()
syn = nest.StaticSynapse(weight=1.0)
nest.Projection(poisson_generator, p1.sample(3), conn, syn, receptor_type="excitatory")
nest.run(15)
p1.get_data()
def two_neuron_example(
current=1000.0,
time_simulation=2000.,
weight=0.4,
neuron_parameters={
'v_rest' : -65.0,
'cm' : 0.1,
'tau_m' : 1.0,
'tau_refrac' : 2.0,
'tau_syn_E' : 10.0,
'tau_syn_I' : 10.0,
'i_offset' : 0.0,
'v_reset' : -65.0,
'v_thresh' : -50.0,
},
):
"""
Connects to neurons with corresponding parameters.
The first is stimulated via current injection while the second receives
the other one's spikes.
"""
sim.setup(timestep=0.1, min_delay=0.1)
pulse = sim.DCSource(amplitude=current, start=0.0, stop=time_simulation)
pre = sim.Population(1, sim.IF_curr_exp(**neuron_parameters))
post = sim.Population(1, sim.IF_curr_exp(**neuron_parameters))
pre.record('spikes')
post.record('spikes')
sim.Projection(pre, post, connector=sim.OneToOneConnector(),
synapse_type=sim.StaticSynapse(weight=weight),
receptor_type='excitatory')
pulse.inject_into(pre)
sim.run(time_simulation)
# rates in Hz
rate_pre = len(pre.get_data('spikes').segments[0].spiketrains[0])\
/ time_simulation * 1000.
rate_post = len(post.get_data('spikes').segments[0].spiketrains[0])\
/ time_simulation * 1000.
sim.end()
return rate_pre, rate_post
def loop():
for device_instance in Interfaces.DeviceMeta._instances:
device_instance._create_device()
print "Entered loop"
i = 0
while(True):
sim.run(20.0)
Observers.Observer.notify()
Setters.Setter.notify()
SimulatorPorts.RPCPort.execute()
for pop_view in population_register.values():
pass
#print pop_view.meanSpikeCount()
#print 'amplitude', pop_view.get_data().segments[0].filter(name='v')
#print nest.GetStatus(map(int, [pop_view.all_cells[0]]), 'V_m')
i += 1
sim.end()
def test_record_native_model():
if not have_nest:
raise SkipTest
nest = pyNN.nest
from pyNN.random import RandomDistribution
init_logging(logfile=None, debug=True)
nest.setup()
parameters = {'tau_m': 17.0}
n_cells = 10
p1 = nest.Population(n_cells, nest.native_cell_type("ht_neuron")(**parameters))
p1.initialize(V_m=-70.0, Theta=-50.0)
p1.set(theta_eq=-51.5)
#assert_arrays_equal(p1.get('theta_eq'), -51.5*numpy.ones((10,)))
assert_equal(p1.get('theta_eq'), -51.5)
print(p1.get('tau_m'))
p1.set(tau_m=RandomDistribution('uniform', low=15.0, high=20.0))
print(p1.get('tau_m'))
current_source = nest.StepCurrentSource(times=[50.0, 110.0, 150.0, 210.0],
amplitudes=[0.01, 0.02, -0.02, 0.01])
p1.inject(current_source)
p2 = nest.Population(1, nest.native_cell_type("poisson_generator")(rate=200.0))
print("Setting up recording")
p2.record('spikes')
p1.record('V_m')
connector = nest.AllToAllConnector()
syn = nest.StaticSynapse(weight=0.001)
prj_ampa = nest.Projection(p2, p1, connector, syn, receptor_type='AMPA')
tstop = 250.0
nest.run(tstop)
vm = p1.get_data().segments[0].analogsignals[0]
n_points = int(tstop / nest.get_time_step()) + 1
assert_equal(vm.shape, (n_points, n_cells))
assert vm.max() > 0.0 # should have some spikes
def scnn_test(l_cnn, w_cnn, num_test, test, max_rate, dur_test, silence):
p.setup(timestep=1.0, min_delay=1.0, max_delay=3.0)
L = l_cnn
random.seed(0)
input_size = L[0][1]
pops_list = []
pops_list.append(init_inputlayer(input_size, test[:num_test, :], max_rate, dur_test, silence))
for l in range(len(w_cnn)):
pops_list.append(construct_layer(pops_list[l], L[l+1][0], L[l+1][1], w_cnn[l]))
result = pops_list[-1][0]
result.record()
p.run((dur_test+silence)*num_test)
spike_result = result.getSpikes(compatible_output=True)
p.end()
spike_result_count = count_spikes(spike_result, 10, num_test, dur_test, silence)
predict = np.argmax(spike_result_count, axis=0)
# prob = np.exp(spike_result_count)/np.sum(np.exp(spike_result_count), axis=0)
return predict
def test_record_native_model():
nest = pyNN.nest
from pyNN.random import RandomDistribution
from pyNN.utility import init_logging
init_logging(logfile=None, debug=True)
nest.setup()
parameters = {'Tau_m': 17.0}
n_cells = 10
p1 = nest.Population(n_cells, nest.native_cell_type("ht_neuron"), parameters)
p1.initialize('V_m', -70.0)
p1.initialize('Theta', -50.0)
p1.set('Theta_eq', -51.5)
assert_equal(p1.get('Theta_eq'), [-51.5]*10)
print p1.get('Tau_m')
p1.rset('Tau_m', RandomDistribution('uniform', [15.0, 20.0]))
print p1.get('Tau_m')
current_source = nest.StepCurrentSource({'times' : [50.0, 110.0, 150.0, 210.0],
'amplitudes' : [0.01, 0.02, -0.02, 0.01]})
p1.inject(current_source)
p2 = nest.Population(1, nest.native_cell_type("poisson_generator"), {'rate': 200.0})
print "Setting up recording"
p2.record()
p1._record('V_m')
connector = nest.AllToAllConnector(weights=0.001)
prj_ampa = nest.Projection(p2, p1, connector, target='AMPA')
tstop = 250.0
nest.run(tstop)
n_points = int(tstop/nest.get_time_step()) + 1
assert_equal(p1.recorders['V_m'].get().shape, (n_points*n_cells, 3))
id, t, v = p1.recorders['V_m'].get().T
assert v.max() > 0.0 # should have some spikes
def compute(self, data, learn=True):
"""Perform the actual computation"""
timestep = self.parameters.config.timestep
# run simulation
for i, d in enumerate(data):
t = pynn.get_current_time()
d = d.astype(np.int32)
activity = np.array(self.calculate_activity([d]))
train = np.ndarray((np.sum(activity), 2))
pos = 0
for j in range(len(self.stimulus)):
spikes = np.sort(
np.random.normal(1.0 + t, 0.01, activity[0][j]))
train[pos:pos + activity[0][j], :] = np.vstack(
[np.ones(spikes.size) * j, spikes]).T
pos += activity[0][j]
for j, s in enumerate(self.stimulus):
s.spike_times = train[train[:, 0] == j, 1]
pynn.run(timestep)
# extract spikes and calculate activity
spikes = self.columns.getSpikes()
mask = (spikes[:, 1] > t) & (spikes[:, 1] < t + timestep)
active_columns = np.unique(spikes[mask, 0]).astype(np.int32)
yield active_columns
if learn > 0:
# wake up, school's starting in five minutes!
c = np.zeros(self.permanences.shape[0], dtype=np.bool)
c[active_columns] = 1
d = d.astype(np.bool)
self.permanences[np.outer(c, d)] += 0.01
self.permanences[np.outer(c, np.invert(d))] -= 0.01
self.permanences = np.minimum(np.maximum(self.permanences, 0),
1)
if type(learn) == int:
learn -= 1
def main():
# setup timestep of simulation and minimum and maximum synaptic delays
setup(timestep=simulationTimestep, min_delay=minSynapseDelay, max_delay=maxSynapseDelay, threads=4)
# create a spike sources
retinaLeft = createSpikeSource("Retina Left")
retinaRight = createSpikeSource("Retina Right")
# create network and attach the spike sources
network = createCooperativeNetwork(retinaLeft=retinaLeft, retinaRight=retinaRight)
# run simulation for time in milliseconds
print "Simulation started..."
run(simulationTime)
print "Simulation ended."
# plot results
plotSimulationResults(network, 1, False)
# finalise program and simulation
end()
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
def compute(self, data, learn=True):
"""Perform the actual computation"""
timestep = self.parameters.config.timestep
# run simulation
for i, d in enumerate(data):
t = pynn.get_current_time()
d = d.astype(np.int32)
activity = np.array(self.calculate_activity([d]))
train = np.ndarray((np.sum(activity), 2))
pos = 0
for j in range(len(self.stimulus)):
spikes = np.sort(np.random.normal(1.0 + t, 0.01, activity[0][j]))
train[pos:pos+activity[0][j],:] = np.vstack([np.ones(spikes.size)*j, spikes]).T
pos += activity[0][j]
for j, s in enumerate(self.stimulus):
s.spike_times = train[train[:,0] == j,1]
pynn.run(timestep)
# extract spikes and calculate activity
spikes = self.columns.getSpikes()
mask = (spikes[:,1] > t) & (spikes[:,1] < t + timestep)
active_columns = np.unique(spikes[mask,0]).astype(np.int32)
yield active_columns
if learn > 0:
# wake up, school's starting in five minutes!
c = np.zeros(self.permanences.shape[0], dtype=np.bool)
c[active_columns] = 1
d = d.astype(np.bool)
self.permanences[np.outer(c, d)] += 0.01
self.permanences[np.outer(c, np.invert(d))] -= 0.01
self.permanences = np.minimum(np.maximum(self.permanences, 0), 1)
if type(learn) == int:
learn -= 1
def test_column_input():
"""
Tests whether all neurons receive the same feedforward input from
common proximal dendrite.
"""
LOG.info('Testing column input...')
# reset the simulator
sim.reset()
column = Column.Column()
sim.run(1000)
spikes = column.FetchSpikes()
print('Spikes before: {}'.format(spikes))
# now stream some input into the column
column.SetFeedforwardDendrite(1000.0)
sim.run(1000)
spikes = column.FetchSpikes().segments[0]
print('Spikes after: {}'.format(spikes))
LOG.info('Test complete.')
def test_encoder_rate_1():
"""
Checks if encoder is properly encoding provided values.
"""
encoder = ScalarEncoder.ScalarEncoder(
size=10, width=1, min_val=0, max_val=10)
encoder.encode(5.0)
sim.run(100)
rate = encoder.population.getSpikes()
voltages = encoder.population.get_v().segments[0]
pdb.set_trace()
plot_signal(voltages, 1)
# get index of maximum rate neuron
idx_max = np.argmax(rate)
LOG.info(rate)
LOG.info('Max firing rate: {}'.format(idx_max))
assert idx_max == 4 # indexing starts from zero
def _run_microcircuit(plot_filename, conf):
import plotting
import logging
simulator = conf['simulator']
# we here only need nest as simulator, simulator = 'nest'
import pyNN.nest as sim
# prepare simulation
logging.basicConfig()
# extract parameters from config file
master_seed = conf['params_dict']['nest']['master_seed']
layers = conf['layers']
pops = conf['pops']
plot_spiking_activity = conf['plot_spiking_activity']
raster_t_min = conf['raster_t_min']
raster_t_max = conf['raster_t_max']
frac_to_plot = conf['frac_to_plot']
record_corr = conf['params_dict']['nest']['record_corr']
tau_max = conf['tau_max']
# Numbers of neurons from which to record spikes
n_rec = helper_functions.get_n_rec(conf)
sim.setup(**conf['simulator_params'][simulator])
if simulator == 'nest':
n_vp = sim.nest.GetKernelStatus('total_num_virtual_procs')
if sim.rank() == 0:
print 'n_vp: ', n_vp
print 'master_seed: ', master_seed
sim.nest.SetKernelStatus({'print_time': False,
'dict_miss_is_error': False,
'grng_seed': master_seed,
'rng_seeds': range(master_seed + 1,
master_seed + n_vp + 1),
'data_path': conf['system_params'] \
['output_path']})
import network
# result of export-files
results = []
# create network
start_netw = time.time()
n = network.Network(sim)
# contains the GIDs of the spike detectors and voltmeters needed for
# retrieving filenames later
device_list = n.setup(sim, conf)
end_netw = time.time()
if sim.rank() == 0:
print 'Creating the network took ', end_netw - start_netw, ' s'
# simulate
if sim.rank() == 0:
print "Simulating..."
start_sim = time.time()
sim.run(conf['simulator_params'][simulator]['sim_duration'])
end_sim = time.time()
if sim.rank() == 0:
print 'Simulation took ', end_sim - start_sim, ' s'
# extract filename from device_list (spikedetector/voltmeter),
# gid of neuron and thread. merge outputs from all threads
# into a single file which is then added to the task output.
for dev in device_list:
label = sim.nest.GetStatus(dev)[0]['label']
gid = sim.nest.GetStatus(dev)[0]['global_id']
# use the file extension to distinguish between spike and voltage
# output
extension = sim.nest.GetStatus(dev)[0]['file_extension']
if extension == 'gdf': # spikes
data = np.empty((0, 2))
elif extension == 'dat': # voltages
data = np.empty((0, 3))
for thread in xrange(conf['simulator_params']['nest']['threads']):
filenames = glob.glob(conf['system_params']['output_path']
+ '%s-*%d-%d.%s' % (label, gid, thread, extension))
assert(
len(filenames) == 1), 'Multiple input files found. Use a clean output directory.'
data = np.vstack([data, np.loadtxt(filenames[0])])
# delete original files
os.remove(filenames[0])
order = np.argsort(data[:, 1])
data = data[order]
outputfile_name = 'collected_%s-%d.%s' % (label, gid, extension)
outputfile = open(outputfile_name, 'w')
# the outputfile should have same format as output from NEST.
# i.e., [int, float] for spikes and [int, float, float] for voltages,
# hence we write it line by line and assign the corresponding filetype
if extension == 'gdf': # spikes
for line in data:
outputfile.write('%d\t%.3f\n' % (line[0], line[1]))
outputfile.close()
filetype = 'application/vnd.juelich.nest.spike_times'
elif extension == 'dat': # voltages
for line in data:
outputfile.write(
'%d\t%.3f\t%.3f\n' % (line[0], line[1], line[2]))
outputfile.close()
filetype = 'application/vnd.juelich.nest.analogue_signal'
res = (outputfile_name, filetype)
results.append(res)
if record_corr and simulator == 'nest':
start_corr = time.time()
if sim.nest.GetStatus(n.corr_detector, 'local')[0]:
print 'getting count_covariance on rank ', sim.rank()
cov_all = sim.nest.GetStatus(
n.corr_detector, 'count_covariance')[0]
delta_tau = sim.nest.GetStatus(n.corr_detector, 'delta_tau')[0]
cov = {}
for target_layer in np.sort(layers.keys()):
for target_pop in pops:
target_index = conf['structure'][target_layer][target_pop]
cov[target_index] = {}
for source_layer in np.sort(layers.keys()):
for source_pop in pops:
source_index = conf['structure'][
source_layer][source_pop]
cov[target_index][source_index] = \
np.array(list(
cov_all[target_index][source_index][::-1])
+ list(cov_all[source_index][target_index][1:]))
f = open(conf['system_params'][
'output_path'] + '/covariances.dat', 'w')
print >>f, 'tau_max: ', tau_max
print >>f, 'delta_tau: ', delta_tau
print >>f, 'simtime: ', conf['simulator_params'][
simulator]['sim_duration'], '\n'
for target_layer in np.sort(layers.keys()):
for target_pop in pops:
target_index = conf['structure'][target_layer][target_pop]
for source_layer in np.sort(layers.keys()):
for source_pop in pops:
source_index = conf['structure'][
source_layer][source_pop]
print >>f, target_layer, target_pop, '-', source_layer, source_pop
print >>f, 'n_events_target: ', sim.nest.GetStatus(
n.corr_detector, 'n_events')[0][target_index]
print >>f, 'n_events_source: ', sim.nest.GetStatus(
n.corr_detector, 'n_events')[0][source_index]
for i in xrange(len(cov[target_index][source_index])):
print >>f, cov[target_index][source_index][i]
print >>f, ''
f.close()
# add file covariances.dat into bundle
res_cov = ('covariances.dat',
'text/plain')
results.append(res_cov)
end_corr = time.time()
print "Writing covariances took ", end_corr - start_corr, " s"
if plot_spiking_activity and sim.rank() == 0:
plotting.plot_raster_bars(raster_t_min, raster_t_max, n_rec,
frac_to_plot, n.pops,
conf['system_params']['output_path'],
plot_filename, conf)
res_plot = (plot_filename, 'image/png')
results.append(res_plot)
sim.end()
return results
import pyNN.nest as sim
parameters = {
u'E_L': 0.0,
u'I_e': 0.9, # 用这个参数来表示leaky
u'V_reset': 0.0,
u'V_th': 0.5,
u't_ref': .0,
}
sim.setup(timestep=01.0)
nt = sim.native_cell_type('iaf_psc_delta_xxq')
n = sim.Population(1, nt(**parameters))
s = sim.Population(1, sim.SpikeSourceArray())
s[0].spike_times = [10, 15, 20, 30, 40]
p = sim.Projection(s, n, sim.FromListConnector([(0, 0, 0.00025, 0.01)]))
# p1 = sim.Projection(n, n, sim.FromListConnector([(0, 0, 0.00025, 1.0)]))
n.record('V_m')
n.record('V_m')
sim.initialize(n, V_m=0.)
sim.run(128.0)
vtrace = n.get_data(clear=True).segments[0].filter(name='V_m')[0]
print p.get(['weight'], format='array')
plt.figure()
plt.plot(vtrace.times, vtrace, 'o')
plt.ylim([0, 0.6])
plt.show()
sim.end()
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
'e_rev_leak': ELeak,
'e_rev_E' : EIs[1],
'e_rev_I' : EIs[2],
'tau_syn_E' : 0.2,
'tau_syn_I' : 2.0,
'i_offset' : 0.0,
}
#vs = np.linspace(-75.0, EIs[0] + 5, 3)
vs = [-80.0, -61.0, -60.0]
neurons = [sim.create(sim.HH_cond_exp(**cellparams)) for _ in vs]
for i in xrange(len(vs)):
neurons[i].record(["v"])
neurons[i].initialize(v=vs[i])
sim.run(tEnd)
fig = plt.figure(figsize=(cm2inch(12.4), cm2inch(7)))
ax = fig.add_subplot(111)
#cmap = plt.cm.rainbow
#cmap = colors.LinearSegmentedColormap.from_list('blues', ['#729fcf', '#3465a4',
# '#193a6b'])
lss = ['--', ':', '-']
#colors = iter(cmap(np.linspace(0, 1, len(vs))))
colors = iter(['#204a87'] * 3)
for i in xrange(len(vs)):
data = neurons[i].get_data()
signal_names = [s.name for s in data.segments[0].analogsignalarrays]
vm = data.segments[0].analogsignalarrays[signal_names.index('v')]
ax.plot(vm.times, vm, lss[i], color=next(colors),
connSTDP = pynn.nest.FindConnections(measure)
weightList = []
aCausalList = []
aAnticausalList = []
timeGrid = np.arange(0, runtime + timeStep / 2.0, timeStep)
#run simulation step-wise to record charge on "capacitors" and discrete synaptic weight
for timeNow in timeGrid:
weightList.append(prj.getWeights()[0])
for i in range(len(connSTDP)): #read out "capacitors"
if pynn.nest.GetStatus([connSTDP[i]])[0]['synapse_model'].find(synapseModel) > -1:
aCausalList.append(pynn.nest.GetStatus([connSTDP[i]])[0]['a_causal'])
aAnticausalList.append(pynn.nest.GetStatus([connSTDP[i]])[0]['a_acausal'])
if not timeNow == timeGrid[-1]:
pynn.run(timeStep)
spikes = neuron.getSpikes()
#membrane = neuron.get_v() #for debugging
print 'presynaptic spikes (static synapse)'
print stimSpikes
print 'presynaptic spikes (plastic synapse)'
print measureSpikes
print 'postsynaptic spikes'
print spikes
pynn.end()
#visualization of results
import matplotlib.pyplot as plt
def run(a_state):
output_base = "out/"
spike_count_filename = "gpi_spike_count.dat"
weight_filename = conn_filename # filename, from which the cortex - striatum connections are read
spike_count_full_filename = output_base + spike_count_filename
#active_state = int(sys.argv[1])
active_state = a_state
#Model of the basal ganglia D1 and D1 pathways. States and actions are populations coded.
pyNN.utility.init_logging(None, debug=True)
sim.setup(time_step)
# cell class for all neurons in the network
# (on HMF can be one of IF_cond_exp, EIF_cond_exp_isfa_ista)
cellclass = sim.IF_cond_exp
# #############
# POPULATIONS
# #############
#CORTEX input population: N states, poisson inputs
#?assemblies of m_actions populations or dictionnary of populations?
#STRIATUM 2 populations of M actions, D1 and D2
#GPi/SNr 1 population of M actions, baseline firing rate driven by external poisson inputs
cortex = [
sim.Population(n_cortex_cells, cellclass, neuron_parameters, label="CORTEX_{}".format(i))
for i in xrange(n_states)]
cortex_assembly = sim.Assembly(
*cortex,
label="CORTEX")
# independent Poisson input to cortex populations.
# /active_state/ determines, which population receives
# a different firing rate
cortex_input = []
for i in xrange(n_states):
if i == active_state:
rate = active_state_rate
else:
rate = inactive_state_rate
new_input = sim.Population(
n_cortex_cells,
sim.SpikeSourcePoisson,
{'rate': rate},
label="STATE_INPUT_" + str(i))
sim.Projection(
new_input,
cortex[i],
sim.OneToOneConnector(),
sim.StaticSynapse(weight=cortex_input_weight, delay=cortex_input_delay)
)
cortex_input.append(new_input)
#print 'cortex ok'
# striatum:
# exciatatory populations
striatum_d1 = [
sim.Population(n_msns, cellclass, neuron_parameters, label="D1_{}".format(i))
for i in xrange(m_actions)]
# inhibitory populations
striatum_d2 = [
sim.Population(n_msns, cellclass, neuron_parameters, label="D2_{}".format(i))
for i in xrange(m_actions)]
# Striatum D2->D2 and D1->D1 lateral inhibition
for lat_inh_source in xrange(m_actions):
for lat_inh_target in xrange(m_actions):
if lat_inh_source == lat_inh_target:
continue
sim.Projection(
striatum_d1[lat_inh_source],
striatum_d1[lat_inh_target],
sim.FixedProbabilityConnector(
d1_lat_inh_prob),
sim.StaticSynapse(
weight=d1_lat_inh_weight,
delay=d1_lat_inh_delay),
receptor_type="inhibitory",
label="d1_lateral_inhibition_{}_{}".format(
lat_inh_source, lat_inh_target))
sim.Projection(
striatum_d2[lat_inh_source],
striatum_d2[lat_inh_target],
sim.FixedProbabilityConnector(
d2_lat_inh_prob),
sim.StaticSynapse(
weight=d2_lat_inh_weight,
delay=d2_lat_inh_delay),
receptor_type="inhibitory",
label="d2_lateral_inhibition_{}_{}".format(
lat_inh_source, lat_inh_target))
striatum_assembly = sim.Assembly(
*(striatum_d1 + striatum_d2),
label="STRIATUM")
#gids_cortex= []
#gids_d1= []
#gids_d2= []
#for s in xrange(n_states):
# gids_cortex.append([gid for gid in cortex_assembly.get_population("CORTEX_"+str(s)).all()])
#for a in xrange(m_actions):
# gids_d1.append([gid1 for gid1 in striatum_assembly.get_population("D1_"+str(a)).all()])
# gids_d2.append([gid2 for gid2 in striatum_assembly.get_population("D2_"+str(a)).all()])
#for i in xrange(0,3):
# print i, 'len cortex ', len(gids_cortex[i]), 'unique ', len(np.unique(gids_cortex[i]))
# print i, 'len d1', len(gids_d1[i]), 'unique ', len(np.unique(gids_d1[i]))
# print i, 'len d2', len(gids_d2[i]), 'unique ', len(np.unique(gids_d2[i]))
#print "striatum ok"
#for i in xrange(0,3):
# print np.unique(gids_cortex[i])
# gids_cortex[i][:]-=3
#if init:
# init_w(gids_cortex, gids_d1, gids_d2)
# cortex - striatum connection, all-to-all using loaded weights
cs = sim.Projection(
cortex_assembly,
striatum_assembly,
#sim.AllToAllConnector(),
#sim.StaticSynapse(
# weight=wd1,
# delay=ctx_strd1_delay))
sim.FromFileConnector(
weight_filename))
gpi = [
sim.Population(n_gpi, cellclass, neuron_parameters,
label="GPI_{}".format(i))
for i in xrange(m_actions)
]
gpi_assembly = sim.Assembly(
*gpi,
label="GPi")
# external Poisson input to GPi
gpi_input = sim.Population(
m_actions * n_gpi,
sim.SpikeSourcePoisson,
dict(
duration=sim_duration,
rate=gpi_external_rate,
start=0.),
label="GPI_EXT_INPUT")
sim.Projection(
gpi_input,
gpi_assembly,
sim.OneToOneConnector(),
sim.StaticSynapse(
weight=gpi_external_weight,
delay= gpi_external_delay))
# striatum - gpi connections
for i in xrange(m_actions):
gpi_p = sim.Projection(
striatum_d1[i],
gpi[i],
sim.FixedProbabilityConnector(d1_gpi_prob),
sim.StaticSynapse( weight=d1_gpi_weight, delay = d1_gpi_delay))
sim.Projection(
striatum_d2[i],
gpi[i],
sim.FixedProbabilityConnector(d2_gpi_prob),
sim.StaticSynapse(weight=d2_gpi_weight, delay=d2_gpi_delay),
#target="inhibitory")
receptor_type="inhibitory")
#print gpi_p.get('weight', format='list')
cortex_assembly.record('spikes')
striatum_assembly.record('spikes')
gpi_assembly.record('spikes')
#print 'sim start'
sim.run(sim_duration)
sim.end()
label = "CORTEX_0"
#print 'cortex get pop', cortex_assembly.get_population(label)
#print 'cortex describe', cortex_assembly.describe()
#cortex_assembly.write_data("spikes")
#cortex_assembly.get_population(label).write_data("spikes")
#spikes = gpi_assembly #get_data("spikes", gather=True)
# print "getdata spikes", spikes
# print 'spikes.segment', spikes.segments
#print 'spikes.segments.SpikeTrains', spikes.segments.spike
#save_spikes(cortex_assembly, output_base, "cortex.dat")
#save_spikes(striatum_d1, output_base, "striatum_d1.dat")
#save_spikes(striatum_d2, output_base, "striatum_d2.dat")
#save_spikes(gpi, output_base, "gpi.dat")
#output_rates = np.array(
# [len(i.getSpikes()) for i in gpi])
#np.savetxt(spike_count_full_filename, output_rates)
# for seg in cortex_assembly.segments:
# print("Analyzing segment %d" % seg.index)
# stlist = [st - st.t_start for st in seg.spiketrains]
# plt.figure()
# count, bins = np.histogram(stlist)
# plt.bar(bins[:-1], count, width=bins[1] - bins[0])
# plt.title("PSTH in segment %d" % seg.index)
cortex_mean_spikes = np.zeros(n_states)
gpi_mean_spikes = np.zeros(m_actions)
d1_mean_spikes = np.zeros(m_actions)
d2_mean_spikes = np.zeros(m_actions)
for i in xrange(n_states):
cortex_mean_spikes[i] = cortex_assembly.get_population("CORTEX_"+str(i)).mean_spike_count()
for i in xrange(m_actions):
gpi_mean_spikes[i] = gpi_assembly.get_population("GPI_"+str(i)).mean_spike_count()
d1_mean_spikes[i] = striatum_assembly.get_population("D1_"+str(i)).mean_spike_count()
d2_mean_spikes[i] = striatum_assembly.get_population("D2_"+str(i)).mean_spike_count()
print 'CORTEX ', cortex_mean_spikes
print 'D1', d1_mean_spikes
print 'D2', d2_mean_spikes
return gpi_mean_spikes
#!/usr/bin/env python
import faulthandler
from music_wizard.pynn import XmlFactory, Factory
import music
import pyNN.nest as sim
sim.setup()
music_setup = music.Setup()
xml = music_setup.config('xml')
model_factory = Factory.PyNNProxyFactory(sim, music_setup, acc_latency=10.0)
connector_factory = Factory.PyNNConnectorFactory(sim)
##########
# Load and execute brain file
import brainfile
##########
population_dict = brainfile.__population_views
proxy_factory = XmlFactory.ProxyFactory("app2", connector_factory, model_factory, population_dict)
with open(xml, 'r') as xml_stream:
proxys = proxy_factory.create_proxys(xml_stream.read())
for i in xrange(20):
sim.run(20.0)
def simulate(self):
# reset detectors before simulating the next step
nest.nest.SetStatus(self.network.detectors.values(), 'n_events', 0)
nest.run(self.simduration)
nest.end()
import pyNN.nest as sim
sim.nest.Install("coronetmodule")
sim.setup()
sim.nest.SetKernelStatus({"dict_miss_is_error": False})
coronet_neuron = sim.native_cell_type("coronet_neuron")
p1 = sim.Population(10, coronet_neuron)
s1 = sim.Population(10, sim.SpikeSourcePoisson, {"rate": 20})
s2 = sim.Population(10, sim.SpikeSourcePoisson, {"rate": 20})
sim.Projection(s1, p1, sim.OneToOneConnector(weights=0.02), target="EX")
sim.Projection(s2, p1, sim.OneToOneConnector(weights=0.01), target="IN")
p1.initialize("V_m", -76.0) # this works as expected
p1.record()
p1._record("V_m") # ugly
sim.run(1000)
id, t, v = p1.recorders["V_m"].get().T # ugly
import pylab as pl
import numpy as np
pl.figure()
sl = p1.getSpikes()
pl.plot(sl[:, 1], sl[:, 0], ".")
pl.figure()
id_is_0 = np.where(id == 0)
pl.plot(t[id_is_0], v[id_is_0])
pl.show()
def run_retina(params):
"""Run the retina using the specified parameters."""
print "Setting up simulation"
timer = Timer()
timer.start() # start timer on construction
pyNN.setup(timestep=params['dt'], max_delay=params['syn_delay'], threads=params['threads'], rng_seeds=params['kernelseeds'])
N = params['N']
phr_ON = pyNN.Population((N, N), pyNN.native_cell_type('dc_generator')())
phr_OFF = pyNN.Population((N, N), pyNN.native_cell_type('dc_generator')())
noise_ON = pyNN.Population((N, N), pyNN.native_cell_type('noise_generator')(mean=0.0, std=params['noise_std']))
noise_OFF = pyNN.Population((N, N), pyNN.native_cell_type('noise_generator')(mean=0.0, std=params['noise_std']))
phr_ON.set(start=params['simtime']/4, stop=params['simtime']/4*3,
amplitude=params['amplitude'] * params['snr'])
phr_OFF.set(start=params['simtime']/4, stop=params['simtime']/4*3,
amplitude=-params['amplitude'] * params['snr'])
# target ON and OFF populations
v_init = params['parameters_gc'].pop('Vinit')
out_ON = pyNN.Population((N, N), pyNN.native_cell_type('iaf_cond_exp_sfa_rr')(**params['parameters_gc']))
out_OFF = pyNN.Population((N, N), pyNN.native_cell_type('iaf_cond_exp_sfa_rr')(**params['parameters_gc']))
out_ON.initialize(v=v_init)
out_OFF.initialize(v=v_init)
#print "Connecting the network"
retina_proj_ON = pyNN.Projection(phr_ON, out_ON, pyNN.OneToOneConnector())
retina_proj_ON.set(weight=params['weight'])
retina_proj_OFF = pyNN.Projection(phr_OFF, out_OFF, pyNN.OneToOneConnector())
retina_proj_OFF.set(weight=params['weight'])
noise_proj_ON = pyNN.Projection(noise_ON, out_ON, pyNN.OneToOneConnector())
noise_proj_ON.set(weight=params['weight'])
noise_proj_OFF = pyNN.Projection(noise_OFF, out_OFF, pyNN.OneToOneConnector())
noise_proj_OFF.set(weight=params['weight'])
out_ON.record('spikes')
out_OFF.record('spikes')
# reads out time used for building
buildCPUTime = timer.elapsedTime()
print "Running simulation"
timer.start() # start timer on construction
pyNN.run(params['simtime'])
simCPUTime = timer.elapsedTime()
out_ON_DATA = out_ON.get_data().segments[0]
out_OFF_DATA = out_OFF.get_data().segments[0]
print "\nRetina Network Simulation:"
print(params['description'])
print "Number of Neurons : ", N**2
print "Output rate (ON) : ", out_ON.mean_spike_count(), \
"spikes/neuron in ", params['simtime'], "ms"
print "Output rate (OFF) : ", out_OFF.mean_spike_count(), \
"spikes/neuron in ", params['simtime'], "ms"
print "Build time : ", buildCPUTime, "s"
print "Simulation time : ", simCPUTime, "s"
return out_ON_DATA, out_OFF_DATA
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]])
cells.recorders["GABAa_g"].write("Results/nineml_neuron.g_gabaA", filter=[cells[0]])
cells.recorders["GABAb_g"].write("Results/nineml_neuron.g_gagaB", filter=[cells[0]])
t = cells.recorders["iaf_V"].get()[:, 1]
v = cells.recorders["iaf_V"].get()[:, 2]
gInhA = cells.recorders["GABAa_g"].get()[:, 2]
gInhB = cells.recorders["GABAb_g"].get()[:, 2]
gExc = cells.recorders["AMPA_g"].get()[:, 2]
import pylab
def test_run_0(self, ): # see https://github.com/NeuralEnsemble/PyNN/issues/191
sim.setup(timestep=0.123, min_delay=0.246)
sim.run(0)
self.assertEqual(sim.get_current_time(), 0.0)
for j in range(n_j):
l.append((i, j, W[i, j] * w0, delay))
con = sim.FromListConnector(l, column_names=["weight", "delay"])
return con
connections_hid = sim.Projection(p_in, p_hid, W_to_connector(W_hid))
connections_out = sim.Projection(p_hid, p_out, W_to_connector(W_out))
# Record spikes
p_in.record("spikes")
p_hid.record("spikes")
p_out.record("spikes")
t = sim.run(T)
# Get recorded data and plot
data_block = p_hid.get_data()
print(data_block)
fig, ax_list = plt.subplots(3)
for k, p, ax in zip(range(3), [p_in, p_hid, p_out], ax_list):
ax.scatter(p.get_data()["spikes"])
ax.set_xlim[0, T]
plt.show()