def setUp(self):
sim.setup()
self.p1 = sim.Population(7, sim.IF_cond_exp())
self.p2 = sim.Population(4, sim.IF_cond_exp())
self.p3 = sim.Population(5, sim.IF_curr_alpha())
self.syn_rnd = sim.StaticSynapse(weight=0.123, delay=0.5)
self.syn_a2a = sim.StaticSynapse(weight=0.456, delay=0.4)
self.random_connect = sim.FixedNumberPostConnector(n=2)
self.all2all = sim.AllToAllConnector()
def test():
if not HAVE_H5PY and HAVE_NEST:
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)
# Build the network
sim.setup()
# spike source
#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'])
# http://ec.europa.eu/programmes/horizon2020/en/h2020-section/fet-flagships # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # ---LICENSE-END """ This is just an example of a neural network """ __author__ = 'Georg Hinkel' import pyNN.nest as sim population = sim.Population(3, sim.IF_curr_alpha()) view = population[1:2] assembly = population + view # A list of populations is also possible list = [population, view, [assembly]]
if u >= v_thresh:
spikes.append(1)
else:
spikes.append(0)
# Plot
plt.plot(xs, uvec[:wf])
plt.xlabel("ms")
plt.ylabel("mV")
plt.title("Single LIF Neuron (current)")
# %% PYNN Neuron
sim.setup(timestep=0.1, min_delay=0.1, max_delay=10.0)
IF_sim = sim.Population(1,
sim.IF_curr_alpha(i_offset=1.0),
label="IF_curr_exp")
IF_sim.record('v')
## Parameters
# {'v_rest': -65.0, 'cm': 1.0, 'tau_m': 20.0,
# 'tau_refrac': 0.1, 'tau_syn_E': 0.5, 'tau_syn_I': 0.5,
# 'i_offset': 1.0, 'v_reset': -65.0, 'v_thresh': -50.0}
# Running simulation in MS
sim.run(100.0)
# Data
v_data = IF_sim.get_data()
data = IF_sim.get_data().segments[0]
v = data.filter(name="v")[0]
weightScale = 1.0 # general weight for all connections between neurons
constantInput = 0.5 # input given to the tonic interneuron layer and to the top-down activated segmentation neurons
inputPoisson = 0 # 1 for a poisson spike source input, 0 for a DC current input
cellParams = { # parameters for any neuron in the network
'i_offset': 0.0, # (nA)
'tau_m': 10.0, # (ms)
'tau_syn_E': 2.0, # (ms)
'tau_syn_I': 2.0, # (ms)
'tau_refrac': 2.0, # (ms)
'v_rest': -70.0, # (mV)
'v_reset': -70.0, # (mV)
'v_thresh':
-56.0, # (mV) -55.0 is NEST standard, -56.0 good for the current setup
'cm': 0.25
} # (nF)
cellType = sim.IF_curr_alpha(**cellParams)
connections = {
# Input and LGN
'brightInputToLGN': 0.1, # only useful if inputDC == 1
'darkInputToLGN': 0.1, # only useful if inputDC == 1
'LGN_ToV1Excite': 0.4, # 400.0
'LGN_ToV4Excite': 0.28, # 280.0,
# V1 layers
'V1_6To4Excite': 0.001, # 1.0,
'V1_6To4Inhib': -0.001, # -1.0,
'V1_4To23Excite': 0.5, # 500.0,
'V1_23To6Excite': 0.1, # 100.0,
'V1_ComplexExcite': 0.5, # 500.0,
'V1_ComplexInhib': -0.5, # -500.0,
'V1_FeedbackExcite': 0.5, # 500.0,