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.syn1 = sim.StaticSynapse(weight=0.123, delay=0.5)
self.syn2 = sim.StaticSynapse(weight=0.456, delay=0.4)
self.random_connect = sim.FixedNumberPostConnector(n=2)
self.all2all = sim.AllToAllConnector()
def setUp(self):
sim.setup()
self.p = sim.Population(
4,
sim.IF_cond_exp(
**{
'tau_m': 12.3,
'cm': lambda i: 0.987 + 0.01 * i,
'i_offset': numpy.array([-0.21, -0.20, -0.19, -0.18])
}))
"""
"""
from plot_helper import plot_current_source
import pyNN.neuron as sim
sim.setup()
population = sim.Population(30, sim.IF_cond_exp(tau_m=10.0))
population[0:1].record_v()
noise = sim.NoisyCurrentSource(mean=1.5, stdev=1.0, start=50.0, stop=450.0,
dt=1.0)
population.inject(noise)
noise._record()
sim.run(500.0)
t, i_inj = noise._get_data()
v = population.get_data().segments[0].analogsignals[0]
plot_current_source(t, i_inj, v,
v_range=(-66, -48),
v_ticks=(-65, -60, -55, -50),
i_range=(-3, 5),
i_ticks=range(-2, 6, 2),
t_range=(0, 500))
neuronParameters = {
'tau_m': tau_m,
'cm': cm,
'v_rest': v_rest,
'v_thresh': v_thresh,
'tau_syn_E': tau_syn_E,
'tau_syn_I': tau_syn_I,
'e_rev_E': e_rev_E,
'e_rev_I': e_rev_I,
'v_reset': v_reset,
'tau_refrac': tau_refrac,
'i_offset': i_offset
}
cell_type = sim.IF_cond_exp(**neuronParameters)
input = sim.Population(20, sim.SpikeSourcePoisson(rate=50.0))
output = sim.Population(25, cell_type)
rand_distr = RandomDistribution('uniform', (v_reset, v_thresh), rng=NumpyRNG(seed=85524))
output.initialize(v=rand_distr)
stdp = sim.STDPMechanism(weight_dependence=sim.AdditiveWeightDependence(w_min=0.0, w_max=0.1),
timing_dependence=sim.Vogels2011Rule(eta=0.0, rho=1e-3),
def setUp(self):
self.p = sim.Population(2, sim.IF_cond_exp())
self.rec = recording.Recorder(self.p)
self.cells = self.p.all_cells # [MockID(22), MockID(29)]
