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)]