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 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
import pyNN.nest as sim
import numpy
__population_views = {}
pop1 = sim.Population(10, sim.IF_cond_alpha())
__population_views['pop1'] = pop1
source = sim.StepCurrentSource(times=[0.1], amplitudes=[.8])
source.inject_into(pop1)
print "Network loaded"
#pop1.sample(10).record('v')
params = phasic_spiking
n = 100
v_init = -64
increments = 1 * np.linspace(dt, 1 + dt, n)
ramps = []
for incr in increments:
val = -0.5
amplitudes = []
for t in range(int(duration / dt)):
amplitudes.append(val)
val += incr
ramps.append(amplitudes)
ramp_inputs = [
sim.StepCurrentSource(times=np.arange(0, int(duration / dt)),
amplitudes=ramp) for ramp in ramps
]
neurons = sim.Population(
n, sim.IF_cond_alpha(tau_m=1)) # sim.Izhikevich(**params))
for i, ramp_input in enumerate(ramp_inputs):
ramp_input.inject_into(neurons[i:i + 1])
neurons.record(['v', 'spikes'])
neurons.initialize(v=v_init, u=-params['b'] * v_init)
# === Run the simulation =====================================================
sim.run(duration)
# === Save the results, optionally plot a figure =============================
