def test_sample(self):
n = 1000
c = space.Cuboid(3, 4, 5)
positions = c.sample(n, numpy.random)
assert_equal(positions.shape, (n, 3))
assert 1 < max(positions[:, 0]) < 1.5, max(positions[:, 0])
assert -1 > min(positions[:, 0]) > -1.5
assert -1.5 > min(positions[:, 1]) > -2.0
assert -2 > min(positions[:, 2]) > -2.5
def __init__(self, model, parameters):
SheetWithMagnificationFactor.__init__(self, model, parameters)
dx, dy = self.cs_2_vf(parameters.sx, parameters.sy)
rs = space.RandomStructure(boundary=space.Cuboid(dx, dy, 0),
origin=(0.0, 0.0, 0.0),
rng=mozaik.pynn_rng)
self.pop = self.sim.Population(
int(parameters.sx * parameters.sy / 1000000 * parameters.density),
getattr(self.model.sim, self.parameters.cell.model),
self.parameters.cell.params,
structure=rs,
initial_values=self.parameters.cell.initial_values,
label=self.name)
def __init__(self, model, parameters):
SheetWithMagnificationFactor.__init__(self, model, parameters)
dx, dy = self.cs_2_vf(parameters.sx, parameters.sy)
rs = space.RandomStructure(boundary=space.Cuboid(dx, dy, 0),
origin=(0.0, 0.0, 0.0),
rng=mozaik.pynn_rng)
self.pop = self.sim.Population(
int(parameters.sx * parameters.sy / 1000000 * parameters.density),
getattr(self.model.sim, self.parameters.cell.model),
self.parameters.cell.params,
structure=rs,
initial_values=self.parameters.cell.initial_values,
label=self.name)
# Forces PyNN to generate the positions to ensure the reproducibility with multiprocessing
self.pop.positions
def __init__(self, model, parameters):
Sheet.__init__(self, model, parameters.sx, parameters.sy, parameters)
logger.info("Creating %s with %d neurons." %
(self.__class__.__name__,
int(parameters.sx * parameters.sy * parameters.density)))
rs = space.RandomStructure(boundary=space.Cuboid(
self.size_x, self.size_y, 0),
origin=(0.0, 0.0, 0.0),
rng=mozaik.pynn_rng)
#rs = space.Grid2D(aspect_ratio=1, dx=parameters.sx/parameters.density, dy=parameters.sy/parameters.density, x0=-parameters.sx/2,y0=-parameters.sy/2,z=0.0)
self.pop = self.sim.Population(
int(parameters.sx * parameters.sy * parameters.density),
getattr(self.model.sim, self.parameters.cell.model),
self.parameters.cell.params,
structure=rs,
initial_values=self.parameters.cell.initial_values,
label=self.name)
def __init__(self, model, parameters):
# from spynnaker8.extra_models import Izhikevich_cond
# import spynnaker8.extra_models as models
SheetWithMagnificationFactor.__init__(self, model, parameters)
dx, dy = self.cs_2_vf(parameters.sx, parameters.sy)
rs = space.RandomStructure(boundary=space.Cuboid(dx, dy, 0),
origin=(0.0, 0.0, 0.0),
rng=mozaik.pynn_rng)
# l4_cortex_inh, l4_cortex_exc: model: EIF_cond_exp_isfa_ista
# Exponential integrate and fire neuron with spike triggered and sub-threshold adaptation currents
# must change to Izhikevich model
self.pop = self.sim.Population(
int(parameters.sx * parameters.sy / 1000000 * parameters.density),
getattr(self.model.sim, self.parameters.cell.model
), # no attribute 'EIF_cond_exp_isfa_ista'
# getattr(models, self.parameters.cell.model),
self.parameters.cell.params,
structure=rs,
initial_values=self.parameters.cell.initial_values,
label=self.name)
def __init__(self, model, parameters):
Sheet.__init__(self, model, parameters.sx, parameters.sy, parameters)
logger.info("Creating *LGN* %s with %d neurons." %
(self.__class__.__name__,
int(parameters.sx * parameters.sy * parameters.density)))
rs = space.RandomStructure(boundary=space.Cuboid(
self.size_x, self.size_y, 0),
origin=(0.0, 0.0, 0.0),
rng=mozaik.pynn_rng)
print("* getattr(self.model.sim, self.parameters.cell.model) ",
getattr(self.model.sim, self.parameters.cell.model))
#rs = space.Grid2D(aspect_ratio=1, dx=parameters.sx/parameters.density, dy=parameters.sy/parameters.density, x0=-parameters.sx/2,y0=-parameters.sy/2,z=0.0)
self.pop = self.sim.Population(
int(parameters.sx * parameters.sy * parameters.density),
getattr(
self.model.sim,
self.parameters.cell.model), # replace with SpikeSourceArray
self.parameters.cell.params,
structure=rs,
initial_values=self.parameters.cell.initial_values,
# cellclass = self.sim.SpikeSourceArray(spike_times=[])) # spike times from nest/lgn output
label=self.name)
'tau_syn_E': 2.0,
'tau_syn_I': 5.0
}
sphere = space.Sphere(radius=100.0)
struct = space.RandomStructure(sphere, origin=(0.0, 100.0, 0.0))
pop_pre = sim.Population(pop_size,
sim.IF_cond_alpha(**cell_params),
label="pop_pre",
structure=struct)
pop_pre.record('v')
pop_pre.annotate(radius=5)
pop_pre.annotate(color='0 0.6 0')
cuboid = space.Cuboid(30, 40, 50)
struct = space.RandomStructure(cuboid, origin=(-200.0, 0.0, -200.0))
pop_post = sim.Population(pop_size,
sim.IF_cond_alpha(**cell_params),
label="pop_post",
structure=struct)
pop_post.record('v')
pop_post.annotate(radius=5)
pop_post.annotate(color='0.2 0.2 1')
sim.run(tstop)
for pop in [pop_pre, pop_post]:
print("Positions of %s: %s" % (pop.label, pop.positions))
'v_reset': 0.0, # (mV)
'v_thresh': 20.0, # (mV)
'cm': 0.5
} # (nF)
RateScale = 1e6
Tstep = 10.0
rngseed = 1240498
width = 1.0
depth = 1.0
height = 6.0
exc_structure = space.RandomStructure(
boundary=space.Cuboid(width, height, depth))
inh_structure = space.RandomStructure(
boundary=space.Cuboid(width, height, depth))
c_lambda = 1.0
exc_connector = DistanceDependentProbabilityConnector('exp(-d*d/(%f*%f))' %
(c_lambda, c_lambda),
weights=0.1,
delays=1.0)
inh_connector = DistanceDependentProbabilityConnector('exp(-d*d/(%f*%f))' %
(c_lambda, c_lambda),
weights=-0.4,
delays=1.0)
