def test_generate_positions(self):
n = 1000
s = space.Sphere(2.5)
rs = space.RandomStructure(boundary=s, origin=(1.0, 1.0, 1.0))
positions = rs.generate_positions(n)
assert_equal(positions.shape, (3, n))
for axis in range(2):
assert 3 < max(positions[axis, :]) < 3.5
assert -1 > min(positions[axis, :]) > -1.5
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)
reference="Positions",
save_format='hdf5')
pop_size = 400
cell_params = {
'tau_refrac': 5,
'v_thresh': -50.0,
'v_reset': -65.0,
'i_offset': 0.9,
'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",
import numpy as np
from pyNN.utility import get_script_args
import pyNN.space as space
simulator_name = get_script_args(1)[0]
sim = import_module("pyNN.%s" % simulator_name)
tstop = 500.0
time_step = 0.005
sim.setup(timestep=time_step, debug=True, reference='ConnectionsTest')
sphere1 = space.Sphere(radius=100.0)
struct1 = space.RandomStructure(sphere1, origin=(0.0, 0.0, 0.0))
cell_params = {'tau_refrac':5,'v_thresh':-50.0, 'v_reset':-65.0, 'i_offset': 0.9, 'tau_syn_E' : 2.0, 'tau_syn_I': 5.0}
pop_pre = sim.Population(5, sim.IF_cond_alpha(**cell_params), label="pop_pre",structure=struct1)
pop_pre.record('v')
pop_pre.annotate(radius=5)
pop_pre.annotate(color='0 0.6 0')
sphere2 = space.Sphere(radius=100.0)
struct2 = space.RandomStructure(sphere2, origin=(0.0, 200.0, 0.0))
pop_post = sim.Population(5, sim.IF_cond_alpha(**cell_params), label="pop_post",structure=struct2)
pop_post.record('v')
pop_post.annotate(radius=5)
pop_post.annotate(color='0 0.2 0.6')
pre_selection = sim.PopulationView(pop_pre, np.array([1,3]),label='pre_selection')
# Use CVode to calculate i_membrane_ for fast LFP calculation
cvode = h.CVode()
cvode.active(0)
# Get the second spatial derivative (the segment current) for the collateral
cvode.use_fast_imem(1)
# Set initial values for cell membrane voltages
v_init = -68
# Create random distribution for cell membrane noise current
r_init = RandomDistribution('uniform', (0, Pop_size))
# Create Spaces for STN Population
STN_Electrode_space = space.Space(axes='xy')
STN_space = space.RandomStructure(
boundary=space.Sphere(2000)) # Sphere with radius 2000um
# Generate poisson distributed spike time striatal input
striatal_spike_times = np.load(
'Striatal_Spike_Times.npy') # Load spike times from file
# Generate the cortico-basal ganglia neuron populations
Cortical_Pop = Population(
Pop_size,
Cortical_Neuron_Type(soma_bias_current_amp=0.245),
structure=STN_space,
label='Cortical Neurons') # Better than above (ibias=0.2575)
Interneuron_Pop = Population(Pop_size,
Interneuron_Type(bias_current_amp=0.070),
initial_values={'v': v_init},
label='Interneurons')
'v_rest': 0.0, # (mV)
'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)
