print(p[0].gbar_Na)
#setting from an array
import numpy as np
p = sim.Population(6,
sim.SpikeSourcePoisson(rate=np.linspace(10.0, 20.0, num=6)))
print(p.get('rate'))
#using function to calculate
from numpy import sin, pi
p = sim.Population(8, sim.IF_cond_exp(i_offset=lambda i: sin(i * pi / 8)))
print(p.get('i_offset'))
#Setting parameters as a function of spatial position
from pyNN.space import Grid2D
grid = Grid2D(dx=10.0, dy=10.0)
p = sim.Population(16, sim.IF_cond_alpha(), structure=grid)
def f_v_thresh(pos):
x, y, z = pos.T
return -50 + 0.5 * x - 0.2 * y
p.set(v_thresh=lambda i: f_v_thresh(p.position_generator(i)))
print(p.get('v_thresh').reshape((4, 4)))
#multiple types
n = 1000
parameters = {
'tau_m': RandomDistribution('uniform', (10.0, 15.0)),
from NeuroTools.parameters import ParameterSet
import sys
from math import sqrt
from pyNN.space import Space, Grid2D
P = ParameterSet(sys.argv[1])
exec("import pyNN.%s as sim" % P.simulator)
sim.setup()
dx1 = dy1 = 500.0 / sqrt(P.n1)
dx2 = dy2 = 500.0 / sqrt(P.n2)
struct1 = Grid2D(dx=dx1, dy=dy1)
struct2 = Grid2D(dx=dx2, dy=dy2)
p1 = sim.Population(P.n1, sim.IF_cond_exp, structure=struct1)
p2 = sim.Population(P.n2, sim.IF_cond_exp, structure=struct2)
space = Space()
DDPC = sim.DistanceDependentProbabilityConnector
c = DDPC(P.d_expression, P.allow_self_connections, P.weights, P.delays, space,
P.safe)
prj = sim.Projection(p1, p2, c)
sys.stdout.write(p1.describe().encode('utf-8'))
sys.stdout.write(p2.describe().encode('utf-8'))
sys.stdout.write(prj.describe().encode('utf-8'))
sim.end()
sim = import_module("pyNN.%s" % args.simulator_name)
simtime = 100.0
input_rate = 20.0
n_cells = 9
sim.setup()
cell_type = sim.IF_cond_exp(tau_m=10.0,
# v_rest=lambda x, y, z: -60.0 - sqrt((x**2 + y**2)/100),
# v_thresh=lambda x, y, z: -55.0 + x/10.0)
v_rest=lambda i: -60.0 + i,
v_thresh=lambda i: -55.0 + i)
cells = sim.Population(n_cells, cell_type,
structure=Grid2D(dx=100.0, dy=100.0),
initial_values={'v': lambda i: -60.0 - i},
label="cells")
print "positions:"
print cells.positions
for name in ('tau_m', 'v_rest', 'v_thresh'):
print name, "=", cells.get(name)
number = int(2*simtime*input_rate/1000.0)
numpy.random.seed(26278342)
def generate_spike_times(i):
gen = lambda: Sequence(numpy.add.accumulate(numpy.random.exponential(1000.0/input_rate, size=number)))
if hasattr(i, "__len__"):
return [gen() for j in i]