def default_parameters():
# receptive field parameters
p = ParameterSpace({})
p.Ac = 1.
p.As = 1. / 3.
p.K1 = 1.05
p.K2 = 0.7
p.c1 = 0.14
p.c2 = 0.12
p.n1 = 7.
p.n2 = 8.
p.t1 = -6. # ms
p.t2 = -6. # ms
p.td = 6.0 # time differece between ON-OFF
p.sigma_c = 0.3 #0.4 # Allen 2006 # sigma of center gauss degree
p.sigma_s = 1.5 #p.sigma_c*1.5+0.4 # Allen 2006 # sigma of surround gauss degree
# Kernel dims
# temporal
p.size = 10. # degree
p.degree_per_pixel = 0.1133
# spatial
p.dt = 1.0 # ms
p.duration = 200. # ms
return p
def default_parameters():
# receptive field parameters
p = ParameterSpace({})
p.Ac = 1.
p.As = 1./3.
p.K1 = 1.05
p.K2 = 0.7
p.c1 = 0.14
p.c2 = 0.12
p.n1 = 7.
p.n2 = 8.
p.t1 = -6. # ms
p.t2 = -6. # ms
p.td = 6.0 # time differece between ON-OFF
p.sigma_c = 0.3#0.4 # Allen 2006 # sigma of center gauss degree
p.sigma_s = 1.5#p.sigma_c*1.5+0.4 # Allen 2006 # sigma of surround gauss degree
# Kernel dims
# temporal
p.size = 10. # degree
p.degree_per_pixel = 0.1133
# spatial
p.dt = 1.0 # ms
p.duration = 200. # ms
return p
"""
import numpy, pylab
import NeuroTools.stgen as stgen
sg = stgen.StGen()
from NeuroTools.parameters import ParameterSpace
from NeuroTools.parameters import ParameterRange
from NeuroTools.sandbox import make_name
# creating a ParameterSpace
p = ParameterSpace({})
# adding fixed parameters
p.nu = 20. # rate [Hz]
p.duration = 1000.
# adding ParameterRanges
p.c = ParameterRange([0.0, 0.01, 0.1, 0.5])
p.jitter = ParameterRange([
0.0,
1.0,
5.0,
])
# calculation of the ParameterSpace dimension and the labels of the parameters
# containing a range
dims, labels = p.parameter_space_dimension_labels()
print "dimensions: ", dims
print ' labels: ', labels
"""
import numpy, pylab
import NeuroTools.stgen as stgen
sg = stgen.StGen()
from NeuroTools.parameters import ParameterSpace
from NeuroTools.parameters import ParameterRange
from NeuroTools.sandbox import make_name
# creating a ParameterSpace
p = ParameterSpace({})
# adding fixed parameters
p.nu = 20. # rate [Hz]
p.duration = 1000.
# adding ParameterRanges
p.c = ParameterRange([0.0,0.01,0.1,0.5])
p.jitter = ParameterRange([0.0,1.0,5.0,])
# calculation of the ParameterSpace dimension and the labels of the parameters
# containing a range
dims, labels = p.parameter_space_dimension_labels()
print "dimensions: ", dims
print ' labels: ', labels
def calc_cc(p):
"""
Generate correlated spike trains from the ParameterSet.