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.