def make_eE_noiseCov(ssn, noise_pars, LFPrange):
# setting up e_E and e_I: the projection/measurement vectors for
# representing the "LFP" measurement (e_E good for LFP interpretation, but e_I ?)
# eE = np.zeros(ssn.N)
# # eE[LFPrange] =1/len(LFPrange)
# index_update(eE, LFPrange, 1/len(LFPrange))
# eI = np.zeros(ssn.N)
# eI[ssn.Ne + LFPrange] =1/len(LFPrange)
eE = np.hstack((np.array([i in LFPrange for i in range(ssn.Ne)],
dtype=np.float32), np.zeros(ssn.Ni)))
# the script assumes independent noise to E and I, and spatially uniform magnitude of noise
noiseCov = np.hstack((noise_pars.stdevE**2 * np.ones(ssn.Ne),
noise_pars.stdevI**2 * np.ones(ssn.Ni)))
OriVec = ssn.topos_vec
if noise_pars.corr_length > 0 and OriVec.size > 1: #assumes one E and one I at every topos
dOri = np.abs(OriVec)
L = OriVec.size * np.diff(OriVec[:2])
dOri[dOri > L /
2] = L - dOri[dOri > L / 2] # distance on circle/periodic B.C.
SpatialFilt = toeplitz(
np.exp(-(dOri**2) / (2 * noise_pars.corr_length**2)) /
np.sqrt(2 * pi) / noise_pars.corr_length * L / ssn.Ne)
sigTau1Sprd1 = 0.394 # roughly the std of spatially and temporally filtered noise when the white seed is randn(ssn.Nthetas,Nt)/sqrt(dt) and corr_time=corr_length = 1 (ms or angle, respectively)
SpatialFilt = SpatialFilt * np.sqrt(
noise_pars.corr_length /
2) / sigTau1Sprd1 # for the sake of output
SpatialFilt = np.kron(np.eye(2), SpatialFilt) # 2 for E/I
else:
SpatialFilt = np.array(1)
return eE, noiseCov, SpatialFilt # , eI
def __init__(self,
n,
k,
tauE,
tauI,
J_2x2,
s_conn,
Ne,
L=180,
**kwargs): #, Ni=None,
#Ni = Ni if Ni is not None else Ne
Ni = Ne
Ns = [Ne, Ni]
tau_vec = np.hstack([tauE * np.ones(Ne), tauI * np.ones(Ni)])
distsq = lambda x: np.minimum(np.abs(x), L - np.abs(x))**2
from util import toeplitz
# s_2x2 = np.array(s_conn) if not np.isscalar(s_conn) else s_conn * np.ones((2,2))
# assert s_2x2.shape == (2,2)
# blk = lambda i, j: toeplitz(np.exp(-distsq(topos_vec[i,j])/2/s_2x2[i,j]**2))
topos_vec = np.linspace(0, L, Ne + 1)[:-1]
blk = toeplitz(np.exp(-distsq(topos_vec) / 2 / s_conn**2))
W = np.vstack([
# np.hstack([J_2x2[i,j]/Ns[i] * blk(i,j) for j in range(2)])
np.hstack([J_2x2[i, j] / Ns[i] * blk for j in range(2)])
for i in range(2)
])
super(SSNHomogRing, self).__init__(n=n,
k=k,
Ne=Ne,
Ni=Ni,
tau_vec=tau_vec,
W=W,
**kwargs)