# compute electrode positions on the outer radius for different angular offsets
_theta = np.linspace(-np.pi / 4, np.pi / 4, 9)
_x = 90000. * np.sin(_theta)
_y = np.zeros(_theta.size)
_z = 90000. * np.cos(_theta)
PSET.foursphereParams = {
'radii': [79000., 80000., 85000., 90000.], # shell radii
'sigmas': [0.3, 1.5, 0.015, 0.3], # shell conductivity
'r': np.c_[_x, _y, _z], # contact coordinates
}
# Optional arguments to Network.simulate() for computing extracellular potential
# contribution from passive leak, membrane capactitance and synaptic currents
PSET.NetworkSimulateArgs = {
'use_ipas': False,
'use_icap': False,
'use_isyn': False,
}
# layer thickness top to bottom L1-L6, Markram et al. 2015 Fig 3A.
PSET.layer_data = np.array([('L1', 165., -82.5), ('L2', 149., -239.5),
('L3', 353., -490.5), ('L4', 190., -762.),
('L5', 525, -1119.5), ('L6', 700, -1732.)],
dtype=[('layer', '|{}2'.format(stringType)),
('thickness', float), ('center', float)])
# Define electrode geometry corresponding to an ECoG electrode, where contact
# points have a radius r, surface normal vectors N, and ECoG is calculated as the
# average LFP in n random points on each contact:
PSET.ecogParameters = {
'sigma_S': 0., # CSF conductivity
_theta = np.linspace(-np.pi/4, np.pi / 4, 9)
_x = 90000.*np.sin(_theta)
_y = np.zeros(_theta.size)
_z = 90000.*np.cos(_theta)
PSET.foursphereParams = {
'radii' : [79000., 80000., 85000., 90000.], # shell radii
'sigmas' : [0.3, 1.5, 0.015, 0.3], # shell conductivity
'r' : np.c_[_x, _y, _z], # contact coordinates
}
# Optional arguments to Network.simulate() for computing extracellular potential
# contribution from passive leak, membrane capactitance and synaptic currents
PSET.NetworkSimulateArgs = {
'use_ipas' : False,
'use_icap' : False,
'use_isyn' : False,
}
# layer thickness top to bottom L1-L6, Markram et al. 2015 Fig 3A.
PSET.layer_data = np.array([('L1', 165., -82.5),
('L2', 149., -239.5),
('L3', 353., -490.5),
('L4', 190., -762.),
('L5', 525, -1119.5),
('L6', 700, -1732.)],
dtype=[('layer', '|{}2'.format(stringType)), ('thickness', float), ('center', float)])
# Define electrode geometry corresponding to an ECoG electrode, where contact
# compute electrode positions on the outer radius for different angular offsets
_theta = np.linspace(-np.pi / 4, np.pi / 4, 9)
_x = 90000. * np.sin(_theta)
_y = np.zeros(_theta.size)
_z = 90000. * np.cos(_theta)
PSET.foursphereParams = {
'radii': [79000., 80000., 85000., 90000.], # shell radii
'sigmas': [0.3, 1.5, 0.015, 0.3], # shell conductivity
'r_electrodes': np.c_[_x, _y, _z], # contact coordinates
}
# Optional arguments to Network.simulate() for computing extracellular
# contribution from passive leak, membrane capactitance and synaptic currents
PSET.NetworkSimulateArgs = {
'use_ipas': False,
'use_icap': False,
'use_isyn': False,
'to_memory': True,
}
# layer thickness top to bottom L1-L6, Markram et al. 2015 Fig 3A.
PSET.layer_data = np.array([('L1', 165., -82.5), ('L2', 149., -239.5),
('L3', 353., -490.5), ('L4', 190., -762.),
('L5', 525, -1119.5), ('L6', 700, -1732.)],
dtype=[('layer', '|{}2'.format(stringType)),
('thickness', float), ('center', float)])
# Define electrode geometry corresponding to an ECoG electrode, where contact
# points have a radius r, surface normal vectors N, and ECoG is calculated as
# the average LFP in n random points on each contact:
PSET.ecogParameters = {
'sigma_S': 0., # CSF conductivity
