# 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