{
'mode': 'Perlin',
'specs': {
'size': 4
}
},
{
'mode': 'Perlin_uniform',
'specs': {
'size': 4
}
},
]
simulation = 'sequence_EI_networks'
params = protocol.get_parameters(simulation).as_dict()
params.update({'landscape': landscapes[-1]})
gids, ts = protocol.get_or_simulate(simulation, params)
nrow = ncol = params['nrowE']
npop = nrow * ncol
offset = 1
idx = gids - offset < npop
gids, ts = gids[idx], ts[idx]
ts_bins = np.arange(500., 2500., 10.)
h = np.histogram2d(ts, gids - offset, bins=[ts_bins, range(npop + 1)])[0]
hh = h.reshape(-1, nrow, ncol)
{
'mode': 'Perlin_uniform',
'specs': {
'size': 4
}
},
{
'mode': 'homogeneous',
'specs': {
'phi': 3
}
},
]
simulation = 'sequence_I_networks'
params = protocol.get_parameters(simulation)
nrow, ncol = params['nrow'], params['ncol']
npop = nrow * ncol
ncon = params['ncon']
size = 8
ax = axes[1, 0]
ax.text(0.05,
0.95,
'Random',
verticalalignment='top',
horizontalalignment='left',
transform=ax.transAxes,
fontsize=8,
bbox={
