def simulator( model , N_timebins , candidate_subunits ):
stimulus = simulate_retina.Stimulus( simulate_retina.white_gaussian )
bigU = gaussian2D_weights( model['cones'], candidate_subunits ,
sigma=model['sigma_spatial'][0] )
return simulate_retina.run_LNLEP( model , stimulus = stimulus ,
N_timebins = N_timebins ,
average_me = {'features':lambda x:
model['nonlinearity'](np.dot(bigU,x))} )
def simulator( v2, N_filters, nonlinearity, N_cells , sigma_spatial , N_timebins ):
retina = simulate_retina.LNLNP_ring_model( nonlinearity = nonlinearity ,
N_cells = N_cells ,
sigma_spatial = sigma_spatial )
# stimulus = simulate_retina.white_gaussian_stimulus( dimension = N_cells[0] ,
# sigma = 1. )
stimulus = simulate_retina.Stimulus( simulate_retina.white_gaussian )
return simulate_retina.run_LNLEP( retina , stimulus = stimulus ,
N_timebins = N_timebins ,
average_me = {'features':lambda x: NL(np.dot(filters,x))} )
