示例#1
0
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))} )
示例#2
0
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))} )