Identifying high-dimensional subspaces | The nonlinearity f is parametrized using basis functions {(p,(-)}, i = 1, . |
Identifying high-dimensional subspaces | If we fix 9 and the basis functions {(p,} in advance, fitting the nonlinearity simply involves estimating the parameters a,- from the projected stimuli and associated spike counts. |
Identifying high-dimensional subspaces | Standard MID can be seen as a special case of this general framework: it sets gto the identity function and the basis functions (p,- to histogram-bin indicator functions (denoted lBi(-) in Equation 7). |