Non-Parametric Modeling

LKER
Computes 1st and 2nd order Volterra kernels from stimulus-response data using the Laguerre expansion technique.

PKER
Computes 1st and 2nd order Volterra kernels from point-process input data using the Laguerre expansion technique.

CKER
Computes 1st and 2nd order Wiener kernels from (white noise) stimulus and response data, using cross-correlation technique.

MODRES
Computes the model response for a given input, based on given 1st and/or 2nd order Volterra kernels.

LKER3
Computes 1st, 2nd, and 3rd order Volterra kernels from stimulus-response data using the Laguerre expansion technique.

LMOD3
Computes the model response for a given input, based on 1st, 2nd, and 3rd order Laguerre expansion coefficients.

CKER3
Computes 2-D “slices” of the 3rd order Wiener kernel from (white noise) stimulus-response data, using cross-correlation.

CKER2I
Computes 2nd order. Wiener cross-kernel from two independent (white noise) stimuli and response data, using cross-correlation.

MOD2I
Computes the model response for two given inputs, based on a given 2nd order cross-kernel.