A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control

Figure 1

Schematic representation of the construction of the RKHS defined by the tensor-product kernel from the individual spike and LFP kernel spaces, along with the mapping from the original data. Specifically, 𝐬_𝑖 denotes a window of multiunit spike trains; 𝐱_𝑛 denotes a window of multichannel LFPs; 𝜅_𝑠(⋅,⋅) denotes the spike train kernel with implicit mapping function 𝜙(⋅); and 𝜅_𝑥(⋅,⋅) denotes the LFP kernel with implicit mapping function 𝜑(⋅).