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The kernel least mean squares (KLMS) algorithm is a computationally efficient nonlinear adaptive filtering method that “kernelizes” the celebrated (linear) least mean squares algorithm. We demonstrate that the least mean squares algorithm is closely related to the Kalman filtering, and thus, the KLMS can be interpreted as an approximate Bayesian filtering method. This allows us to systematically develop...
This paper proposes a nonlinear adaptive decoder for somatosensory micro-stimulation based on the kernel least mean square (KLMS) algorithm applied directly on the space of spike trains. Instead of using a binned representation of spike trains, we transform the vector of spike times into a function in reproducing kernel Hilbert space (RKHS), where the inner product of two spike time vectors is defined...
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