An adaptive decoder from spike trains to micro-stimulation using kernel least-mean-squares (KLMS)

Lin Li, Il Memming Park, Sohan Seth, John S. Choi, Joseph T. Francis, Justin C. Sanchez, José C. Príncipe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

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 by a nonlinear cross intensity kernel. This representation encapsulates the statistical description of the point process that generates the spike trains, and bypasses the curse of dimensionality- resolution of the binned spike representations. We compare our method with two other methods based on binned data: GLM and KLMS, in reconstructing biphasic micro-stimulation. The results indicate that the KLMS based on RKHS for spike train is able to detect the timing, the shape and the amplitude of the biphasic stimulation with the best accuracy.

Original languageEnglish
Title of host publicationIEEE International Workshop on Machine Learning for Signal Processing
DOIs
StatePublished - Dec 5 2011
Event21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011 - Beijing, China
Duration: Sep 18 2011Sep 21 2011

Other

Other21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011
CountryChina
CityBeijing
Period9/18/119/21/11

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Keywords

  • Adaptive Neural decoder
  • KLMS
  • microstimulation
  • spike train

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

Cite this

Li, L., Park, I. M., Seth, S., Choi, J. S., Francis, J. T., Sanchez, J. C., & Príncipe, J. C. (2011). An adaptive decoder from spike trains to micro-stimulation using kernel least-mean-squares (KLMS). In IEEE International Workshop on Machine Learning for Signal Processing [6064603] https://doi.org/10.1109/MLSP.2011.6064603