Kernel temporal differences for neural decoding

Jihye Bae, Luis G. Sanchez Giraldo, Eric A. Pohlmeyer, Joseph T. Francis, Justin C. Sanchez, José C. Príncipe

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces.

Original languageEnglish
Article number481375
JournalComputational Intelligence and Neuroscience
StatePublished - 2015

Bibliographical note

Publisher Copyright:
© 2015 Jihye Bae et al.

ASJC Scopus subject areas

  • General Computer Science
  • General Neuroscience
  • General Mathematics


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